Podcasts about connectomics

Play Episode Listen Later Jun 26, 2023

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.06.23.546329v1?rss=1 Authors: Luppi, A. I., Gellersen, H. M., Liu, Z.-Q. I., Peattie, A. R. D., Manktelow, A. E., Adapa, R., Owen, A. M., Naci, L., Menon, D., Dimitriadis, S. I., Stamatakis, E. A. Abstract: Functional interactions between brain regions can be viewed as a network, empowering neuroscientists to leverage network science to investigate distributed brain function. However, obtaining a brain network from functional neuroimaging data involves multiple steps of data manipulation, which can drastically affect the organisation and validity of the estimated brain network and its properties. Here, we provide a systematic evaluation of 576 unique data-processing pipelines for functional connectomics from resting-state functional MRI, obtained from all possible recombinations of popular choices for brain atlas type and size, connectivity definition and selection, and global signal regression. We use the portrait divergence, an information-theoretic measure of differences in network topology across scales, to quantify the influence of analytic choices on the overall organisation of the derived functional connectome. We evaluate each pipeline across an entire battery of criteria, seeking pipelines that (i) minimise spurious test-retest discrepancies of network topology, while simultaneously (ii) mitigating motion confounds, and being sensitive to both (iii) inter-subject differences and (iv) experimental effects of interest, as demonstrated by propofol-induced general anaesthesia. Our findings reveal vast and systematic variability across pipelines' suitability for functional connectomics. Choice of the wrong data-processing pipeline can lead to results that are not only misleading, but systematically so, distorting the functional connectome more drastically than the passage of several months. We also found that the majority of pipelines failed to meet at least one of our criteria. However, we identified 8 candidates satisfying all criteria across each of four independent datasets spanning minutes, weeks, and months, ensuring the generalisability of our recommendations. Our results also generalise to alternative acquisition parameters and preprocessing and denoising choices. By providing the community with a full breakdown of each pipeline's performance across this multi-dataset, multi-criteria, multi-scale and multi-step approach, we establish a comprehensive set of benchmarks to inform future best practices in functional connectomics. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

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Connectomics #9 - Andrea Hiott on Cognition as Navigation

The Nonlinear Library: LessWrong

Play Episode Listen Later May 10, 2023 110:07

In episode #9 of the Connectomics podcast, Mark sits down with neuroscientist and philosopher Andrea Hiott. In this episode they talk about Andrea's work in providing a new metaphor for understanding cognition: cognition as navigation or way-making. As well as exploring what exactly is meant by the claim that cognition is way-making, they explore how this intersects with contemporary views within philosophy and science, what some of its practical implications are, and indeed, how Andrea made her way to these ideas in the first place. As you will hear, the path was long and winding, and no doubt played a substantial role in making Andrea the fascinating and lively conversation partner she is today. This episode was edited and produced by Shane Byrne and in association with the Okinawa Institute of Science and Technology Graduate University. Cover art is provided by Cian Brennan.

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AF - Connectomics seems great from an AI x-risk perspective by Steve Byrnes

The Nonlinear Library

Play Episode Listen Later Apr 30, 2023 17:14

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Connectomics seems great from an AI x-risk perspective, published by Steve Byrnes on April 30, 2023 on The AI Alignment Forum. Context Numerous people are in a position to accelerate certain areas within science or technology, whether by directing funds and resources, or by working in the area directly. But which areas are best to accelerate? One possible consideration (among others) is the question: “Is accelerating this technology going to increase the chance that our future transition to superhuman artificial general intelligence (AGI) goes well? Or decrease it? Or make no difference?” My goal here is to try to answer that question for connectomics (the science & technology of mapping how neurons connect to each other in a brain). This blog post is an attempt to contribute to Differential Technology Development (DTD) (part of the broader field of Differential Intellectual Progress). Successful DTD involves trying to predict complicated and deeply uncertain future trajectories and scenarios. I think the best we can hope for is to do better than chance. But I'm optimistic that we can at least exceed that low bar. My qualifications: I'm probably as qualified as anyone to discuss AI x-risk and how it relates to neuroscience. As for connectomics, I'm not too familiar with the techniques, but I'm quite familiar with how the results are used—in the past few years I have scrutinized probably hundreds of journal articles describing neural tracer measurements. (Think of neural tracer measurements as the traditional, “artisanal”, small-scale version of connectomics.) I find such articles extremely useful; I would happily trade away 20 fMRI papers for one neural tracer paper. This post is very much “my opinions” as opposed to consensus, and I'm happy for further discussion. TL;DR Improved connectomics technology seems like it would be very helpful for the project of reverse-engineering circuitry in the hypothalamus and brainstem that implement the “innate drives” upstream of human motivations and morality. And that's a good thing! We may wind up in a situation where future researchers face the problem of designing “innate drives” for an AI; knowing how they work in humans would be helpful for various reasons. Improved connectomics technology seems like it would NOT be very helpful for the project of reverse-engineering the learning algorithms implemented by various parts of the brain, particularly the neocortex. And that's a good thing too! I think that this reverse-engineering effort would lead directly to knowledge of how to build superhuman AGI, whereas I would like us to collectively make much more progress on AGI safety & alignment first, and to learn exactly how to build AGI second. Improved connectomics technology might open up a path to achieving Whole Brain Emulation (WBE) earlier than non-WBE AGI. And that's a good thing too! Generally, a WBE-first future seems difficult to pull off, because (I claim) as soon as we understand the brain well enough for WBE, then we already understand the brain well enough to make non-WBE AGI, and someone will probably do that first. But if we could pull it off, it would potentially be very useful for a safe transition to AGI. I have previously been very skeptical that WBE is a possibility at all, but when I imagine a scenario where radically improved human connectomics technology is available in the near future, then it does actually seem like a possibility to have WBE come before non-WBE AGI, at least by a year or two, given enough effort and luck. 1. Background considerations 1.1 The race between reverse-engineering the cortex versus reverse-engineering the hypothalamus & brainstem My theory is that parts of the brain (esp. cortex, thalamus, striatum, and cerebellum) are running large-scale learning algorithms, while other parts of...

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LW - Connectomics seems great from an AI x-risk perspective by Steven Byrnes

The Nonlinear Library

Play Episode Listen Later Apr 30, 2023 17:13

Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Connectomics seems great from an AI x-risk perspective, published by Steven Byrnes on April 30, 2023 on LessWrong. Context Numerous people are in a position to accelerate certain areas within science or technology, whether by directing funds and resources, or by working in the area directly. But which areas are best to accelerate? One possible consideration (among others) is the question: “Is accelerating this technology going to increase the chance that our future transition to superhuman artificial general intelligence (AGI) goes well? Or decrease it? Or make no difference?” My goal here is to try to answer that question for connectomics (the science & technology of mapping how neurons connect to each other in a brain). This blog post is an attempt to contribute to Differential Technology Development (DTD) (part of the broader field of Differential Intellectual Progress). Successful DTD involves trying to predict complicated and deeply uncertain future trajectories and scenarios. I think the best we can hope for is to do better than chance. But I'm optimistic that we can at least exceed that low bar. My qualifications: I'm probably as qualified as anyone to discuss AI x-risk and how it relates to neuroscience. As for connectomics, I'm not too familiar with the techniques, but I'm quite familiar with how the results are used—in the past few years I have scrutinized probably hundreds of journal articles describing neural tracer measurements. (Think of neural tracer measurements as the traditional, “artisanal”, small-scale version of connectomics.) I find such articles extremely useful; I would happily trade away 20 fMRI papers for one neural tracer paper. This post is very much “my opinions” as opposed to consensus, and I'm happy for further discussion. TL;DR Improved connectomics technology seems like it would be very helpful for the project of reverse-engineering circuitry in the hypothalamus and brainstem that implement the “innate drives” upstream of human motivations and morality. And that's a good thing! We may wind up in a situation where future researchers face the problem of designing “innate drives” for an AI; knowing how they work in humans would be helpful for various reasons. Improved connectomics technology seems like it would NOT be very helpful for the project of reverse-engineering the learning algorithms implemented by various parts of the brain, particularly the neocortex. And that's a good thing too! I think that this reverse-engineering effort would lead directly to knowledge of how to build superhuman AGI, whereas I would like us to collectively make much more progress on AGI safety & alignment first, and to learn exactly how to build AGI second. Improved connectomics technology might open up a path to achieving Whole Brain Emulation (WBE) earlier than non-WBE AGI. And that's a good thing too! Generally, a WBE-first future seems difficult to pull off, because (I claim) as soon as we understand the brain well enough for WBE, then we already understand the brain well enough to make non-WBE AGI, and someone will probably do that first. But if we could pull it off, it would potentially be very useful for a safe transition to AGI. I have previously been very skeptical that WBE is a possibility at all, but when I imagine a scenario where radically improved human connectomics technology is available in the near future, then it does actually seem like a possibility to have WBE come before non-WBE AGI, at least by a year or two, given enough effort and luck. 1. Background considerations 1.1 The race between reverse-engineering the cortex versus reverse-engineering the hypothalamus & brainstem My theory is that parts of the brain (esp. cortex, thalamus, striatum, and cerebellum) are running large-scale learning algorithms, while other parts of the brain (...

ai risk speech ea improved generally tl agi fmri byrnes rationalist wbe lesswrong connectomics

LW - Connectomics seems great from an AI x-risk perspective by Steven Byrnes

Play Episode Listen Later Apr 30, 2023 17:13

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Connectomics seems great from an AI x-risk perspective, published by Steven Byrnes on April 30, 2023 on LessWrong. Context Numerous people are in a position to accelerate certain areas within science or technology, whether by directing funds and resources, or by working in the area directly. But which areas are best to accelerate? One possible consideration (among others) is the question: “Is accelerating this technology going to increase the chance that our future transition to superhuman artificial general intelligence (AGI) goes well? Or decrease it? Or make no difference?” My goal here is to try to answer that question for connectomics (the science & technology of mapping how neurons connect to each other in a brain). This blog post is an attempt to contribute to Differential Technology Development (DTD) (part of the broader field of Differential Intellectual Progress). Successful DTD involves trying to predict complicated and deeply uncertain future trajectories and scenarios. I think the best we can hope for is to do better than chance. But I'm optimistic that we can at least exceed that low bar. My qualifications: I'm probably as qualified as anyone to discuss AI x-risk and how it relates to neuroscience. As for connectomics, I'm not too familiar with the techniques, but I'm quite familiar with how the results are used—in the past few years I have scrutinized probably hundreds of journal articles describing neural tracer measurements. (Think of neural tracer measurements as the traditional, “artisanal”, small-scale version of connectomics.) I find such articles extremely useful; I would happily trade away 20 fMRI papers for one neural tracer paper. This post is very much “my opinions” as opposed to consensus, and I'm happy for further discussion. TL;DR Improved connectomics technology seems like it would be very helpful for the project of reverse-engineering circuitry in the hypothalamus and brainstem that implement the “innate drives” upstream of human motivations and morality. And that's a good thing! We may wind up in a situation where future researchers face the problem of designing “innate drives” for an AI; knowing how they work in humans would be helpful for various reasons. Improved connectomics technology seems like it would NOT be very helpful for the project of reverse-engineering the learning algorithms implemented by various parts of the brain, particularly the neocortex. And that's a good thing too! I think that this reverse-engineering effort would lead directly to knowledge of how to build superhuman AGI, whereas I would like us to collectively make much more progress on AGI safety & alignment first, and to learn exactly how to build AGI second. Improved connectomics technology might open up a path to achieving Whole Brain Emulation (WBE) earlier than non-WBE AGI. And that's a good thing too! Generally, a WBE-first future seems difficult to pull off, because (I claim) as soon as we understand the brain well enough for WBE, then we already understand the brain well enough to make non-WBE AGI, and someone will probably do that first. But if we could pull it off, it would potentially be very useful for a safe transition to AGI. I have previously been very skeptical that WBE is a possibility at all, but when I imagine a scenario where radically improved human connectomics technology is available in the near future, then it does actually seem like a possibility to have WBE come before non-WBE AGI, at least by a year or two, given enough effort and luck. 1. Background considerations 1.1 The race between reverse-engineering the cortex versus reverse-engineering the hypothalamus & brainstem My theory is that parts of the brain (esp. cortex, thalamus, striatum, and cerebellum) are running large-scale learning algorithms, while other parts of the brain (...

ai risk speech ea improved generally tl agi fmri byrnes rationalist wbe lesswrong connectomics

Mind of a dauer: Comparative connectomics reveals developmental plasticity

Play Episode Listen Later Mar 23, 2023

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.03.23.533915v1?rss=1 Authors: Yim, H., Choe, D. T., Bae, J. A., Kang, H.-M., Nguyen, K. C. Q., Choi, M.-k., Ahn, S., Bahn, S.-k., Yang, H., Hall, D. H., Kim, J. S., Lee, J. Abstract: A fundamental question in neurodevelopmental biology is how flexibly the nervous system can change during development. To address this question of developmental plasticity, we analyzed the connectome of dauer, an alternative developmental stage of nematodes with physiological and behavioral characteristics remarkably distinct from other developmental stages. We reconstructed the complete chemical connectome of a dauer by manual volumetric reconstruction and automated synapse detection using deep learning. While the basic architecture of the nervous system was preserved, there were also structural changes in neurons, large or small, that were closely associated with changes in the connectivity, some of which in turn evoked dauer-specific behaviors such as nictation. Combining the connectome data and optogenetic experiments were enough to reveal dauer-specific neural connections for the dauer-specific behavior. Graph theoretical analyses showed higher clustering of motor neurons and more feedback connections from motor to sensory neurons in the dauer connectome, suggesting that the dauer connectome allows a quick response to an ever-changing environment. We suggest that the nervous system in the nematode, which can be extended to animals in general, has evolved to obtain the ability to respond to harsh environments by reversibly developing a connectome quantitatively and qualitatively differentiated from other developmental stages. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

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Your Future Self Will Thank You | Connectomics Part II

Neurotech Pub

Play Episode Listen Later Mar 22, 2023 60:57

We're back with Part II of our two-part series on Connectomics! In part one we speculated on the legal and ethical implications of emerging technologies in the connectomics field. In part two, we don our lab coats and take a deep dive into the latest research tools, from fixation protocols for the preservation of neural tissue, to multimodal imaging techniques, to the machine intelligence designed to interpret massive data sets and reconstruct the vast neural circuits that make up the connectome. Our guests are: Kenneth Hayworth, PhD, President and Co-Founder of the Brain Preservation Foundation, Senior Scientist at the Howard Hughes Medical Institute's Janelia Farm Research Campus (JFRC) Robert McIntyre, CEO at Nectome Jeremy Maitin-Shepard, PhD, Software Engineer–Connectomics at Google In this episode, Ken and Robert from part one return to the pub, and we are also joined by Jeremy Maitin-Shepard, an engineer and researcher at Google, who shares insights into some of the machine intelligence modalities being used to decode previously uncharted neural networks. Check out Jeremy's recent paper on BioRxiv, as well as his published work at Google. If you missed part one, you can listen and explore the show notes here. Cheers!Show Notes: 0:00 | Intro1:03 | Kenneth Hayworth, PhD1:12 | Robert McKintyre, CEO, Nectome1:17 | Jeremy Maitin-Shepard, PhD1:51 | Setting the record straight 3:09 | The nucleotide sequence of bacteriophage φX1744:22 | Frozen Zoo at San Diego Zoo12:01| Glutaraldehyde and reduction techniques for immunolabeling 17:39 | SWITCH Framework19:14 | Population Responses in V1 Encode Different Figures by Response Amplitude Enhanced mirror neuron network activity and effective connectivity during live interaction among female subjects Permeabilization-free en bloc immunohistochemistry for correlative microscopy 19:57 | Synaptic Signaling in Learning and Memory Structure and function of a neocortical synapse Engineering a memory with LTD and LTP Synapse-specific representation of the identity of overlapping memory engrams 20:28 | Ultrastructure of Dendritic SpinesStructure–stability–function relationships of dendritic spines 24:25 | Reconstructing the connectome 24:32 | Connectomics Research Team at Google 24:55 | Google x HHMI: Releasing the Drosophila Hemibrain Connectome 28:38 | Serial Block-Face Scanning Electron Microscopy 29:22 | Automated Serial Sections to Tape29:45 | Mapping connections in mouse neocortex30:59 | A connectome and analysis of the adult Drosophila central brain 32:14 | Expansion Microscopy34:37 | The future of connectomics 45:13 | Contribution of apical and basal dendrites to orientation encoding in mouse V1 L2/3 pyramidal neurons49:49 | Mice and rats achieve similar levels of performance in an adaptive decision-making task Want More?Follow Neurotech Pub on TwitterFollow Paradromics on Twitter, LinkedIn, and InstagramFollow Matt on LinkedIn and Twitter

We're in Heated Agreement Over Here | Connectomics Part I

Neurotech Pub

Play Episode Listen Later Mar 14, 2023 49:06

Welcome back to Neurotech Pub! In this first installment of two episodes on Connectomics, host and Paradromics CEO Matt Angle kicks off a lively discussion on the rapidly accelerating research in the mapping, preservation, and reconstruction of the human connectome. We explore the ethical and legal ramifications of disruptive technology, and some of the unique challenges faced when driving innovation in emerging industries. Our guests are: Nita Faraheny, JD, PhD, Everett Distinguished Professor of Law & Philosophy at Duke Law School, the Founding Director of Duke Science & Society, the Faculty Chair of the Duke MA in Bioethics & Science Policy, and principal investigator of SLAP Lab. Kenneth Hayworth, PhD, President and Co-Founder of the Brain Preservation Foundation, Senior Scientist at the Howard Hughes Medical Institute's Janelia Farm Research Campus (JFRC) Robert McIntyre, CEO at Nectome As an exciting new development since the recording of this episode, Nita recently published a book, The Battle for Your Brain, which examines many topics in neuroethics, from Connectomics to Brain-Computer Interfaces. It is currently available on Amazon.Keep an eye out for part two in this series, which will take a deep dive into the latest technical and engineering innovations in the connectomics ecosystem. Coming soon!Please be advised that this episode contains a brief discussion of assisted suicide in a medical setting.Show Notes: 0:00 | Episode Intro 1:16 | Nita A. Farahany, JD, PhD1:21 | Kenneth Hayworth, PhD1:27 | Robert McKintyre, CEO, Nectome1:56 | Meeting of the minds 2:53 | Aldehyde-stabilized cryopreservation wins final phase of brain preservation prize3:56 | The Brain Preservation Foundation4:09 | Documentary series on the Brain Preservation Foundation5:21 | Letter of Support for Aldehyde Stabilized Cryopreservation (and ‘next steps' caveats)5:51 | Nita's 2018 Neuroethics Ted Talk 5:54 | International Neuroethics Society6:25 | Connectomics & new paths in neuroscience 8:10 | Allen Institute for Brain Science8:47 | A connectome and analysis of the adult Drosophila central brain9:33 | A visual intro to synaptic imaging in connectomics10:28 | The structure of the nervous system of the nematode Caenorhabditis elegans 11:16 | Mouse Connectome Project at CIC14:59 | Cryonics controversy 19:00 | Death, taxes, and synapses 20:51 | Uniform Law Commission21:08 | The Uniform Determination of Death Act24:25 | Watch Altered Carbon on Netflix25:49 | Understanding the “Loss of Chance” Doctrine 37:13 | Understanding Physician-Assisted Death, or ‘Death with Dignity' 40:21 | Euthanasia in the Netherlands46:01 | Autonomy, Dignity, and Consent to Harm, Rutgers Law Review Want More?Follow Neurotech Pub on TwitterFollow Paradromics on Twitter, LinkedIn, and InstagramFollow Matt on LinkedIn and Twitter

Functional connectomics reveals general wiring rule in mouse visual cortex

Play Episode Listen Later Mar 14, 2023

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.03.13.531369v1?rss=1 Authors: Ding, Z., Fahey, P. G., Papadopoulos, S., Wang, E., Celii, B., Papadopoulos, C., Kunin, A., Chang, A., Fu, J., Ding, Z., Patel, S., Ponder, K., Bae, J. A., Bodor, A. L., Brittain, D., Buchanan, J., Bumbarger, D. J., Castro, M. A., Cobos, E., Dorkenwald, S., Elabbady, L., Halageri, A., Jia, Z., Jordan, C., Kapner, D., Kemnitz, N., Kinn, S., Lee, K., Li, K., Lu, R., Macrina, T., Mahalingam, G., Mitchell, E., Mondal, S. S., Mu, S., Nehoran, B., Popovych, S., Schneider-Mizell, C. M., Silversmith, W., Takeno, M., Torres, R., Turner, N. L., Wong, W., Wu, J., Yin, W., Yu, S.-c., Froudarakis, E., Sinz Abstract: To understand how the neocortex underlies our ability to perceive, think, and act, it is important to study the relationship between circuit connectivity and function. Previous research has shown that excitatory neurons in layer 2/3 of the primary visual cortex of mice with similar response properties are more likely to form connections. However, technical challenges of combining synaptic connectivity and functional measurements have limited these studies to few, highly local connections. Utilizing the millimeter scale and nanometer resolution of the MICrONS dataset, we studied the connectivity-function relationship in excitatory neurons of the mouse visual cortex across interlaminar and interarea projections, assessing connection selectivity at the coarse axon trajectory and fine synaptic formation levels. A digital twin model of this mouse, that accurately predicted responses to arbitrary video stimuli, enabled a comprehensive characterization of the function of neurons. We found that neurons with highly correlated responses to natural videos tended to be connected with each other, not only within the same cortical area but also across multiple layers and visual areas, including feedforward and feedback connections, whereas we did not find that orientation preference predicted connectivity. The digital twin model separated each neuron's tuning into a feature component (what the neuron responds to) and a spatial component (where the neuron's receptive field is located). We show that the feature, but not the spatial component, predicted which neurons were connected at the fine synaptic scale. Together, our results demonstrate the "like-to-like" connectivity rule generalizes to multiple connection types, and the rich MICrONS dataset is suitable to further refine a mechanistic understanding of circuit structure and function. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Connectonomics #8 - Dr. Abeba Birhane on the limits and liabilities of artificial intelligence

Play Episode Listen Later Feb 8, 2023 77:16

In episode #8 of the Connectomics podcast, Mark sits down with cognitive scientist and ethical AI researcher, Dr. Abeba Birhane. In this episode, they talk about Abeba's work auditing large image datasets, how they have discovered racial biases in these data sets, and what the implications of that are. They also talk about Abeba's work in embodied cognitive science, and how the understanding of the person that emerges from that work challenges some of the assumptions underlying the value of the 'predictive' algorithms that are being used more and more in, for instance, crime prevention. Abeba provides a great overview of the state of AI research at the moment, some of the challenges it faces, and some of the things we can do to ensure we are developing technologies that serve everyone and not just certain privileged groups. This episode was edited and produced by Shane Byrne and in association with the Okinawa Institute of Science and Technology Graduate University. Cover art is provided by Cian Brennan.

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Simulation-based inference for efficient identification of generative models in connectomics

Play Episode Listen Later Feb 3, 2023

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.01.31.526269v1?rss=1 Authors: Boelts, J., Harth, P., Gao, R., Udvary, D., Yanez, F., Baum, D., Hege, H.-C., Oberlaender, M., Macke, J. H. Abstract: Recent advances in connectomics research enable the acquisition of increasing amounts of data about the connectivity patterns of neurons. How can we use this wealth of data to efficiently derive and test hypotheses about the principles underlying these patterns? A common approach is to simulate neural networks using a hypothesized wiring rule in a generative model and to compare the resulting synthetic data with empirical data. However, most wiring rules have at least some free parameters and identifying parameters that reproduce empirical data can be challenging as it often requires manual parameter tuning. Here, we propose to use simulation-based Bayesian inference (SBI) to address this challenge. Rather than optimizing a single rule to fit the empirical data, SBI considers many parametrizations of a wiring rule and performs Bayesian inference to identify the parameters that are compatible with the data. It uses simulated data from multiple candidate wiring rules and relies on machine learning methods to estimate a probability distribution (the `posterior distribution over rule parameters conditioned on the data') that characterizes all data-compatible rules. We demonstrate how to apply SBI in connectomics by inferring the parameters of wiring rules in an in silico model of the rat barrel cortex, given in vivo connectivity measurements. SBI identifies a wide range of wiring rule parameters that reproduce the measurements. We show how access to the posterior distribution over all data-compatible parameters allows us to analyze their relationship, revealing biologically plausible parameter interactions and enabling experimentally testable predictions. We further show how SBI can be applied to wiring rules at different spatial scales to quantitatively rule out invalid wiring hypotheses. Our approach is applicable to a wide range of generative models used in connectomics, providing a quantitative and efficient way to constrain model parameters with empirical connectivity data. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

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Choice of parcellation atlas might not be too critical for connectomic analysis

llc copy verma datasets biorxiv connectomics

Play Episode Listen Later Dec 21, 2022

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.12.20.521276v1?rss=1 Authors: Nesbit, S. C., Parker, D., Verma, R., Osmanlioglu, Y. Abstract: Connectomics has been a rapidly growing discipline in neuroimaging and neuroscience that evolved our understanding of the brain. Connectomics involves representing the brain as a network of regions, where the parcellation of the brain into regions using a template atlas is an integral part of the analysis. Over developmental and young adult cohorts of healthy individuals, we investigated how choosing parcellation atlases at certain resolutions affect sex classification and age prediction tasks performed using deep learning on structural connectomes. Datasets were processed on a total of 35 parcellations, where the only significant difference was observed for age prediction on the developmental cohort with a slight improvement on higher resolutions. This indicates that choice of parcellation scheme is generally not critical for deep learning-based age prediction and sex classification. Therefore, results between studies using different parcellation schemes could be comparable and repeating analyses on multiple atlases might be unnecessary. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Embodied Cognitive Science and Artificial Intelligence

Creative Language Technologies

Play Episode Listen Later Nov 24, 2022 56:03

This is episode #27 of the podcast and it's Thursday, the 17th of November, 2022. My invited speaker today is Dr. Mark James, a philosopher and theoretical cognitive scientist who adopts an embodied approach to questions about the development of habits in both individuals and collectives. Specifically, he is interested in how the designed world shapes such habits, and how we can leverage this understanding to address questions of well-being. More recently, Mark has begun researching how psychological flexibility, our ability to switch between habits, is scaffolded by our bodies and environments. Mark hosts the Connectomics podcast, wherein he speaks with theorists and practitioners about the intersection of embodied cognitive science, culture, technology and design. Mark is also a meditator, musician and martial artist, and a lover of good stories.We started the show talking about his journey in this field and then delved deeper into aspects of Embodied Cognitive Science and its methodologies. We also looked into ways of studying subjective experience scientifically, and debated if subjective experience can be intersubjectively verified. Mark also elaborated Tom Froese's proposal for an Irruption Theory of Consciousness, a new theory of consciousness that integrates an embodied-enactive account of basic mind with radical formulations of the freedom and efficacy of intentional agency.The second part of the interview covered the future of digital technologies (including immersive technologies like mixed reality and artificial intelligence) — and if/how they can be (re)shaped by embodied cognitive science. Here is the show.Show Notes:- Embodied Cognitive Science - definition and methodologies- The scientific study of subjective experience - Can subjective experience be intersubjectively verified?- Tom Froese's proposal for an Irruption Theory of Consciousness- The future of digital technologies (including immersive technologies like MR and AI) The Connectomics podcast - https://podcasts.apple.com/ie/podcast/connectomics/id1606319926www.markmjames.com

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#7 Tony Zador: The Embodied Turing Test, Genomic Bottlenecks, Molecular Connectomics

The Embodied AI Podcast

Play Episode Listen Later Nov 16, 2022 89:49

Timestamps: (00:00) - Intro (02:08) - Tony's background, Costa Rican singing mouse (06:59) - Traditional & embodied Turing Test, large language models (15:16) - Mouse intelligence, evolution, modularity, dish-washing dogs? (26:16) - Platform for training non-human animal-like virtual agents (36:14) - Exploration in children vs animals, innate vs learning, cognitive maps, complementary learning systems theory (46:53) - Genomic bottleneck, transfer learning, artificial Laplacian evolution (01:02:06) - Why AI needs connectomics? (01:06:55) - Brainbow, molecular connectomics: MAPseq & BRICseq (01:14:52) - Comparative (corvid) connectomics (01:18:04) - "Human uniqueness" - why do/ don't people believe in evolutionary continuity (01:25:29) - Career questions & virtual mouse passing the Embodied Turing Test in 5 years? Tony's lab website https://zadorlab.labsites.cshl.edu/ Tony's Twitter https://twitter.com/TonyZador Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution - Embodied Turing Test paper (2022) https://arxiv.org/ftp/arxiv/papers/2210/2210.08340.pdf A critique of pure learning and what artificial neural networks can learn from animal brains paper (2019) http://zadorlab.labsites.cshl.edu/wp-content/uploads/sites/59/2019/08/A-critique-of-pure-learning-and-what-artificial-neuralnetworks-can-learn-from-animal-brains.pdf Genomic bottleneck paper (2021) http://zadorlab.labsites.cshl.edu/wp-content/uploads/sites/59/2021/03/Encoding-innate-ability-through-a-genomic-bottleneck.pdf MAPseq paper (2016) http://zadorlab.labsites.cshl.edu/wp-content/uploads/sites/59/2018/04/Zador-etal_2016_neuron_High-throughput-mapping.pdf BRICseq paper (2020) http://zadorlab.labsites.cshl.edu/wp-content/uploads/sites/59/2020/07/BRICseq-Bridges-Brain-wide-Interregional.pdf Squirrel ninja warrior course video https://www.youtube.com/watch?v=hFZFjoX2cGg Marbled Lungfish wiki https://en.wikipedia.org/wiki/Marbled_lungfish Papers about corvids https://www.science.org/doi/10.1126/science.1098410 https://link-springer-com.ezproxy1.bath.ac.uk/article/10.3758/s13420-020-00434-5 Twitter https://twitter.com/Embodied_AI

ai career human platform traditional exploration mouse embodied molecular comparative costa rican genomic bottlenecks turing test encoding connectomics laplacian zador

Connectomics of the Octopus vulgaris vertical lobe provides insight into conserved and novel principles of a memory acquisition network

Play Episode Listen Later Oct 4, 2022

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.03.510303v1?rss=1 Authors: Bidel, F., Meirovitch, Y., Schalek, R. L., Lu, X., Pavarino, E. C., Yang, F., Peleg, A., Wu, Y., Shomrat, T., Berger, D. R., Shaked, A., Lichtman, J. W., Hochner, B. Abstract: We present the first analysis of the connectome of the vertical lobe (VL) of Octopus vulgaris, a brain structure mediating the acquisition of long-term memory in this behaviorally advanced mollusk. Serial section electron microscopy revealed new types of interneurons, cellular components of extensive modulatory systems, and multiple synaptic motifs. The sensory input to the VL is conveyed via {approx}1,800,000 axons that sparsely innervate two parallel and interconnected feedforward networks formed by the two AM types, simple AMs (SAMs) and complex AMS (CAMs). SAMs make up 89.3% of the 25,000,000 AMs, each receiving synaptic input from only a single input neuron on its non-bifurcating primary neurite, suggesting that each input neuron is represented in only {approx}12 SAMs. The CAMs, a newly described amacrine type, comprise 1.6% of the amacrine population. Their bifurcating neurites integrate multiple inputs from the input axons and SAMs. While the SAM network appears to feedforward sparse memorizable sensory representations into the VL output layer, the CAMs appear to monitor global activity and feedforward a balancing inhibition for sharpening the stimulus-specific VL output. While sharing morphological and wiring features with circuits supporting associative learning in other animals, the VL has evolved a unique circuit that enables associative learning based strictly on feedforward information flow. Copy rights belong to original authors. Visit the link for more info Podcast created by PaperPlayer

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RoboEM: automated 3D flight tracing for synaptic-resolution connectomics

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Play Episode Listen Later Sep 10, 2022

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.08.507122v1?rss=1 Authors: Schmidt, M., Motta, A., Sievers, M., Helmstaedter, M. Abstract: Mapping neuronal networks from 3-dimensional electron microscopy data still poses substantial reconstruction challenges, in particular for thin axons. Currently available automated image segmentation methods, while substantially progressed, still require human proof reading for many types of connectomic analyses. RoboEM, an AI-based self-steering 3D flight system trained to navigate along neurites using only EM data as input, substantially improves automated state-of-the-art segmentations and replaces human proof reading for more complex connectomic analysis problems, yielding computational annotation cost for cortical connectomes about 400-fold lower than the cost of manual error correction. Copy rights belong to original authors. Visit the link for more info Podcast created by PaperPlayer

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#29: Mike Fox – Finding Therapeutic Treatment Targets using Causal Brain Connectomics

Stimulating Brains

Play Episode Listen Later Sep 3, 2022 100:13

Mike Fox leads the Center for Brain Circuit Therapeutics at the Brigham & Women's Hospital at Harvard Medical School in Boston. The center is unique in that it houses colleagues from neurosurgery, neurology, psychiatry and neuroradiology under the same roof – with the aim to collaboratively work on novel neuromodulation treatments. It is a great honor to interview Mike about his earlier work with Marc Raichle on anticorrelated networks in the brain, his work on TMS network mapping, lesion network mapping and DBS network mapping. Our conversation was enriched by guest questions of many friends and members of the center, Shan Siddiqi, Aaron Boes, Michael Ferguson, Fred Schaper and Dan Corp. We cover how lesion network mapping originated, why effective invasive and noninvasive neuromodulation targets must be linked by brain networks and ways Mike has taken to uncover those relationships. We talk about what makes causal sources of inference – brain lesions and neuromodulation targets – so unique to study the brain, treatment concepts that can be traced back to basic science work in animals vs. serendipitous findings in humans and discuss why and how brain lesions are set for a comeback – both for treatment and investigation.

Omniscient Neurotechnology: Connectomics, a Deeper Understanding of the Brain

The Bleeding Edge of Digital Health

Play Episode Listen Later Aug 30, 2022 58:07

In this week's episode, host Mike Moore speaks with Stephen Scheeler, the CEO and Managing Director of brain data company Omniscient Neurotechnology. Omniscient uses aritifical intelligence and a new field of neurotechnology called connectomics to create a digital map of the brain, known as a connectome, to better understand and eventually design unique treatments for conditions like depression, Alzheimer's, schizophrenia and stroke. Today, Stephen talks about how brain mapping works, the science behind connectomics, and the vast human potential Omniscient Neurotechnology is hoping to unlock. Notable Quotes “They said, this changes everything. This changes our understanding of the human brain so fundamentally that it's gonna unlock all kinds of human potential over the next, you know, hundred years. And when you have people with this caliber tell you those things, you take pause. And so we started to talk about how we could maybe work together. I started helping them figure out their business model. I went to China to talk to some of the researchers that were already working in that market. And I got more and more fascinated and just saw the potential of what we were trying to build. So I kind of got more and more involved.” – Stephen (10:35) “Everybody, every wiring, every building is a little different. And so what we do is we bring those wiring maps. We build those maps so that if you have pain or you forgot where you left your keys, or you're feeling a little anxious or depressed or whatever, if you're suicidal, if it's that severe, we can map that. And we can show where in your connectome, in your wiring, the problem lies. And so that's a complete transformation for everything we do about the human brain today.” – Stephen (15:31) In This Episode (01:33) Stephen's background (04:15) The genesis of Omniscient Neurotechnology (08:45) Neuroscience, AI and the field of connectomics (13:01) What is connectomics? (16:16) Connectomics and mental health treatment (17:31) How brain mapping works (27:10) The economic benefits and human potential of Omniscient Neurotechnology (29:44) Psychedelics and neuroscience (35:26) The surgical implications (48:56) The future of Omniscient (51:30) Omniscient's biggest challenges Our Guest Stephen Scheeler is the CEO and Managing Director of brain data company Omniscient Neurotechnology. Previously, he served as the CEO of Facebook for Australia and New Zealand. Stephen is also the founder of global advisory The Digital CEO, Senior Advisor to McKinsey & Company, and Executive-in-Residence at Asia-Pacific's leading business school, the Australian Graduate School of Management. He has advised the leaders of Qantas, Suncorp, Telstra, Wesfarmers, AMP, CUB, Brambles, Google, and the Australian Government. Stephen is also a member of the Australian Prime Minister's Knowledge Nation 100 as one of Australia's top innovation leaders. Resources & Links Mike Moore https://www.linkedin.com/in/michaeljeffreymoore/ https://www.linkedin.com/company/thebleedingedgeofdigitalhealth/ The Bleeding Edge of Digital Health Apple Podcasts Google Amazon Spotify YouTube Stephen Scheeler https://www.linkedin.com/in/stephenscheeler/?originalSubdomain=au https://www.linkedin.com/company/omniscientneurotechnology/ https://www.o8t.com/

Evolution, Technology, and the Brain – Intel on AI Season 3, Episode 13

Intel on AI

Play Episode Listen Later Aug 17, 2022 62:56

In this episode of Intel on AI host Amir Khosrowshahi talks with Jeff Lichtman about the evolution of technology and mammalian brains. Jeff Lichtman is the Jeremy R. Knowles Professor of Molecular and Cellular Biology at Harvard. He received an AB from Bowdoin and an M.D. and Ph.D. from Washington University, where he worked for thirty years before moving to Cambridge. He is now a member of Harvard's Center for Brain Science and director of the Lichtman Lab, which focuses on connectomics— mapping neural connections and understanding their development. In the podcast episode Jeff talks about why researching the physical structure of brain is so important to advancing science. He goes into detail about Brainbrow—a method he and Joshua Sanes developed to illuminate and trace the “wires” (axons and dendrites) connecting neurons to each other. Amir and Jeff discuss how the academic rivalry between Santiago Ramón y Cajal and Camillo Golgi pioneered neuroscience research. Jeff describes his remarkable research taking nanometer slices of brain tissue, creating high-resolution images, and then digitally reconstructing the cells and synapses to get a more complete picture of the brain. The episode closes with Jeff and Amir discussing theories about how the human brain learns and what technologists might discover from the grand challenge of mapping the entire nervous system. Academic research discussed in the podcast episode: Principles of Neural Development The reorganization of synaptic connexions in the rat submandibular ganglion during post-natal development Development of the neuromuscular junction: Genetic analysis in mice A technicolour approach to the connectome The big data challenges of connectomics Imaging Intracellular Fluorescent Proteins at Nanometer Resolution Stimulated emission depletion (STED) nanoscopy of a fluorescent protein-labeled organelle inside a living cell High-resolution, high-throughput imaging with a multibeam scanning electron microscope Saturated Reconstruction of a Volume of Neocortex A connectomic study of a petascale fragment of human cerebral cortex A Canonical Microcircuit for Neocortex

Connectonomics #6 - Dr. Mog Stapleton on the enactive approach, empirical science, and the role of ritual in eduction and transformation

Play Episode Listen Later Jun 28, 2022 82:41

In episode #6 of the Connectomics podcast, Mark speaks with philosopher of embodied cognitive science, Dr. Mog Stapleton. Mog is presently a visiting researcher at OIST, and is working on understanding the relationship between cognition and the gut-brain axis. In this episode, they talked about the role of aesthetics in the production of knowledge, the relationship between enaction and the empirical mind sciences, affectivity in cognition, enacting eduction and the value of ritual for transformation. This episode was edited and produced by Shane Byrne and in association with the Okinawa Institute of Science and Technology Graduate University. Cover art is provided by Cian Brennan.

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Connectonomics #5 - Roope Kaaronen on embodiment, sustainability and strategic design interventions

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Play Episode Listen Later Jun 6, 2022 115:18

In episode #5 of the Connectomics podcast, Mark speaks with sustainability researcher Roope Kaaronen. Roope is a postdoc at the University of Helsinki, Finland. In this episode, they talked about Roope's fortuitous introduction to ecological psychology and its value to his present work, his present work on strategic design interventions for large scale behaviour change/cultural evolution, the challenges and opportunities at the intersection between individual and collective change, nudging, and more. This episode was edited and produced by Shane Byrne and in association with the Okinawa Institute of Science and Technology Graduate University.

Alison Calder, "Synaptic" (U Regina Press, 2022)

New Books Network

Play Episode Listen Later May 17, 2022 46:45

This intricate, yearning work from award-winning poet Alison Calder asks us to think about the way we perceive and the ways in which we seek to know ourselves and others. In Synaptic (University of Regina Press, 2022) each section explores key themes in science, neurology, and perception. The first, Connectomics, riffs on scientific language to work with and against that language's intentions. Attempting to map the brain's neural connections, it raises fundamental questions about interiority and the self. The lyric considerations in these poems are juxtaposed against the scientific-like footnotes which, in turn, invoke questions undermining authority and power. The second section, Other Disasters, explores ways of seeing or and being seen, from considerations of folklore to modern art to daily life. Sine Yaganoglu trained as a neuroscientist and bioengineer (PhD, ETH Zurich). She currently works in innovation management and diagnostics. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

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In episode #4 of the Connectomics podcast, Mark speaks with theoretical cognitive scientist Marek McGann. Marek is a lecturer in the psychology department in Mary Immaculate College in Limerick, Ireland. In this episode they talked about Marek existing at the intersection of enaction and ecological psychology, the concept of behaviour settings and his understanding of agency as a multiscale affair, emergentism as a framing for embodied cognitive science, and the craft of research and education. This episode was edited and produced by Shane Byrne and in association with the Okinawa Institute of Science and Technology Graduate University.

science japan psychology ireland philosophy emergence cognition marek okinawa limerick cognitive science mcgann shane byrne mary immaculate college connectomics

Activeloop with Davit Buniatyan

Contributor

Play Episode Listen Later Apr 13, 2022 30:30

Eric Anderson (@ericmander) meets with Davit Buniatyan (@DBuniatyan) of Activeloop, the database for AI. Davit was inspired to found Activeloop while working on large datasets in a neuroscience research lab at Princeton. Powering the technology at Activeloop is Hub, the open-source dataset format for AI applications. Join us to learn how Hub promises to enhance and expand various verticals in deep learning. In this episode we discuss: Reconfiguring traditional ML tooling for the cloud Connectomics - working with thin slices of a mouse brain with neuroscientist Sebastian Seung Choosing between university, a start-up, and open-source Davit's original product, that ran computation on crypto mining GPUs on a distributed scale Focusing on different data modalities for computer vision Links: Activeloop Activeloop Hub Apache Parquet Apache Spark TensorFlow Snowflake Databricks Timescale People mentioned: Sebastian Seung (@SebastianSeung) Other episodes: TensorFlow with Rajat Monga

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Connectonomics #3 - Laura Mojica

science japan philosophy gender cognition okinawa cognitive science mojica shane byrne connectomics

Play Episode Listen Later Mar 11, 2022 64:42

In episode #3 of the Connectomics podcast, Mark speaks with philosopher Laura Mojica. At the time of recording Laura was also working in the Embodied Cognitive Science Unit at OIST. Laura works on the notion of normativity from an enactive standpoint, though she brings her experience working in analytic philosophy to bear on the topic. Laura is also very concerned with how enaction intersects with more cultural concerns and is interested in thinking about how enactive cognitive science can illuminate discussions about race, gender, intersectionality and so on. This episode was edited and produced by Shane Byrne and in association with the Okinawa Institute of Science and Technology Graduate University.

Connectonomics #2 - Dr. Fred Cummins on embodiment as a frame for the sciences

science technology japan philosophy speech sciences frame embodiment linguistics cognition okinawa cummins cognitive science university college dublin shane byrne connectomics

Play Episode Listen Later Feb 9, 2022 103:53

In episode #2 of the Connectomics podcast, Mark speaks with Dr. Fred Cummin, head of the Cognitive Science program at University College Dublin. Fred is a linguist and the originator of the field of study that is joint speech: where two or more people say the same thing at the same time, such as within religious rituals, at sporting events, or at political protests. Fred is also an incredibly well studied individual in a range of disciplines and has a profoundly philosophical, idiosyncratic and insightful take on embodiment as a frame within the sciences. This episode was edited and produced by Shane Byrne and in association with the Okinawa Institute of Science and Technology.

Opportunities of Connectomic Neuromodulation

Neuromodulation Today & Tomorrow

Play Episode Listen Later Feb 3, 2022 44:18

Machine Medicine Interview Series hosted by Dr Jonathan O'Keeffe with Prof. Andreas Horn. What is connectomics and how can it improve the response of the patients undergoing neuromodulation? Transcript for interview highlight: https://machinemedicine.com/interview-series/opportunities-of-connectomic-neuromodulation/ Find out more topics: https://machinemedicine.com/interview-series/

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Connectonomics - Trailer

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Play Episode Listen Later Jan 24, 2022 13:42

Connectomics explores the intersection between embodied cognitive science, philosophy, culture, technology and design.

Connectonomics #1 - Dr. Tom Froese on embodied cognitive science and its implications

science technology japan implications embodied cognition okinawa cognitive science froese shane byrne connectomics

Play Episode Listen Later Jan 24, 2022 108:23

In episode #1 of the Connectomics podcast, Mark speaks with Dr. Tom Froese, head of the Embodied Cognitive Science Unit at the Okinawa Institute of Science and Technology (OIST). The conversation touches on the central themes of embodied cognitive science and the enactive framework in particular, the methods of this approach, the mission of the unit at OIST, and some of the implications of developing or adopting such an understanding. This episode was edited and produced by Shane Byrne and in association with the Okinawa Institute of Science and Technology.

Connectomics: An Introduction

science connections cognition mark james connectomics

Play Episode Listen Later Oct 18, 2021 13:30

The Connectomics podcast, a spin-off from the OIST podcast, explores the intersection between embodied cognitive science, philosophy, culture, technology and design. In this intro episode of Connectomics, host, Mark James, research fellow in OIST's Embodied Cognitive Science Unit, asks the question 'Where is my mind?' and introduces you to some ideas that will provide some – possibly – surprising answers. Mark also talks about some of the central topics for the podcast going forward (e.g., embodied cognition, social cognition), and why he thinks these ideas are relevant to thinking about and maybe even addressing some of the problems of our present age.

2: When brains go wrong

Playing With Marbles

Play Episode Listen Later Sep 28, 2021 34:28

When something goes wrong with a brain we can't just get under the hood and poke around. So how do we figure out what's going wrong? Well sometimes the answer to that question involves watching monkeys watch TV, so we're taking a trip to a monkey cinema, and along the way learning about the different ways to see inside your head and find faulty wiring. Follow the show:Twitter: @marblespodInstagram: @marblespod

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Moritz Helmstädter: Wie funktioniert unser Gehirn?

Hertie-Interviews

Play Episode Listen Later Sep 27, 2021 26:07

Moritz Helmstaedter betreibt mit modernster Technik und einem hochmotivierten Team am Max-Planck-Institut für Hirnforschung in Frankfurt Grundlagenforschung am Gehirn auf seinem Forschungsgebiet der Connectomics. Das ist einerseits sehr spannend, weil viele Einblicke, die die Forscher erlangen, tatsächlich das erste Mal wissenschaftlich möglich sind. Andererseits bedeutet Grundlagenforschung auch oft, dass die praktische Anwendung der Forschungsergebnisse noch weit in der Zukunft liegen kann. Moritz Helmstaedter berichtet im Hertie-Interview, warum er optimistisch ist, dass seine Grundlagenforschung in Zukunft ihre Anwendung finden wird und welche Erkenntnisse man jetzt schon aus der Forschung ziehen kann.

66- Catching Up on Connectomics with Dr. Jeff Lichtman

catching up harvard university neuroscience lichtman connectomics

Play Episode Listen Later Jun 3, 2021 29:05

On this episode of Neurotransmissions, Dr Jeff Lichtman professor of Neuroscience at Harvard University and our guest from the 2nd episode of Neurotransmissions is back to visit during MPFI’s annual imaging course. We were lucky enough that Dr. Lichtman spared a bit of time to sit down with Misha and Alex to discuss recent progress that has been made in the field of connectomics. Learn about ongoing projects, technological progress and hurdles, and the growing understanding about the value of connectomics in understanding the brain.

Focusing on Research with Adam Marblestone [Idea Machines #33]

Idea Machines

Play Episode Listen Later Oct 26, 2020 66:47

A conversation with Adam Marblestone about his new project - Focused Research Organizations. Focused Research Organizations (FROs) are a new initiative that Adam is working on to address gaps in current institutional structures. You can read more about them in this white paper that Adam released with Sam Rodriques. Links FRO Whitepaper Adam on Twitter Adam's Website Transcript [00:00:00] In this conversation, I talked to Adam marble stone about focused research organizations. What are focused research organizations you may ask. It's a good question. Because as of this recording, they don't exist yet. There are new initiatives that Adam is working on to address gaps. In current institutional structures, you can read more about them in the white paper that Adam released recently with San Brad regens. I'll put them in the show notes. Uh, [00:01:00] just a housekeeping note. We talk about F borrows a lot, and that's just the abbreviation for focus, research organizations. just to start off, in case listeners have created a grave error and not yet read the white paper to explain what an fro is. Sure. so an fro is stands for focus research organization. the idea is, is really fundamentally, very simple and maybe we'll get into it. On this chat of why, why it sounds so trivial. And yet isn't completely trivial in our current, system of research structures, but an fro is simply a special purpose organization to pursue a problem defined problem over us over a finite period of time. Irrespective of, any financial gain, like in a startup and, and separate from any existing, academic structure or existing national lab or things [00:02:00] like that. It's just a special purpose organization to solve, a research and development problem. Got it. And so the, you go much more depth in the paper, so I encourage everybody to go read that. I'm actually also really interested in what's what's sort of the backstory that led to this initiative. Yeah. it's kind of, there's kind of a long story, I think for each of us. And I would be curious your, a backstory of how, how you got involved in, in thinking about this as well. And, but I can tell you in my personal experience, I had been spending a number of years, working on neuroscience and technologies related to neuroscience. And the brain is sort of a particularly hard a technology problem in a number of ways. where I think I ran up against our existing research structures. in addition to just my own abilities and [00:03:00] everything, but, but I think, I think I ran up against some structural issues too, in, in dealing with, the brain. So, so basically one thing we want to do, is to map is make a map of the brain. and to do that in a, in a scalable high-speed. Way w what does it mean to have a map of the brain? Like what, what would, what would I see if I was looking at this map? Yeah, well, we could, we could take this example of a mouse brain, for example. just, just, just for instance, so that there's a few things you want to know. You want to know how the individual neurons are connected to each other often through synopsis, but also through some other types of connections called gap junctions. And there are many different kinds of synopsis. and there are many different kinds of neurons and, There's also this incredibly multi-scale nature of this problem where a neuron, you know, it's, it's axon, it's wire that it sends out can shrink down to like a hundred nanometers in [00:04:00] thickness or less. but it can also go over maybe centimeter long, or, you know, if you're talking about, you know, the neurons that go down your spinal cord could be meter long, neurons. so this incredibly multi-scale it poses. Even if irrespective of other problems like brain, computer interfacing or real time communication or so on, it just poses really severe technological challenges, to be able to make the neurons visible and distinguishable. and to do it in a way where, you can use microscopy, two image at a high speed while still preserving all of that information that you need, like which molecules are aware in which neuron are we even looking at right now? So I think, there's a few different ways to approach that technologically one, one is with. The more mature technology is called the electron microscope, electromicroscopy approach, where basically you look at just the membranes of the neurons at any given pixel sort of black or white [00:05:00] or gray scale, you know, is there a membrane present here or not? and then you have to stitch together images. Across this very large volume. but you have to, because you're just able to see which, which, which pixels have membrane or not. you have to image it very fine resolution to be able to then stitch that together later into a three D reconstruction and you're potentially missing some information about where the molecules are. And then there's some other more, less mature technologies that use optical microscopes and they use other technologies like DNA based barcoding or protein based barcoding to label the neurons. Lots of fancy, but no matter how you do this, This is not about the problem that I think can be addressed by a small group of students and postdocs, let's say working in an academic lab, we can go a little bit into why. Yeah, why not? They can certainly make big contributions and have to, to being able to do this. But I think ultimately if we're talking about something like mapping a mouse brain, it's not [00:06:00] going to be, just a, a single investigator science, Well, so it depends on how you think about it. One, one, one way to think about it is if you're just talking about scaling up, quote, unquote, just talking about scaling up the existing, technologies, which in itself entails a lot of challenges. there's a lot of work that isn't academically novel necessarily. It's things like, you know, making sure that, Improving the reliability with which you can make slices of the brain, into, into tiny slices are making sure that they can be loaded, onto, onto the microscope in an automated fast way. those are sort of more engineering problems and technology or process optimization problems. That's one issue. And just like, so Y Y Can't like, why, why couldn't you just sort of have like, isn't that what grad students are for like, you know, it's like pipetting things and, doing, doing graduate work. So like why, why couldn't that be done in the lab? That's not why [00:07:00] they're ultimately there. Although I, you know, I was, I was a grad student, did a lot of pipetting also, but, But ultimately they're grad student. So are there in order to distinguish themselves as, as scientists and publish their own papers and, and really generate a unique academic sort of brand really for their work. Got it. So there's, there's both problems that are lower hanging fruit in order to. in order to generate that type of academic brand, but don't necessarily fit into a systems engineering problem of, of putting together a ConnectTo mapping, system. There's also the fact that grad students in, you know, in neuroscience, you know, may not be professional grade engineers, that, for example, know how to deal with the data handling or computation here, where you would need to be, be paying people much higher salaries, to actually do, you know, the kind of industrial grade, data, data piping, and, and, and many other [00:08:00] aspects. But I think the fundamental thing that I sort of realized that I think San Rodriquez, my coauthor on this white paper also realized it through particularly working on problems that are as hard as, as clinic Comix and as multifaceted as a system building problem. I th I think that's, that's the key is that there's, there's certain classes of problems that are hard to address in academia because they're system building problems in the sense that maybe you need five or six different. activities to be happening simultaneously. And if any, one of them. Doesn't follow through completely. you're sort of, you don't have something that's novel and exciting unless you have all the pieces putting, you know, put together. So I don't have something individually. That's that exciting on my own as a paper, Unless you, and also three other people, separately do very expert level, work, which is itself not academically that interesting. Now having the connectome is academically [00:09:00] interesting to say the least. but yes, not only my incentives. but also everybody else's incentives are to, to maybe spend say 60% of their time doing some academically novel things for their thesis and only spend 40% of their time on, on building the connectome system. Then it's sort of, the probability of the whole thing fitting together. And then. We see everyone can perceive that. And so, you know, they basically, the incentives don't align well, for, for what you would think of as sort of team science or team engineering or systems engineering. yeah. And so I'm like, I think, I think everybody knows that I'm actually like very much in favor of this thing. So, I'm going to play devil's advocate to sort of like tease out. what I think are. Important things to think about. so, so one sort of counter argument would be like, well, what about projects? Like cert, right? Like that [00:10:00] is a government yeah. Led, you should, if you do requires a lot of systems engineering, there's probably a lot of work that is not academic interesting. And yet, it, it, it happens. So like there's clearly like proof of concepts. So like what what's like. W why, why don't we just have more things like, like certain for, the brain. Yeah. And I think this gets very much into why we want to talk about a category of focused research organizations and also a certain scale, which we can get into. So, so I think certain is actually in many ways, a great example of, of this, obviously this kind of team science and team engineering is incredible. And there are many others, like LIGO or, or CBO observatory or the human genome project. These are great examples. I think the, the problem there is simply that these, these are multibillion dollar initiatives that really take decades of sustained. government involvement, to make it happen. And so once they get going, and [00:11:00] once that flywheel sort of start spinning, then you have you have it. And so, and so that, that is a nonacademic research project and also the physics and astronomy communities, I think have more of a track record and pipeline overall. perhaps because it's easier, I think in physical sciences, then in some of these sort of emerging areas of, of, you know, biology or sort of next gen fabrication or other areas where it's, it's, there's less of a, a grounded set of principles. So, so for CERN, everybody in the physics basically can agree. You need to get to a certain energy scale. Right. And so none of the theoretical physicists who work on higher energy systems are going to be able to really experimentally validate what they're doing without a particle accelerator of a certain level. None of the astronomers are gonna be able to really do deep space astronomy without a space telescope. and so you can agree, you know, community-wide that, This is something that's worth doing. And I think there's a lot of incredible innovation that happens in those with focus, research organizations. We're thinking about a scale that, [00:12:00] that sort of medium science, as opposed to small science, which is like a, you know, academic or one or a few labs working together, Or big science, which is like the human genome project was $3 billion. For example, a scope to be about $1 per base pair. I don't know what actually came out, but the human genome has 3 billion basis. So that was a good number. these are supposed to be medium scale. So maybe similar to the size of a DARPA project, which is like maybe between say 25 and. A hundred or $150 million for a project over a finite period of time. And they're there. The idea is also that they can be catalytic. So there's a goal that you could deliver over a, some time period. It doesn't have to be five years. It could be seven years, but there's some, some definable goal over definable time period, which is then also catalytic. so in some ways it will be more equivalent to. For the genome project example, what happened after the genome project where, the [00:13:00] cost of genome sequencing through, through new technologies was brought down, basically by a million fold or so is, is, is, how George Church likes to say it, inventing new technologies, bringing them to a level of, of readiness where they can then be, be used catalytically. whereas CERN, you know, It's just a big experiment that really has to keep going. Right. And it's also sort of a research facility. there's also permanent institutes. I think there's a, is a, is a, certainly a model that can do team science and, and many of the best in the brain mapping space, many of the sort of largest scale. connectomes in particular have come either from Janelia or from the Allen Institute for brain science, which are both sort of permanent institutes, that are, that are sort of, nonacademic or semi academic. but that's also a different thing in the sense that it's, it takes a lot of activation energy to create an Institute. And then that becomes now, a permanent career path rather than sort of focusing solely on what's the shortest path to. To some [00:14:00] innovation, the, the, the permanence. So, so the, the flip side of the permanence is that, I guess, how are you going to convince people to do this, this, like this temporary thing, where. I think, someone asked on Twitter about like, you know, if it's being run by the government, these people are probably going to get, government salaries. So you're, you're getting a government salary, without the like one upside of a government job, which is the security. so like what, what is the incentive for, for people to, to come do this? Yeah. And I think, I think it depends on whether it's government or philanthropic, philanthropic fro Faros are also definitely. An option and maybe in many ways more flexible, because the, you know, the government sort of has to, has to contract in a certain way and compete out, you know, contracts in a certain way. They can't just decide, the exact set of people to do something, for example. So, so the government side has. Both a huge [00:15:00] opportunity in the sense that I think this is a very good match for a number of things that the government really would care about. and the government has, has, has the money, and resources to do this, but philanthropic is also one we should consider. but in any case, there are questions about who and who will do Froy and, and why. and I think the basic answer though, it, it comes down to, it's not a matter of, of cushiness of the career certainty. it's, it's really, these are for problems that are not doable any other way. this is actually in many ways, the definition is that you're only going to do this. if this is the only way to do it, and if it's incredibly important. So it really is a, it's a medium scale moonshots. you would have to be extremely passionate about it. That being said, there are reasons I think in approximate sense why one might want to do it both in terms of initiating one and in terms of sort of B being part of them. [00:16:00] so one is simply that you can do science. that is for a fundamental purpose or, or, or, pure, purely driven toward your passion to solve a problem. and yet can have potentially a number of the affordances of, of industry such as, industry competitive salaries, potentially. I think the government, we have to ask about what the government can do, but, but in a certain philanthropic setting, you could do it another aspect that I think a lot of scientists find. Frustrating in the academic system is precisely that they have to. spend so much work to differentiate themselves and do something that's completely separate from what their friends are doing, in order to pay the bills basically. So, so if, if you don't eventually go and get your own appealing, you know, Tenure track job or, or so on and so forth. the career paths available in academia are much, much fewer, and often not, not super well compensated. And, and [00:17:00] so there are a number of groups of people that I've seen in sort of, if you want critical mass labs or environments where they're working together, actually, despite perhaps the. Incentive to, to, differentiate where they're working, does a group of three or four together. and they would like to stay that way, but they can't stay that way forever. And so it's also an opportunity if you, if you have a group of people that wants to solve a problem, to create something a little bit like, like a seal team. so like when, when I was, I'm not very generally militaristic person, but, when I was a kid, I was very obsessed with the Navy seals. But, but anyway, I think the seal team was sort of very tight knit. kind of a special forces operation that works together on one project is something that a lot of scientists and engineers I think want. and the problem is just that they don't have a structure in which they can do that. Yeah. So then finally, I think that, although in many cases maybe essentially built into the structure fro is make sense. We can [00:18:00] talk about this as, as nonprofit organizations. these are the kinds of projects where, you would be getting a relatively small team together to basically create a new industry. and if you're in the right place at the right time, then after an fro is over, you would be in the ideal place to start. The next startup in an area where it previously, it's not been possible to do startups because the horizons for a venture investment would have been too long to make it happen from the beginning. Well, that's actually a great transition to a place that I'm still not certain about, which is what happens. After it fro, cause you, you said that it, that it's a explicitly temporary organization. And then, how do you make sure that it sort of achieves its goal, right? Like, because you can see so many of these, these projects that actually sound really great and they like go in and possibly could do good work and then somehow it all just sort of diffuses. [00:19:00] so, so have you thought about how to sort of make sure that that lives on. Well, this is a tricky thing as we've discussed, in a number of settings. So, in a, like to maybe throw that question back to you after I answer it. Cause I think you have interesting thoughts about that too, but, but in short, it's, it's a tricky thing. So, so the fro. Is entirely legal focused there isn't, there's no expectation that it would continue, by default and simply because it's a great group of people, or because it's been doing interesting work, it's sort of, it is designed to fulfill a certain goal and it should be designed also from the beginning to have a, a plan of the transition. Like it could be a nonprofit organization where it is explicitly intended that at the end, assuming success, One or more startups could be created. One or more datasets could be released and then a, you know, a much less expensive and intensive, nonprofits, structure could be be there to [00:20:00] host the data and provide it to the world. it could be something where. the government would be using it as a sort of prototyping phase for something that could then become a larger project or be incorporated into a larger moonshot project. So I think you explicitly want a, a goal of a finite tune to it, and then also a explicit, upfront, deployment or transition plan, being central to it much more so than any publication or anything. Of course. At the same time. there is the pitfall that when you have a milestone driven or goal focused organization, that the funder would try to micromanage that and say, well, actually, not only do I care about you meeting this goal, but also I really care that by month six, you've actually got exactly this with this instrument and this throughput, and I'm not going to let you buy this other piece of equipment. Unless, you know, you show me that, you know, [00:21:00] and that's a problem that I think, we sometimes see with, externalized research models, like DARPA ARPA models, that try to. achieve more coordination and, and, and goal driven among otherwise, somewhat uncoordinated entities like contractors and, and universities that, that are working on programs, but then they, they, they, they achieve that coordination by then, managing the process and, with an fro, I think it will be closer to. You know, if you have a series, a investment in the startup, you know, you are reporting back to your investors and, and they, they, at some level care, you know, about the process and maybe they're on your board. but ultimately the CEO gets to decide, how am I going to spend the money? And it's extremely flexible to get to the goal. Yeah. Yeah. The, the micromanage, like [00:22:00] figuring out how to avoid, Micromanagement seems like it's going to be really tricky because it's sort of like once you get to that amount of money, I like, have you, have you thought about, like how, like, if you could do some kind of like actually, well, I'll, I'll give her the, the, the, the, the, the thing that the cruxy thing is like this, I think there's a huge amount of trust that needs to happen in it. And what I'm. like I constantly wonder about is like, is there this like fundamental tension between the fact that, especially with like government money, we really do want it to be transparent and well-spent, but at the same time, in order to sort of do these like knowledge frontier projects, sometimes you need to do things that. Are a little weird or like seem like a waste of money at the time, if you're not like intimately connected. and so there's, there's this sort of tension [00:23:00] between accountability and, Sort of like doing the things that need to get done. I agree with that and Efros, we're going to navigate that. Yeah. I agree with that. And I think it relates to a number of themes that you've touched on and that we've discussed with, which has sort of, has to do with the changing overall research landscape of, in what situations can that trust actually occur, you know, in bell labs, I think there was a lot of trust. throughout, throughout that system. And as you have more externalized research, conflicting incentives and so on it, it's, it's hard. It's hard to obtain that trust. startups of course, can align that financially, to a large degree. I think there are things that we want to avoid. so one of the reasons I think that these need to be scoped as. Deliverables driven and roadmaps, systematic projects over finite periods of time, is to avoid, individual [00:24:00] personalities, interests, and sort of conflicting politics, ending up. Fragmenting that resource into a million pieces. So, so I think this is a problem that you see a lot with billion dollar scale projects, major international and national initiatives. Everybody has a different, if you say, I want this to be, to solve neuroscience, you know, and here's $10 billion. Everybody has a different opinion about what solved neuroscience is. And there's also lots of different conflicting personalities and, and leadership there. So I think for an fro, there needs to be an initial phase, where there's a sort of objective process of technology roadmapping. And people figure people understand and transparently understand what are the competing technologies? What are the approaches? What, what are the risks? And you understand it. and you also closely understand the people involved. but importantly, the people doing that roadmapping and sort of catalyzing the initial formation of that [00:25:00] fro need to have a somewhat objective perspective. It's not just funding my lab. It's actually, you, you want to have vision, but you, you need to. Subjected to a relatively objective process, which, which is hard because you also don't want it to be a committee driven consensus process. You want it to be active, in, in a, in a systematic, analysis sense, but, but not in a, everyone agrees and likes it, you know, emotionally sense. and so that, that's a hard thing. but you need to establish it's that trust upfront, with, with the funder, And that's a hard process and it gets a hard process to do as a large government program. I think DARPA does it pretty well with their program managers where a program manager will come in and they will pitch DARPA on the idea of the program. there'll be a lot of analysis behind it and, but then once, once they're going, that program manager has tremendous discretion, and trust. To how they actually run that [00:26:00] project. And so I think you need something like a program manager driven process to initiate the fro and figure out is there appropriate leadership and goals and our livable as reasonable, Yeah, that seems the way, at least the way that it's presented in the paper, it, it feels a little bit chicken and egg in that. so with DARPA, DARPA is a sort of permanent organization that brings in program managers. And then those programmers program managers then go, start programs, whereas, The look at fro it seems like there's this chicken and egg between like, you sort of, you need someone spearheading it. It seems like, but then it, you sort of like, it, it seems like it will be very hard to get someone who's qualified to, to spearhead it, to do that before you have funding, but then you need someone spearheading it in order to get that [00:27:00] funding. yeah. Like, yeah. How, how are you thinking about. Cracking that that's, that's sort of the motivation for me behavior over the next year or two, is that I'm trying to go out and search for them. And, a little bit of it is from my own creativity, but a lot of it is going out and talking to people and try and understand what the best ideas. Here would be, and who are the networks of, of human beings behind those ideas, and trying to make kind of a prioritized set of borrows. Now, this kind of thing would have to be done again, I think to some degree, if there was a, larger umbrella program that someone else wanted to do, but, I'm both trying to get a set of, of exemplary. And representative ideas and people together, and try to help those people get funding. You know, I think there can be a stage process. I agree that, in the absence of a funder showing [00:28:00] really strong interest, people committing, to really be involved is difficult, because it is a big change to people's normal. Progression through life to do something like that. but just like with startups, to the extent that you can identify, someone who's. We spiritually just really wants to do this and we'll kind of do anything to do it, the sort of founder type, and also teams that want to behave like that. that's obviously powerful, and also ideas where there's a kind of inevitability, where based on scientific roadmapping, it, it just has to happen. There's no way, you know, for neuroscience to progress unless we get better. Connectomics and I think we can go through many other fields where, because of. The structures we've had available and just the difficulty of problems now, where arguably Faros are needed in order to make progress in fields that people really care about. So, so I think you can get engagement at the level of, of discussion, and, and, and starting to nucleate [00:29:00] people. But, but there is a bit of a chicken and egg problem. In the sense that it's, it's not so much as here's an fro, would you please fund to me it's we need to go and figure out where there might be Faros to be had, and then who is interested in those problems as well to, to fund and support those things. So, yeah. So I guess to recap what I see your process that is, is that you're going out, you're sort of really trying to. Identify possible people possible ideas, then go to funders and say, here, like sort of get some, some tentative interest of like, okay, what, which of these things might you be interested in if I could get it to go further and then you'll circle back to. the, the people who might be interested in sort of say like, okay, I have someone, a funder who's potentially interested. Can we [00:30:00] sort of like refine the idea? and then sort of like, like you will drive that loop hopefully to, Getting a, an fro funded that's right. And there's, there's further chicken and egg to it. that has to be solved in the sense that, when you go to funders and you say, why, you know, I have an idea for an fro. We also need to explain what an fro is, right? in a way that both, engages people in creating these futuristic models, which many people want to do, While also having some specificity of, of what we're looking for and what, what, what we think is as possible. So, and then the same on, on the, on the side of, of scientists and engineers and entrepreneurs all over the world, who, you know, have the ideas certainly, but most of those ideas have been optimized to hit, the needs of existing structures. So, so we are, we are trying to, I think, broker between those, And [00:31:00] then start prototyping a few. but the, you know, the immediate thing I think is to make, w Tom Coolio has referred to a catalog, a Sears catalog of moonshots. and so we're trying to make a catalog of, of moonshots that fit the fro category. but that sounds like the perfect name for this podcast, by the way. the cataloging mood child, like, you're kind of kind of cataloging moonshots and ways to get moonshots and yeah, absolutely. Yeah. and so I guess another sort of, thing that I've seen, and I'm not sure, it's almost like for people like a lot of people who like really want. Who like sees something as inevitable and they really want to get it done. In sort of like the current environment we're recording in October, 2020. there's. There's sort of this perception that capital is really cheap. [00:32:00] you know, there's a lot of venture capitalists there. They're pretty aggressive about funding and one could make an argument that, if it's, if it, it really is going to be inevitable and it really is going to start a new industry. Then that is exactly where venture capital funding should come in. And I do see this a lot where people, you know, it's like they have this thing that they really want to see exist and they, you know, come out of the lab and it started a company that's sort of extremely common. so. I guess, like, what almost would you say to someone who you see doing this that you think maybe should do an fro instead? Yeah, that's a great question. I mean, I think it's a complicated question and obviously, you know, we got to see VC also, you know, obviously VC backed, you know, innovation is, is, is one of, if not sort of the key, [00:33:00] Things that is driving technology right now. So, so I'm in no way saying that fro is, are somehow superior to two startups, in any generalized way. So I think that things that can be startups and are good as startups should be startups and people, if you have an idea that could be good for a startup, I think you should go do it. Generally speaking. But, there, there are a few considerations, so yeah. So I think you can divide it into categories where VCs, no, it's not a good idea for startups. And therefore won't talk to you, in cases where VCs don't always know whether or not it's good for a startup or whether there's a way that you could do it as a startup, but it would involve some compromise that is actually better not to make, even potentially for the longterm. economic prospects of, of an area. So things that can happen, would be, if you have something that's basically meant to be a kind of platform technology or which you [00:34:00] need to develop a tool or a platform in order to explore a whole very wide space of potential applications. maybe you have something like a new method of microscopy or something, or a new way to measure proteins in the cell or things like that, that, you know, you could target it to a very particular, if you want product market fit application, where you would be able to make the most money on that and get the most traction, the soonest. Yeah. Sometimes people call this, you know, the, the, the, the sort of Tesla Roadster, equivalent. You want to guys as quickly as you can to the Tesla Roadster. And I think generally, what people are doing with, with that kind of model, where you take people that have science, to offer, and you say what's the closest fastest you can get, to a Tesla Roadster that lets you it lets you build, get, get revenue and start, start being financially sustainable and start building a team, to go further. generally that's really good. and generally we need more scientists to learn how to do that. it'd be supported to do that, but, [00:35:00] sometimes you have things that really are meant to be. either generalized platforms or public goods, public data, or knowledge to underlie an entire field. And if you work to try to take the path, the shortest path to the Roadster, you would end up not producing that platform. You would end up, producing something that is specialized to compete in that lowest hanging fruit regime, but then in the, in, in doing so you would forego the more general larger. Larger thing. And, you know, Alan Kay has, has the set of quotes, that Brett Victor took is linked on his website. and I think Alan K meant something very different actually, when he said this, but he's, he refers to the dynamics of the trillions rather than the billions. Right. and this is something where in, and we can talk about this more. I'd be curious about your thoughts on that, but something like the transistor. You know, you, you could try to do the transistor as a startup. and maybe at the time, you know, the best application for transistors would have been [00:36:00] radios. I don't think like that. I think it was, it was guiding a rockets. Yeah. So you could have, you could have sort of had had a transistors for rockets company and then tried to branch out into, becoming Intel. You know, but really, given the structures we had, then the transistor was allowed to be more of a, a broadly, broadly explored platform. yeah, that, that progressed in a way where we got the trillions version. And I worry sometimes that even some startups that have been funded at least for a seed round kind of stage, and that are claiming that they want to develop a general platform are going to actually struggle a little bit later. when investors, you know, see that, see that they would need to spend way more money to build that thing. then the natural shortest path to a Roadster, or another words the Roadster is, is, somehow illusory. yeah. Yeah, this [00:37:00] is, this is a. Sort of like a regime that I'm really interested in and a, just on the transistor example, I've, I've looked at it. So just the, the history is that it was developed at bell labs, in order to prevent a T and T from being broken up, bell labs had to, under strictly licensed a bunch of their innovations, including the transistor William Shockley went off and, Started, chocolate semiconductor, the traders eight then left and started, Fairchild and then Intel. And, believe that that's roughly the right history. but the, the really interesting thing about that is to ask the question of like, one, what would have happened if, bell labs had exclusive license to the transistor and then to what would have happened if they had like exclusively licensed it to, Shockley semiconductor. And I think I would argue in both of those situations, you don't [00:38:00] end up. Having the world we have today because I fell labs. It probably goes down this path where it's not part of the core product. and so they just sort of like do some vaguely interesting things with it, but are never incentivized to like, you know, invent, like the, the planner processing method or anything. Interesting. yeah. Yeah. And so I guess where I'm. Go. And then like at the same time, the interesting thing is like, so Shockley is more, akin to like doing a startup. Right. And so it's like, what if they had exclusive license to it? And the, what I would argue is actually like that also would've killed it because, you have like, they had notoriously bad management. And so if you have this, this company with. And like the only reason that, the trader could go and start a Fairchild was because they, that was, that was [00:39:00] an open license. So this is actually a very long way of asking the question of, if F borrows are going to have a huge impact, it seems like they should default to. Really being open about what they create from like IP to data. but at the same time, that sort of raises this incentive problem where, people who think that they are working on something incredibly valuable, should want to do a startup. And then. And so there, and then similarly, even if they'd be like that sort of couldn't be a thing, they would want to privatize as much of the output of an fro. and so which. Maybe necessary in order to, to get the funding to make it happen. So I guess like, how are you thinking about that tension? That was a very long winded. Yeah. [00:40:00] Yeah. Well, there's, there's a lot, a lot there, I think, to loop back to you. So, so I think, right, so, so this idea that we've talked a bit about as sort of default openness, so, so things that can be open for maximum impact should be open. there are some exceptions to that. So, so if, And it's also has to do partly with how you're scoping the problem. Right? So, so rather than having an SRO that develops drugs, let's say, because drugs really need to be patentable, right. In order to get through clinical trials, we're talking about much more money than the fro funding, you know, to do the initial discovery of a target or something. Right. So to actually bring that to humans, you know, you need to have the ability to get exclusive IP. for downstream investors and pharma companies that that would get involved in that. so there are some things that need to be patented in order to have to have their impact. but in general, you, you want, I think fro problems to steer themselves to things where indeed. it can be maximally open and maybe, maybe you, you provide [00:41:00] a system that can be used to, to, or underlie the discovery of a whole new sets of classes of drugs and so on. But you're not so much focused on the drugs themselves. Now, that being said, right. if I invest in an SRO, and I've enabled this thing, right. It kind of would make sense for the effort, you know, maybe three of the people of, of, of, of 15 in the fro will then go and start a company afterwards that then capitalizes on this and actually develops those drugs or what have you, or it takes it to the next stage. And gosh, it would really make sense if I had funded in fro. that's, those people would like to take me as a sort of first, first, first refusal to get a good deal on, on investing in this startup, for example. Right. so I think there are indirect network-based, or potentially even legal based, structure, structure based ways to both incentivize the investors and, But it's, it's a weaker, admittedly weaker, incentive financially than, [00:42:00] than, than the full capture of, of, of something. But then, but then there's, I think this gets back to the previous discussion. So which is sort of the trillions rather than the billions. So if you have something where maybe there are 10 different applications of it, Right in 10 different fields. you know, maybe, maybe we have a better way to measure proteins and based on this better way to measure proteins, we can do things in oncology and we can do things in Alzheimer's and we can do things in a bunch of different directions. We can do things in diagnostics and pandemic surveillance, and so many fields that one startup, It would be hard even to design, to start, if that could capture all of that value just as it would have been hard to design sort of transistor incorporated. Right. Right. given that, I think there's, there's a lot of reason to. To do an fro and then explore the space of applications. Use it as a means to explore a full space in which you'll then get [00:43:00] 10 startups. so if I'm the investor, I might like to be involved in all 10 of the new industry, right. And the way to do that would be to create a platform with which I can explore, but then I have a longer time horizon. Cause I have to first build the thing. Then I have to explore the application space and only then. do I get to invest in a specific verticals, right? Yeah. I think the, the two sort of tricky questions that I, I wonder about what that is one. So you mentioned like, Oh, there's 15 people in an fro, three of them go off to start a startup. What about those other 12 people? Like, I, I assume that they might be a little bit frustrated if, if that happens, Yeah, because like, like they, they did, they did help generate that value in it. It sort of gets into two questions of like capturing, like sort of kicking back, value generated by research in general, but like, yeah, it could, it could, it could be all 15 people, you know, we saw something [00:44:00] similar with open AI, you know, in a way, for example, converting, you know, into, into a, for profit or at least a big arm of it being, being the for-profit, and keeping all the people. Right. So you, you, you, you can imagine, just blanket converting. but yeah, I think, I think it's sort of, In the nature of it, that these are supposed to be things that open up such wide spaces that there's, there's sort of enough for everyone, but no, no, no one person necessarily one startup would completely capture. And I think that's true for clinic Comix too, for example. Right. So if you had really high throughput clinical, connectomics just, just to keep going on this example, that's a great example of perfect. It's a good thing as a good example. It's not. Depending on the details, whether this is exactly the first fro or not. I think it's totally, totally other issue, but, but. Connectomics there's potentially applications for AI and you know, how, how the neurocircuits work, and sort of fundamental, funding. Mental is a brain architecture and intelligence. although there's a bunch of ranges of the sort of uncertainty of exactly what that's going to be. So it's hard to sort of [00:45:00] know it until you see the data. There's also potentially applications for something like drug screening, where you could put a bunch of different, Kind of some CRISPR molecules or drug perturbations on, on a, on a brain and then look at what each one does to their, the synopsis or, and look at that in a, in a brain region specific way and sort of have ultra high, but connect to them based drug screening. Neither of those are things you can start a start up until you have connected. Right. working. but so anyway, so maybe three people would start an AI company and maybe those would be the very risk tolerant ones. and then three would start at, you know, a crisper drug company and, and, and, three would just do, do fundamental neuroscience with it and, take those capabilities and, and, and go, go back into the university system or so on and yeah. And start using that. Yeah. And the, the sort of the other related to. like creating value with it. there's, there's a little like uncut discomfort that like even I have [00:46:00] with, say like philanthropic or government funding, then going to fund a thing that proceeds to make a couple of people very wealthy. Which like, and like, there's very much arguments on both sides, right. Where it's like, it'll generate a lot of good for the world. and, and all and, and such. so, so like, I guess what would you say? I guess like, as a, as a, like, if I were a very wealthy philanthropist and I'm like, do it, like, you know, it's like, I'm just giving away money so that these people can. Yeah, the company is a complicated thing. Right? How much, how many further rich people, you know, did the Rockefeller foundation, you know, investing in the basics of molecular biology or things like that ended up generating? I mean, I think that, I think you, I think in some way the government does want to end up is they want the widely distributed benefit. And I think everything that should be an SRO should have widely distributed benefits. It shouldn't just [00:47:00] be a kind of, A startup that just, just enhances one, one person. It should be something that really contributes very broadly to economic growth and understanding of the universe and all that. But it's almost inevitable. I think that, if you create a new industry, you're gonna, you know, you're gonna, you're gonna feel it going to be some more written about rich successful people in that industry. And they're probably going to be some of the people that were involved. Early and thinking about it for the longest and waiting for the right time to really enter it. And so, yeah, that's a really good point. I guess the, then the question would be like, how do you know, like, like what are, what are sort of a, the sniff test you use to think about whether something would have broadly distributed benefits? That's a great question. Cause it's like connect to them. It seems like fairly clear cut or, or generating sort of like a massive data set that you then open up. Feels very [00:48:00] clear. Cut. it's. We we've talked before about that, like fro is, could like scale up a process or build a proof of concept of, of a technology. and it, it seems like that it's less clear cut how you can be sure that those are going to, like if they succeed. Yeah. I mean, there are a few different frames on it, but I mean, I think one is, FRS could develop technologies that allow you to really reduce the cost of having some. Downstream set of capabilities. so, you know, if, just to give you an example, right? If, if we had, much lower costs, gene therapies available, right? So, so sometimes when drug prices are high, you know, this is basically it's recouping these very large R and D costs and then there's competition and, and, and profit and everything involved. you know, there was the marching squarely situation and, you know, there's a bunch of, sort of. What was that? there was, remember the details, but there [00:49:00] was some instance within which, a financially controlling entity to sort of arbitrarily bumped drug prices way high, right. A particular drug. and then w was, you know, was regarded as an evil person then, and maybe that's right. but anyway, there are some places I think, within the biomedical system where you can genuinely reduce costs for everyone. Right. and it's not simply that I, you know, I make this drug and I captured a bunch of value on this drug, but you know, it's really, it should be available to everyone and I'm just copying there. There's genuine possibility to reduce costs. So if I could reduce the cost of, of the actual manufacturing of. The viruses that you use for gene therapy, that's a, that's a process innovation. that would be, you could order as a magnitude drop the cost of gene therapy. If you could figure out what's going on, in the aging process and what are the real levers on a single, you know, biological interventions that would prevent multiple age related diseases that [00:50:00] would massively drop the cost. Right? So those, those are things where, Maybe even in some ways it would be threatening, to some of, some of the pharma companies, you know, that, that work on specific age related diseases, right? Because you're going to have something that, that replaced, but this is, this is what, you know, things that are broad productivity improvements. And I think economists and people very broadly agree that, that the science and technology innovations, For the most part. although sometimes they can be used to in a way that sort of, only benefits, a very small number of people that generally speaking there's a lot you can do, with technology that will be extremely broadly shared in terms of benefit, right? Yeah. Yeah. I mean, I, I do actually, like I agree with that. I'm, I'm just, I'm trying to represent as much skepticism as, as possible. Definitely. I know you agree with that. And actually, another thing that I have no idea about which I'm really interested in is as you're going and sort of creating this, [00:51:00] this moonshot catalog. how do you tell the difference between people who have these really big ideas who are like hardcore legit? but like maybe a little bit crazy. And then people who are just crackpots. Yeah, well, I don't claim to be able to do it in every field. and, and I think there's a reason why I've, I'm not trying to do a quantum gravity, fro you know, both, both, because I don't know that that's, you know, I think that's maybe better matched for just individual. Totally. Open-ended Sunday, you know, fun, brilliant people for 30 or 40 year long period to just do whatever they want. Right. Yeah. For quantum gravity, rather than directed, you know, research, but, But also because there's a class of problem that I think requires a sort of Einsteinian type breakthrough in fr fro is, are not, not perfect for that in terms of finding people. I mean, I, I find that, there's a lot of pent up need for, this is that's my preliminary feeling. and you can see there's a [00:52:00] question of prioritizing, which are the most important, but there's a huge number of. Process innovations or system building innovations that are needed across many, many fields. And you don't need to necessarily have things that even sound that crazy. There are some that just kind of just make sense, you know, are, are very simple. You know, we here, here in our lab, we have this measurement technology, but we, you know, we can only have the throughput of one cell, you know, every, every few weeks. And if we could build the system, we could get a throughput of, you know, A hundred thousand cells, you know, every month or something. Right. there are some, there's some sort of ones that are pretty obvious, or where there's an obvious inefficiency. In kind of, how things are structured. Like every, every company and lab that's that's modeling fusion reactors, and then also within the fusion reactor, each individual component of it, like the neutrons in the wall versus the Plaza and the core, those are basically modeled with different. Codes many of which are many [00:53:00] decades old. So there's sort of an obvious opportunity to sort of make like a CAD software for fusion, for example, you know, that the, the, it doesn't, it's not actually crazy. It's actually just really basic stuff. In some cases, I think they're ones where we'll need more roadmapping and more bringing people together to really workshop the idea, to really have people that are more expert than me say, critique each other and see what's. Really going on in the fields. and I also rely on a lot of outside experts. if I have someone comes with an idea, you know, for, for energy, you know, and I'm talking to people that are like former RPE program managers or things like that, that, that know more of the questions. so I think we can, we can, we can do a certain amount of, of due diligence on ideas and. and then there are some that are, that are really far out. you know, we both have an interest in atomically precise manufacturing, and that that's when, where we don't know the path I think, forward. and so that's maybe a pre fro that's something where you [00:54:00] need a roadmapping approach, but it's maybe not quite ready to, to just immediately do an fro. Yeah, no, that's, you sort of hit on a really interesting point, which is that. when we think of moonshots, it's generally like this big, exciting thing, but perhaps some of the most valuable is will actually sound incredibly boring, but the things that they'll unlock will be. Extremely exciting. yeah, I think that's true. And, and you have to distinguish there's there's boring. Right? So, so I think there's, there's some decoupling of exactly how much innovation is required and exactly how important something is. And also just how much brute force is required. So I think in general, our system might under weight, the importance of brute force. And somewhat overweight the importance of sort of creative, individual breakthrough thinking. at the same time, there are problems where I think we are bottlenecked by thinking I'm like really how to do something, not just to [00:55:00] connect them of brain, but how do you actually do activity map of entire brain? You actually need to get a bunch of physicists together and stuff to really figure out what's, you know, there's a level of thinking that is not very non-obvious similarly for like truly next gen fabrication. You really, really, really need to do the technology roadmapping approach. And that's a little different than the fro. And in some cases there may be a, as we discussed, I think in the past, there was sort of a, a continuum potentially between DARPA type programs or programs that would start within the existing systems and try to catalyze the emergence of ideas and discoveries. And then fro is, which are a bit, a bit more cut and dry. And in some cases, even you could think of it as boring. but just very important. how do we prevent Faros from becoming a political football? because you see this all the time where, you know, a Senator will say, well, like I'll sponsor this bill, as long as we mandate that. 50% of the work has to happen in my particular state or [00:56:00] district. and, and I imagine that that would be counterproductive towards the goals of . so do you, do you have any sense of like how to, how to get around that probably much easier in philanthropic setting than governments? Although I think I'm overall, I'm, I'm sort of optimistic that, if. If the goals are made very clear, the goal is disruptive, you know, multiplicative improvements in scientific fields. that's the primary goal. It needs to be managed well. so it's not either about the individual peoples, if you want academic politics and also that it doesn't, doesn't become about sort of, you know, districts, congressional districts, or all sorts of other things. I think there's a certain amount of complexity, but the other, the other thing is. I think there's really amazing things to be done in all sorts of places and by all sorts of people that are not necessarily identified as, as the biggest egos or the largest cities also, although certainly there are hubs that [00:57:00] matter. yeah. Cool. I think so. I think those are all like the actual questions I have. Is there anything you want to talk about that we have not touched on? Yeah, that's a good question. I mean, how does this fit into two things that you're thinking about, in terms of your overall analysis of the research system, then, do you think this, what is this leave unsolved as well? if, even if we can get some big philanthropic and government, donors. Yeah. So, so there are sort of two things that I. see it not covering. And so the, the first that you you've sort of touched on is that there are, some problems that still like don't fit into academia, but are not quite at the point where they're ready to be at fro. And so, they need, the, the like mindset of the fro without. Having this sort of, cut and dryness [00:58:00] that you need to sort of plunk down, like have the confidence to plunk down $50 million. so, so we need sort of a, a, what I would see as a sustainable, way of. Getting to the point of fro type projects. And as you know, I'm spending a lot of time with that. and then sort of a, the other thing that I've realized is that when, when people, we sort of have these discussions that are like research is broken, I think what we're actually talking about is, is sort of two really separate phenomenon. So, what we've been talking about, like Efros, Are really sort of sitting in like the Valley of death where it's like helping bridge that. but I think that at the same time, there there's like what I would call like the, the Einstein wouldn't get in any funding problem, which is, as you alluded to there, there are some of these things, like some of the [00:59:00] problems with research that we talk about are just about, The sort of conformity and specialization of really idea based exploratory, like completely uncertain research. And that's also really important, but I, I think it's what we don't do is, is, is sort of like separate those two things out and say like, these are both fall under the category of research, but are in fact. Extremely different processes. They require very different solutions. Yeah. Actually let me, let me, since you mentioned that, and since we are here together on the podcast, I agree with that and I, I have some things to say about that as well. So, so I think that the fro is indeed only address, or are designed to address this issue of sort of system building. problems that have a sort of catalytic nature and are a particular kind of pre-commercial stage. Right? So in some ways, [01:00:00] even though I'm so excited about borrows and how much they can unlock, because I think that this is one of two or three categories that has been, you know, under emphasized by current systems or has systems currently have struggled with it. there are these others. So, so I think that. The, the supporting the next Einstein and people that may have also have just be cognitively socially in any other number of ways, just different and weird and not good at writing grants. You know, not good at competing. Maybe not even good at graduating undergrad. Yeah. You know, I'm running a lab who are, are brilliant and because the system now. Has proliferate in terms of the number of scientists. it's very competitive and, and there is a, there's a lot of need to sort of filter people based on credentials. So there's this sort of credential there's people that don't fit with perfectly with credentials or with a sort of monoculture of who is able to get NSF grants and go through the university system and [01:01:00] get the PhD and all those different Alexey goosey has this nice blog post is oriented toward biomedical, but saying basically that in order to get through the system, you need to do 10 or 15 things simultaneously. Well, and also be lucky. And maybe we want to be looking for some people that are only able to do three of those things about, but are orders of magnitude better than others, then there's people even who have done well with those things, but still don't have the funding or sort of sustained ability, to, to pursue their own individual ideas over decades. even if they do get tenure or something, because the grant system is based on peer review and is, is sort of filtering out really new ideas, for whatever reason, There's kind of the broader issue that Michael Nielsen has talked about, which is sort of the idea that too much funding is centralized in a single organizational model. So particularly the NIH, the NIH grant is kind of hegemonic as, as, as a structure and as a peer review mechanism. then I think we need more [01:02:00] DARPA stuff. We probably need more darker agencies for other problems. Even though I've, I've sort of said that I think Rose can solve some problems that DARPA DARPA will struggle with. Likewise, DARPA walls solve problems that fro may struggle with. particularly if there's a very widely distributed expertise across the world that you need to bring together in a, some transient, interesting way, for a little bit more discovery oriented, perhaps in Faros and less deliverable oriented or team oriented. And then there's even bigger things we need, you know, like we need to be able to create, you know, a bell labs for energy, you know, or sort of something even bigger than fro. so yeah, I think the thing that you're, you're getting at that I is, is sort of simple, but under done is actually analyzing like what the activity is and what. How to best support it. Yep. Which is instead of just saying [01:03:00] like, ah, there's some research let's give some money to the research and then magical things will happen actually saying like, okay, like, like how does this work? Like what, and then what can we do for these, these specific situation? Yes. I think as you've identified. Like there's both on the one hand, there's the tendency to micromanage research and say, research has to do this, this with this equipment and this timescale it's entirely, this is sort of subject to milestone. And on the other hand is research is this magical thing. We have no idea. but just. Let other scientists, peer review each other, and just sort of give as much money to it as we can. and then we see what happens. Right. And I think neither of those, is a, is a good design philosophy, right? Yeah. Yeah. And I think it involves people like thinking it's it's uncomfortable, but like, like thinking and learning about. How, how did you think then understanding how it could, how it could be different? [01:04:00] How it's not a it's it's a system. Kevin has felt set, said it said it well. And so in some ways it's been designed, but really our scientific systems are something that has evolved into large degree. No one has designed it. It's not. Something that's designed to be optimal is it's a, it's a emergent property of many different people's incentives. And, if we actually try to apply more design thinking, I think, I think that can be good as long as we're not over overconfident in saying that there's one model for everyone. Yeah. I think that the trick to, sort of fixing. Emergent systems is to like, basically like do little experiments, poking at them. And that's, that's very much what I see getting fro is going okay. It's like, you're not saying, Oh, we should like dismantle the NSF and have it all be . Okay. Let's do a couple of these. See what happens. That's right. It's I think it's inherently a small perturbation and it it's. And I [01:05:00] think DARPA, by the way is a similar thing. It's sort of dark. You wouldn't need DARPA. If everything else was already sort of efficient, right. Given that things are not perfectly efficient, Darko has all these, all these sort of this niche that it fills. I think similarly Faros, they can only exist. if you also have a huge university system and you also have companies that that doesn't make sense, otherwise it's, it's a perturbation, but as we, I think it's a perturbation in which you unlock a pretty big pressure stream sort of behind it when you open it up. So. Excellent. Well, I think that's, that's actually a great place to close. I guess the last question would be, Like, if people are interested in, in Faros, especially like funding or running one, what is the best way for them to reach you? Well, they can, they can talk to me or they can talk to you. my email has, is prominently listed on my website. Twitter is great. and that, yeah, I really interested in, people that have a kind of specificity [01:06:00] of, of, of what they want of, you know, here here's, here's what I would do, very specifically, but I'm also interested in talking to people that, See problems with the current systems and want to do something and want to learn about, other highly specific fro ideas that others might have, and how to enable those.

FlyWire: Online community for whole-brain connectomics

Play Episode Listen Later Aug 30, 2020

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.08.30.274225v1?rss=1 Authors: Dorkenwald, S., McKellar, C., Macrina, T., Kemnitz, N., Lee, K., Lu, R., Wu, J., Popovych, S., Mitchell, E., Nehoran, B., Jia, Z., Bae, J. A., Mu, S., Ih, D., Castro, M., Ogedengbe, O., Halageri, A., Ashwood, Z., Zung, J., Brittain, D., Collman, F., Schneider-Mizell, C., Jordan, C., Silversmith, W., Baker, C., Deutsch, D. M., Encarnacion-Rivera, L., Kumar, S., Burke, A., Gager, J., Hebditch, J., Koolman, S., Moore, M., Morejohn, S., Silverman, B., Willie, K., Willie, R., Yu, S.-c., Murthy, M., Seung, H. S. Abstract: Due to advances in automated image acquisition and analysis, new whole-brain connectomes beyond C. elegans are finally on the horizon. Proofreading of whole-brain automated reconstructions will require many person-years of effort, due to the huge volumes of data involved. Here we present FlyWire, an online community for proofreading neural circuits in a fly brain, and explain how its computational and social structures are organized to scale up to whole-brain connectomics. Browser-based 3D interactive segmentation by collaborative editing of a spatially chunked supervoxel graph makes it possible to distribute proofreading to individuals located virtually anywhere in the world. Information in the edit history is programmatically accessible for a variety of uses such as estimating proofreading accuracy or building incentive systems. An open community accelerates proofreading by recruiting more participants, and accelerates scientific discovery by requiring information sharing. We demonstrate how FlyWire enables circuit analysis by reconstructing and analysing the connectome of mechanosensory neurons. Copy rights belong to original authors. Visit the link for more info

Impact of Concatenating fMRI Data on Reliability for Functional Connectomics

Play Episode Listen Later May 8, 2020

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.05.06.081679v1?rss=1 Authors: Cho, J. W., Korchmaros, A., Vogelstein, J. T., Milham, M., Xu, T. Abstract: Compelling evidence suggests the need for more data per individual to reliably map the functional organization of the human connectome. As the notion that "more data is better" emerges as a golden rule for functional connectomics, researchers find themselves grappling with the challenges of how to obtain the desired amounts of data per participant in a practical manner, particularly for retrospective data aggregation. Increasingly, the aggregation of data across all fMRI scans available for an individual is being viewed as a solution, regardless of scan condition (e.g., rest, task, movie). A number of open questions exist regarding the aggregation process and the impact of different decisions on the reliability of resultant aggregate data. We leveraged the availability of highly sampled test-retest datasets to systematically examine the impact of data aggregation strategies on the reliability of whole-brain functional connectomics. Specifically, we compared functional connectivity estimates derived after concatenating from: 1) multiple scans under the same state, 2) multiple scans under different states (i.e. hybrid or general functional connectivity), and 3) subsets of one long scan. We also varied connectivity processing (i.e. global signal regression, ICA-FIX, and task regression) and estimation procedures. When the total number of time points is equal, and the scan state held constant, concatenating multiple shorter scans had a clear advantage over a single long scan. However, this was not necessarily true when concatenating across different fMRI states (i.e. task conditions), where the reliability from the aggregate data varied across states. Concatenating fewer numbers of states that are more reliable tends to yield higher reliability. Our findings provide an overview of multiple dependencies of data concatenation that should be considered to optimize reliability in analysis of functional connectivity data. Copy rights belong to original authors. Visit the link for more info

data copy functional reliability fmri xu biorxiv milham connectomics

46: Connectomics with Dr. Moritz Helmstaedter

head moritz max planck institute scientific director brain research frankfurt germany connectomics

Play Episode Listen Later Nov 15, 2019 40:25

What approaches are researchers taking to understand which neurons talk to each other in the brain? Joe and Misha sit down with Dr. Moritz Helmstaedter, Scientific Director and Head of the Department of Connectomics at the Max Planck Institute for Brain Research in Frankfurt Germany. They'll discuss the current state of connectomics, the approaches researchers are taking, the challenges of working with the large datasets necessary to understand neuronal connections, and the work that Dr. Helmstaedter's group is doing with electron microscopy to map the connections within the brain.

Exercising at Night Won’t Mess with Your Sleep, Tonka Beans, and Non-Genome -Omes

Curiosity Daily

Play Episode Listen Later Jan 10, 2019 7:30

Learn about the strange history of the word “genome” and other -omes; where you can taste the legendary tonka bean, and why it’s illegal in some places; and new research that says exercising at night won’t mess with your sleep. In this podcast, Cody Gough and Ashley Hamer discuss the following stories from Curiosity.com to help you get smarter and learn something new in just a few minutes: You've Heard of Your Genome — Now Meet Your Other -Omes — https://curiosity.im/2LCVogA Neuroprosthetics and the Future of Artificial Touch — https://curiosity.im/2sei8e9 (Curiosity Podcast Episode) The Tonka Bean Is Revered For Its Superb Flavor, But It's Illegal And Might Kill You — https://curiosity.im/2rK5QKj New Research Says Exercising at Night Won't Mess With Your Sleep — https://curiosity.im/2LQtPAN If you love our show and you're interested in hearing full-length interviews, then please consider supporting us on Patreon. You'll get exclusive episodes and access to our archives as soon as you become a Patron! https://www.patreon.com/curiositydotcom Download the FREE 5-star Curiosity app for Android and iOS at https://curiosity.im/podcast-app. And Amazon smart speaker users: you can listen to our podcast as part of your Amazon Alexa Flash Briefing — just click “enable” here: https://curiosity.im/podcast-flash-briefing.

science future sleep exercise android mess ios curiosity terms scientific beans exercising culinary desserts genomics terminology genome michelin stars etymology tonka alinea omics worki connectome connectomics ashley hamer cody gough omes amazon alexa flash briefing

Episode 4: "Optical Methods for Molecular Connectomics"

Carboncopies Podcast

Play Episode Listen Later Oct 3, 2018 37:58

Episode 4 of the Carboncopies Podcast features a presentation by Dr. Adam Marblestone, hosted by Allen Sulzen.

methods molecular optical connectomics

#5: The Hedgehog and the Fox - Connectomics, Part 2 with Dr. Tony Movshon

Play Episode Listen Later May 26, 2016 49:33

What can we learn from studying the vast complexity of wiring in the brain? In this week's episode, Professor Movshon explains why a functional perspective is lacking in a connectomics approach that limits our ability to understand what the brain is doing. We'll also hear about how a diverse set of scientific interests can make for a fruitful and enjoyable career in research.

hedgehog connectomics

#2: Connectomics - Unraveling a Society of Cells with Dr. Jeff Lichtman

society humans harvard university neuroscience unraveling cells lichtman connectomics

Play Episode Listen Later Mar 25, 2016 31:09

Humans have 10 times more neurons in our brain than there are people on the earth, so how do these billions of cells manage to all work together? Misha, Ben, and Joe sit down with Dr. Jeff Lichtman, professor of Neuroscience at Harvard University, to talk about his work understanding the complexity of how neurons connect with one another. We discuss recent technological breakthroughs and shortcomings in traditional microscopy, and whether we can transfer our consciousness onto computers.

Brain Gains on the Road to Mapping a Personal Connectome

Artificial Intelligence in Industry with Daniel Faggella

Play Episode Listen Later Feb 7, 2016 31:37

It goes without saying that the brain is difficult to understand, with the billions of neurons, fine individual synapses between each neuron, and the different regions responsible for the innumerable behaviors exhibited by human beings. A new burgeoning and promising intermediary field called Connectomics is making waves in mapping the brain and figuring out how these various connections work together to make us sentient. In this episode with Dr. Olaf Sporns, who is in part credited with coming up with the term Connectomics, we explore the progress that's been made in this new field in the past decade, and take a tentative but hopeful look ahead at what the next decade might bring as the field progresses into its adolescence.

personal mapping connectome connectomics brain gains

“Crowdsourcing a Neuroscience Revolution” — Podcast 10: Sebastian Seung

Podcast – The Connectome

Play Episode Listen Later Jul 2, 2015

Ben chats with Sebastian Seung, a neuroscience researcher whose latest work — in cooperation with teams at MIT, at Germany’s Max Planck Institute and at other cutting-edge institutions — is proving that an improbable-sounding dream isn’t so improbable after all: We may be able to map the structure and function of every neural connection in an entire mammalian nervous system, from the cellular level up… and it may happen within our lifetimes. Seung’s bestselling book Connectome offers an exciting tour through this fast-growing field of connectomics — and in fact, it was his TEDTalk, “I Am My Connectome,” that sparked …

science germany brain psychology mind mit ted talks neuroscience neurobiology crowdsourcing neurons max planck institute seung revolution podcast neuroanatomy connectome connectomics

Precision Medicine, Connectomics, and Addiction

Harm Reduction Radio - HAMS

Play Episode Listen Later Jan 29, 2015 44:00

Our guest is Sean X. Luo, M. D., Ph. D. of Columbia University who will be talking about his research on precision medicine, connectomics, and addiction and how they relate to risk and reward.

risk addiction columbia university reward harm reduction precision medicine luo connectomics

Connectomics (Jan. 18, 2013)

TEDxCaltech 2013

Play Episode Listen Later Apr 15, 2013

Connectomics (Jan. 18, 2013)

TEDxCaltech 2013 - HD

Play Episode Listen Later Apr 15, 2013 17:57

Connectomics (Jan. 18, 2013)

TEDxCaltech 2013 - SD

Play Episode Listen Later Apr 15, 2013 17:57

schools and tech: episode #33

Schools and Tech

Play Episode Listen Later Jan 13, 2011

SaTP33.mp3 Listen on Posterous 1) Rethinking Advanced Placement NYTimes - KB“Next month, the board, the nonprofit organization that owns the A.P. exams as well as the SAT, will release a wholesale revamping of A.P. biology as well as United States history — with 387,000 test-takers the most popular A.P. subject. A preview of the changes shows that the board will slash the amount of material students need to know for the tests and provide, for the first time, a curriculum framework for what courses should look like. The goal is to clear students’ minds to focus on bigger concepts and stimulate more analytic thinking. In biology, a host of more creative, hands-on experiments are intended to help students think more like scientists.”2) PBS Newshour - “Is Technology Wiring Teens to Have Better Brains?” - (10 min video) http://www.pbs.org/newshour/bb/science/jan-june11/digitalbrain_01-05.html 3) KhanApp Offers Free Education To Go Education on-the-go is now easier thanks to Khan Academy's mobile application. KhanApp is a mobile webapp that offers a full-fledged application experience around your favorite Khan Academy videos.4) Virginia Poised to Ban Teacher-Student Texting, Facebooking - (ReadWriteWeb) Such is the case with a set of guidelines, set to be voted on this week by the Virginia Board of Education, that will establish the state's policy for how students and teachers can interact via text-messaging, social networking, and online gaming. In a nutshell: they can't. 5) Connectomics from NYT’s “In Pursuit of a Mind Map, Slice by Slice” - mapping memory and personality in the brain; “The connectome is a product of your genes and your experiences. It’s where nature meets nurture.” The project’s actual website: http://www.humanconnectomeproject.org/ Image from Flickr This project is presently working to achieve the following goals: 1) develop sophisticated tools to process high-angular diffusion (HARDI) and diffusion spectrum imaging (DSI) from normal individuals to provide the foundation for the detailed mapping of the human connectome; 2) optimize advanced high-field imaging technologies and neurocognitive tests to map the human connectome; 3) collect connectomic, behavioral, and genotype data using optimized methods in a representative sample of normal subjects; 4) design and deploy a robust, web-based informatics infrastructure, 5) develop and disseminate data acquisition and analysis, educational, and training outreach materials. Main Topic: A Quaker Education in a High-Tech Era with Guybe Slangen, Assistant Head of San Francisco Friends School - a K–8 co-educational independent school that combines outstanding academics with Quaker values of simplicity, integrity, mutual respect, and peaceful problem-solving.Endorsements: Cammy: http://www.teachparentstech.org/ Kevin: backpacks! I like the Kata DR 467i Tim: How to Audit and Update Your Passwords Researcher Develops Password Hacking Software for Wi-Fi Networks Using Amazon Web Services Permalink | Leave a comment »

Connectomics

Videocast Podcasts

Play Episode Listen Later Mar 18, 2008 64:25

Enhanced Audio PodcastAired date: 3/5/2008 3:00:00 PM Eastern Time

Connectomics

Videocast Podcasts

Play Episode Listen Later Mar 18, 2008 64:25

Enhanced Video PodcastAired date: 3/5/2008 3:00:00 PM Eastern Time