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Are you interested in the changing governmental roles in urban transformations? Summary of the article titled Rethinking the governance of urban infrastructural transformations: a synthesis of emerging approaches from 2022 by Jochen Monstadt, Jonas Colen Ladeia Torrens, Mansi Jain, Rachel M Macrorie, and Shaun R Smith, published in the Environmental Sustainability journal. This is a great preparation to our next interview with Erick A. Brimen in episode 294 talking about a new form of governance from their experiments. Since we are investigating the future of cities, I thought it would be interesting to see how governance changes in our ever-changing world answering current and future challenges. This article synthesizes emerging approaches to the governance of transformative infrastructural change, revealing their underlying logics and potential contributions. You can find the article through this link. Abstract: Recent urban debates on the governance of sustainability transformations have witnessed an 'infrastructural turn'. Previously blacked-boxed, the role of infrastructures in sustainability transformations has been foregrounded by both growing academic scholarship and major investments in new infrastructural programs. How these changes are, and could be, governed remains somewhat opaque however, with traditional forms of knowledge and practices in need of urgent revision. To nuance public and academic debates, this paper synthesizes emerging approaches to the governance of transformative infrastructural change, revealing their underlying logics and potential contributions. These include appraisal of; alternative infrastructural pathways via ‘futuring', their enactment via experimentation processes, supported by cross-domain coordination and new assessment methods. Such approaches may open new directions toward urban sustainability but also surface tensions and contradictions inherent to the governance of infrastructures. Connecting episodes you might be interested in: No.002R - Intelligent Cities No.064R - The Network State No.280 - Interview with Hudson Worsley about nature as urban infrastructure You can find the transcript through ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠th⁠i⁠s link⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. What wast the most interesting part for you? What questions did arise for you? Let me know on Twitter ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@WTF4Cities⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ or on the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠wtf4cities.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ website where the⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠showno⁠t⁠es⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠are also available. I hope this was an interesting episode for you and thanks for tuning in. Music by ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Lesfm ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠from ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Pixabay⁠

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.27.550829v1?rss=1 Authors: Rovira, M., Ferrero, G., Miserocchi, M., Montanari, A., Wittamer, V. Abstract: Recent studies have highlighted the heterogeneity of the immune cell compartment within the steady-state murine and human CNS. However it is not known whether this diversity is conserved among non mammalian vertebrates, especially in the zebrafish, a model system with increasing translational value. Here, we reveal the complexity of the immune landscape of the adult zebrafish brain. Using single-cell transcriptomics, we characterized these different immune cell subpopulations, including cell types that have not been -or have been poorly- characterized in zebrafish so far. By histology, we found that, despite microglia being the main immune cell type in the parenchyma, the zebrafish brain is also populated by a distinct myeloid population that shares a gene signature with mammalian dendritic cells (DC). Notably, zebrafish DC-like cells rely on batf3, a gene essential for the development of conventional DC1 in the mouse. Using specific fluorescent reporter lines that allowed us to reliably discriminate DC-like cells from microglia, we quantified brain myeloid cell defects in commonly used irf8-/-, csf1ra-/- and csf1rb-/- mutant fish, revealing previously unappreciated distinct microglia and DC-like phenotypes. Overall, our results suggest a conserved heterogeneity of brain immune cells across vertebrate evolution and also highlights zebrafish-specific brain immunity characteristics. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.23.550200v1?rss=1 Authors: Avila-Varela, D. S., Hidalgo-Lopez, E., Dagnino, P. C., Acero-Pousa, I., del Agua, E., Deco, G., Pletzer, B., Escrichs, A. Abstract: Recent neuroimaging research suggests that female sex hormone fluctuations modulate brain activity. Nevertheless, how brain network dynamics change across the female menstrual cycle remains largely unknown. Here, we investigated the dynamical complexity underlying three menstrual cycle phases (i.e., early follicular, pre-ovulatory, and mid-luteal) in 60 healthy naturally-cycling women scanned using resting-state fMRI. Our results revealed that the pre-ovulatory phase exhibited the highest variability over time (node-metastability) across the whole-brain functional network compared to the early follicular and mid-luteal phases, while the early follicular showed the lowest. Additionally, we found that large-scale resting-state networks reconfigure along the menstrual cycle phases. Finally, we used multilevel mixed-effects models to examine the impact of hormonal fluctuations and age on whole-brain and resting-state networks. We found significant age-related changes across the whole brain, control, and dorsolateral attention networks. Additionally, we observed progesterone-related changes, specifically within limbic and somatomotor networks. Overall, these findings evidence that both age and progesterone modulate brain network dynamics along the menstrual cycle. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.16.549254v1?rss=1 Authors: Yang, C., He, X., Cai, Y. Abstract: Recent studies have proposed that visual information can be maintained in an activity-silent state during working memory (WM) and that this activity-silent WM can be reactivated by task-irrelevant high-contrast visual impulses (i.e., "pinging the brain"). Although pinging the brain has become a popular tool for exploring activity-silent WM in recent years, its underlying mechanisms remain unclear. In the current study, we directly compared the neural reactivation effects and behavioral consequences of context-independent and context-dependent pings to distinguish between the noise-reduction and target-interaction hypotheses of pinging the brain. In this electroencephalogram study, our neural decoding results showed that the context-independent pings reactivated activity-silent WM without changing the original representations of memorized items and that reactivation effects were significantly higher in individuals with poorer WM performance. In contrast, the context-dependent pings reactivated activity-silent WM in a more durable and consistent way and further reorganized it by decreasing the variability of items' neural representations and disturbing the memory structure between items. Notably, reactivation effects were stronger in the trials with larger recall errors. In an additional behavioral study, we optimized our experimental design to minimize expectation and adaptation effects and found that, compared with the baseline condition (no ping), context-dependent pings impaired recall performance, while context-independent pings did not. Together, our results provided clear evidence for two distinct mechanisms underlying pinging the brain, and the ping's context played a critical role in reactivating and reorganizing activity-silent WM. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.14.548994v1?rss=1 Authors: Mai, A., Hillyard, S. A., Strauss, D. J. Abstract: Recent work in the field of neural speech tracking provided evidence for a cortical representation of speech through superposition of event-related responses to acoustic edges, an idea closely related to the popular linear modeling approach to study cortical synchronization to speech via magneto- or electroencephalography (M/EEG). However, it is still unclear to what extent speech-evoked event-related potentials (ERPs) including well-established phenomena, e.g., the N1 selective attention effect, contribute to the regression-based analyses. Here, we addressed this question by analyzing an EEG dataset obtained during a simple multispeaker selective attention task in which participants were cued to attend to only one of two competing speakers. Segmenting the ongoing EEG based on acoustic edges, we were able to replicate previous findings of event-related responses to speech in MEG data with particularly clear P1-N1-P2 complexes. Crucially, speech-evoked ERPs exhibited significant effects of attention in line with the auditory N1 effect. Comparing speech-evoked ERPs to the linear regression results revealed two major findings. First, temporal response functions (TRFs) obtained from forward modeling were strongly temporally as well as spatially correlated with corresponding true ERPs. Second, effects of attention demonstrated by the stimulus reconstruction (SR) accuracies obtained from backward modeling appeared to be driven by a consistent generation of speech-evoked ERPs including the N1 effect. Taken together, our observations reveal a direct link between ERPs to acoustic edges in speech and the linear TRF and SR modeling techniques. We emphasize the enhancement in signal-to-noise ratio provided by repeatedly evoked N1 responses to be a critical factor in facilitating the tracking and subsequent higher-order processing of selectively attended speech. In addition to that, the findings imply a cortical speech representation through superimposed speech-evoked ERPs in accordance with recent arguments promoting the neural evoked-response speech tracking model. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.13.548931v1?rss=1 Authors: Hobson, R., Levy, S. H. S., Flaherty, D., Xiao, H., Ciener, B., Reddy, H., Singal, C., Teich, A. F., Shneider, N. A., Bradshaw, E. M., Elyaman, W. Abstract: Recent murine studies have highlighted a crucial role for the meninges in surveilling the central nervous system (CNS) and influencing CNS inflammation. However, how meningeal immunity is altered in human neurodegeneration and its potential effects on neuroinflammation is understudied. In the present study, we performed single-cell analysis of the transcriptomes and T cell receptor repertoire of 72,576 immune cells from 36 postmortem human brain and leptomeninges tissues from donors with neurodegenerative diseases including amyotrophic lateral sclerosis, Alzheimers disease, and Parkinsons disease. We identified the meninges as an important site of antigen presentation and CD8 T cell activation and clonal expansion and found that T cell activation in the meninges is a requirement for infiltration into the CNS. We further found that natural killer cells have the potential to negatively regulate T cell activation locally in the meninges through direct killing and are one of many regulatory mechanisms that work to control excessive neuroinflammation. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.12.548751v1?rss=1 Authors: Lurie, D. J., Pappas, I., D'Esposito, M. T. Abstract: Recent years have seen growing interest in characterizing the properties of regional brain dynamics and their relationship to other features of brain structure and function. In particular, multiple studies have observed regional differences in the "timescale" over which activity fluctuates during periods of quiet rest. In the cerebral cortex, these timescales have been associated with both local circuit properties as well as patterns of inter-regional connectivity, including the extent to which each region exhibits widespread connectivity to other brain areas. In the current study, we build on prior observations of an association between connectivity and dynamics in the cerebral cortex by investigating the relationship between BOLD fMRI timescales and the modular organization of structural and functional brain networks. We characterize network community structure across multiple scales and find that longer timescales are associated with greater within-community functional connectivity and diverse structural connectivity. We also replicate prior observations of a positive correlation between timescales and structural connectivity degree. Finally, we find evidence for preferential functional connectivity between cortical areas with similar timescales. We replicate these findings in an independent dataset. These results contribute to our understanding of functional brain organization and structure function relationships in the human brain, and support the notion that regional differences in cortical dynamics may in part reflect the topological role of each region within macroscale brain networks. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.06.29.546558v1?rss=1 Authors: Maboudian, S. A., Willbrand, E. H., Jagust, W. J., Weiner, K. S. Abstract: Recent work suggests that indentations of the cerebral cortex, or sulci, may be uniquely vulnerable to atrophy in aging and Alzheimer's disease (AD) and that posteromedial cortex (PMC) is particularly vulnerable to atrophy and pathology accumulation. However, these studies did not consider small, shallow, and variable tertiary sulci that are located in association cortices and are often associated with human-specific aspects of cognition. Here, we first manually defined 4,362 PMC sulci in 432 hemispheres in 216 participants. Tertiary sulci showed more age- and AD-related thinning than non-tertiary sulci, with the strongest effects for two newly uncovered tertiary sulci. A model-based approach relating sulcal morphology to cognition identified that a subset of these sulci were most associated with memory and executive function scores in older adults. These findings support the retrogenesis hypothesis linking brain development and aging, and provide new neuroanatomical targets for future studies of aging and AD. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

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: Language Agents Reduce the Risk of Existential Catastrophe, published by Cameron Domenico Kirk-Giannini on May 28, 2023 on The AI Alignment Forum. This post was written by Simon Goldstein, associate professor at the Dianoia Institute of Philosophy at ACU, and Cameron Domenico Kirk-Giannini, assistant professor at Rutgers University, for submission to the Open Philanthropy AI Worldviews Contest. Both authors are currently Philosophy Fellows at the Center for AI Safety. Abstract: Recent advances in natural language processing have given rise to a new kind of AI architecture: the language agent. By repeatedly calling an LLM to perform a variety of cognitive tasks, language agents are able to function autonomously to pursue goals specified in natural language and stored in a human-readable format. Because of their architecture, language agents exhibit behavior that is predictable according to the laws of folk psychology: they have desires and beliefs, and then make and update plans to pursue their desires given their beliefs. We argue that the rise of language agents significantly reduces the probability of an existential catastrophe due to loss of control over an AGI. This is because the probability of such an existential catastrophe is proportional to the difficulty of aligning AGI systems, and language agents significantly reduce that difficulty. In particular, language agents help to resolve three important issues related to aligning AIs: reward misspecification, goal misgeneralization, and uninterpretability. 1. Misalignment and Existential Catastrophe There is a significant chance that artificial general intelligence will be developed in the not-so-distant future — by 2070, for example. How likely is it that the advent of AGI will lead to an existential catastrophe for humanity? Here it is worth distinguishing between two possibilities: an existential catastrophe could result from humans losing control over an AGI system (call this a misalignment catastrophe), or an existential catastrophe could result from humans using an AGI system deliberately to bring that catastrophe about (call this a malicious actor catastrophe). In what follows, we are interested in assessing the probability of a misalignment catastrophe rather than a malicious actor catastrophe. Carlsmith (2021) helpfully structures discussion of the probability of a misalignment catastrophe around six propositions. Since we are interested in the probability of a misalignment catastrophe conditional on the development of AGI, we focus our attention on the final four of these propositions, which we summarize as follows: 1. Of the following two options, the first will be much more difficult: a. Build AGI systems with an acceptably low probability of engaging in power-seeking behavior. b. Build AGI systems that perform similarly but do not have an acceptably low probability of engaging in power-seeking behavior. 2. Some AGI systems will be exposed to inputs which cause them to engage in power-seeking behavior. 3. This power-seeking will scale to the point of permanently disempowering humanity. 4. This disempowerment will constitute an existential catastrophe. Carlsmith assigns a probability of .4 to (1) conditional on the rise of AGI, a probability of .65 to (2) conditional on (1) and the rise of AGI, a probability of .4 to (3) conditional on (1), (2), and the rise of AGI, and a probability of .95 to (4) conditional on (1-3) and the rise of AGI. This translates into a probability of approximately .1 (10%) for a misalignment catastrophe conditional on the rise of AGI. We believe that the development of language agents ought to significantly decrease assessments of these probabilities. In particular, we suggest that the development of language agents reduces the probability of (1) conditional on the rise of AGI...

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.05.05.539592v1?rss=1 Authors: Chapp, A. D., Nwakama, C. A., Mermelstein, P. G., Thomas, M. J. Abstract: Recent studies have implicated the ethanol metabolite, acetic acid, as neuroactive, perhaps even more so than ethanol itself. In this study, we investigated sex-specific metabolism of ethanol (1, 2, and 4g/kg) to acetic acid in vivo to guide electrophysiology experiments in the accumbens shell (NAcSh), a key node in the mammalian reward circuit. There was a sex-dependent difference in serum acetate production, quantified via ion chromatography only at the lowest dose of ethanol (males greater than females). Ex vivo electrophysiology recordings of NAcSh neurons in brain slices demonstrated that physiological concentrations of acetic acid (2 mM and 4 mM) increased NAcSh neuronal excitability in both sexes. N-methyl-D-aspartate receptor (NMDAR) antagonists, AP5, and memantine robustly attenuated the acetic acid-induced increase in excitability. Acetic acid-induced NMDAR-dependent inward currents were greater in females compared to males. These findings suggest a novel NMDAR-dependent mechanism by which the ethanol metabolite, acetic acid, may influence neurophysiological effects in a key reward circuit in the brain. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.04.23.537915v1?rss=1 Authors: Schnepel, P., Paricio-Montesinos, R., Ezquerra-Romano, I., Haggard, P., Poulet, J. F. A. Abstract: Recent evidence suggests that primary sensory cortical regions play a role in the integration of information from multiple sensory modalities. How primary cortical neurons integrate multisensory information is unclear, partly because multisensory interactions in the cortex are typically weak or modulatory. To address this question, we take advantage of the robust representation of thermal (cooling) and tactile stimuli in mouse forepaw primary somatosensory cortex (fS1). Using a thermotactile detection task, we show that the perception of threshold level cool or tactile information is enhanced when they are presented simultaneously compared to presentation alone. To investigate the cortical correlates of thermotactile integration, we performed in vivo extracellular recordings from fS1 during unimodal and bimodal stimulation of the forepaw. Unimodal stimulation evoked thermal- or tactile-specific excitatory and inhibitory responses of fS1 neurons. The most prominent features of bimodal, thermotactile stimulation are the recruitment of unimodally silent fS1 neurons, non-linear integration features and a change in the response dynamics to favor longer response durations. Together, we identify quantitative and qualitative changes in cortical encoding that may underlie the improvement in perception of multisensory, thermotactile surfaces during haptic exploration. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.04.18.537383v1?rss=1 Authors: Wang, Y.-J., Seibert, H., Ahn, L. Y., Schaffer, A. E., Mu, T.-W. Abstract: Recent advances in genetic diagnosis identified variants in genes encoding GABAA receptors as causative for genetic epilepsy. Here, we selected eight disease-associated variants in the alpha1 subunit of GABAA receptors causing mild to severe clinical phenotypes and showed that they are loss of function, mainly by reducing the folding and surface trafficking of the alpha1 protein. Furthermore, we sought client protein-specific pharmacological chaperones to restore the function of pathogenic receptors. Applications of positive allosteric modulators, including Hispidulin and TP003, increase the functional surface expression of the alpha1 variants. Mechanism of action study demonstrated that they enhance the folding and assembly and reduce the degradation of GABAA variants without activating the unfolded protein response in HEK293T cells and human iPSC-derived neurons. Since these compounds cross the blood-brain barrier, such a pharmacological chaperoning strategy holds great promise to treat genetic epilepsy in a GABAA receptor-specific manner. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.04.17.537137v1?rss=1 Authors: Davidson, M. J., Keys, R., Szekely, B., MacNeilage, P. J., Verstraten, F., Alais, D. Abstract: Recent evidence suggests that perceptual and cognitive functions are codetermined by rhythmic bodily states. Prior investigations have focused on the cardiac and respiratory rhythms, both of which are also known to synchronise with locomotion - arguably our most common and natural of voluntary behaviours. Unlike the cardiorespiratory rhythms, walking is entirely under voluntary control, enabling a test of how natural and voluntary rhythmic action may affect sensory function. Here, we show that the speed and phase of human locomotion constrains sensorimotor performance. We used a continuous visuo-motor tracking task in a wireless, body-tracking virtual environment, and found that the accuracy and reaction time of continuous reaching movements were decreased at slower walking speeds, and rhythmically modulated according to the phases of the step-cycle. Decreased accuracy when walking at slow speeds suggests an advantage for interlimb coordination at normal walking speeds, in contrast to previous research on dual-task walking and reach-to-grasp movements. Phasic modulations of reach precision within the step-cycle also suggest that the upper limbs are affected by the ballistic demands of motor-preparation during natural locomotion. Together these results show that the natural phases of human locomotion impose constraints on sensory function and demonstrate the value of examining dynamic and natural behaviour in contrast to the traditional and static methods of psychological science. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.04.14.536983v1?rss=1 Authors: Sadibolova, R., Murray-Lawson, C., Family, N., Williams, L. T. J., Luke, D. P., Terhune, D. B. Abstract: Recent theoretical work embedded within the predictive processing framework has proposed that the neurocognitive and therapeutic effects of psychedelics are driven by the modulation of priors (Carhart-Harris & Friston, 2019). We conducted pre-registered re-analyses of previous research (Yanakieva et al., 2019) to examine whether microdoses of lysergic acid diethylamide (LSD) alleviate the temporal reproduction bias introduced by priors, as predicted by this theoretical framework. In a between-groups design, participants were randomly assigned to one of four groups receiving LSD (5, 10, or 20 g) or placebo (0 g) and completed a visual temporal reproduction task spanning subsecond to suprasecond intervals (0.8 to 4 sec). Using mixed-effects modelling, we evaluated the impact of the treatment group, and of the overall history of stimulus intervals (global priors) and the local stimulus history (local priors), weighted by their respective precision weights (inverse of variance), on temporal reproduction. Our principal finding was that the precision-weighted local priors and their precision weights reduced the under-reproduction bias observed under LSD in the original research. Furthermore, controlling for the precision-weighted local prior eliminated the reduced temporal reproduction bias under LSD, indicating that LSD microdosing mitigated the temporal under-reproduction by reducing the relative weighting of priors. These results suggest that LSD microdosing alters human time perception by decreasing the influence of local temporal priors. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.04.04.535572v1?rss=1 Authors: Wu, Y., Maass, W. Abstract: Recent experimental studies in the awake brain have identified a new rule for synaptic plasticity that appears to be instrumental for the induction of episodic and conjunctive memories in the mammalian brain: behavioral time scale synaptic plasticity (BTSP)(Bittner et al. (2015, 2017); Grienberger and Magee (2022)). BTSP differs in essential aspects from previously studied rules for synaptic plasticity. But there is so far no theory that enables a principled understanding of the impact of BTSP on the structure of the associative memory that it induces. We extract fundamental mathematical principles from experimental data on BTSP that elucidate the capacity and associative recall capabilities of memory structures that it creates. Predictions of the resulting theory are corroborated by large-scale numerical experiments. In particular, we show that BTSP can create well-separated memory traces for a very large number of memory items, even if these are not orthogonal. Furthermore, BTSP induces the repulsion effect, a well-known fingerprint of memory organization in the human brain, that could not be explained by preceding types of synaptic plasticity. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.03.20.533452v1?rss=1 Authors: Vardalaki, D., Pham, T. L. D., Frosch, M. P., Cosgrove, G. R., Richardson, M., Cash, S. S., Harnett, M. Abstract: Recent developments in super-resolution microscopy have revolutionized the study of cell biology. However, dense tissues require exogenous protein expression for single cell morphological contrast. In the nervous system, many cell types and species of interest - particularly human - are not amenable to genetic modification and/or exhibit intricate anatomical specializations which make cellular delineation challenging. Here, we present a method for full morphological labeling of individual neurons from any species or cell type for subsequent cell resolved protein analysis without genetic modification. Our method, which combines patch clamp electrophysiology with epitope-preserving magnified analysis of proteome (eMAP), further allows for correlation of physiological properties with subcellular protein expression. We applied Patch2MAP to individual spiny synapses in human cortical pyramidal neurons and demonstrated that electrophysiological AMPA-to-NMDA receptor ratios correspond tightly to respective protein expression levels. Patch2MAP thus permits combined subcellular functional, anatomical, and proteomic analyses of any cell, opening new avenues for direct molecular investigation of the human brain in health and disease. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.03.01.530616v1?rss=1 Authors: Pellegrino, A., Stein, H., Cayco-Gajic, N. A. Abstract: Recent work has argued that large-scale neural recordings are often well described by low-dimensional latent dynamics identified using dimensionality reduction. However, the view that task-relevant variability is shared across neurons misses other types of structure underlying behavior, including stereotyped neural sequences or slowly evolving latent spaces. To address this, we introduce a new framework that simultaneously accounts for variability that is shared across neurons, trials, or time. To identify and demix these covariability classes, we develop a new unsupervised dimensionality reduction method for neural data tensors called sliceTCA. In three example datasets, including motor cortical dynamics during a classic reaching task and recent multi-region recordings from the International Brain Laboratory, we show that sliceTCA can capture more task-relevant structure in neural data using fewer components than traditional methods. Overall, our theoretical framework extends the classic view of low-dimensional population activity by incorporating additional classes of latent variables capturing higher-dimensional structure. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.24.529863v1?rss=1 Authors: Ecker, A., Egas Santander, D., Bolanos Puchet, S., Isbister, J. B., Reimann, M. W. Abstract: Recent developments in experimental techniques have enabled simultaneous recordings from thousands of neurons, enabling the study of functional cell assemblies. However, determining the patterns of synaptic connectivity giving rise to assemblies remains challenging. To address this, we developed a complementary, simulation-based approach, using a detailed, large-scale cortical network model. Using a combination of established methods we detected functional cell assemblies from the stimulus-evoked spiking activity of 186,665 neurons. We studied how the structure of synaptic connectivity underlies assembly composition, quantifying the effects of thalamic innervation, recurrent connectivity, and the spatial arrangement of synapses on dendrites. We determined that these features reduce up to 30%, 22%, and 10% of the uncertainty of a neuron belonging to an assembly. The detected assemblies were activated in a stimulus-specific sequence and were grouped based on their position in the sequence. We found that the different groups were affected to different degrees by the structural features we considered. Additionally, connectivity was more predictive of assembly membership if its direction aligned with the temporal order of assembly activation, if it originated from strongly interconnected populations, and if synapses clustered on dendritic branches. In summary, reversing Hebb's postulate, we showed how cells that are wired together, fire together, quantifying how connectivity patterns interact to shape the emergence of assemblies. This includes a qualitative aspect of connectivity: not just the amount, but also the local structure matters; from the subcellular level in the form of dendritic clustering to the presence of specific network motifs. This connectivity-based characterization of cell assemblies creates an opportunity to study plasticity at the assembly level, and beyond strictly pairwise interactions. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.17.528919v1?rss=1 Authors: Yahata, N., Hirabayashi, T., Minamimoto, T. Abstract: Recent advances in genetic neuromodulation technology have enabled circuit-specific interventions in nonhuman primates (NHPs), thereby revealing the causal functions of specific neural circuits. Going forward, an important step is to use these findings to better understand neuropsychiatric and neurological disorders in humans, in which alterations in functional connectivity between brain regions are demonstrated. We recently identified the causal roles of the pathways from the dorsolateral prefrontal cortex (DLPFC) to the lateral part of the mediodorsal thalamic nucleus (MDl) and dorsal caudate nucleus (dCD) in working memory and decision-making, respectively. In the present study, we examined the resting-state functional connectivity of these two prefronto-subcortical circuits in healthy controls (HCs) and patients with various neuropsychiatric disorders including schizophrenia (SCZ), major depressive disorder (MDD), and autism spectrum disorders (ASD) in humans. We found that the functional connectivity of two pathways, DLPFC-MDl and DLPFC-dCD, was significantly reduced in the SCZ groups compared to HCs; however, this hypoconnectivity was not observed in the ASD or MDD groups, suggesting a disease-specific profile of altered prefronto-subcortical connectivity at rest. These results suggest that causal findings of pathway-specific functions revealed in NHPs can be effectively translated to identify the altered connectivity in neuropsychiatric disorders with related symptoms in humans. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.14.528557v1?rss=1 Authors: Schilling, K., Li, M., Rheault, F., Gao, Y., Cai, L. Y., Zhao, Y., Xu, L., Ding, Z., Anderson, A. W., Landman, B. A., Gore, J. C. Abstract: Recent studies have revealed the production of time-locked blood oxygenation-level dependent (BOLD) functional MRI (fMRI) signals throughout the entire brain in response to a task, challenging the idea of sparse and localized brain functions, and highlighting the pervasiveness of potential false negative fMRI findings. In these studies, "whole-brain" refers to gray matter regions only, which is the only tissue traditionally studied with fMRI. However, recent reports have also demonstrated reliable detection and analyses of BOLD signals in white matter which have been largely ignored in previous reports. Here, using model-free analysis and simple tasks, we investigate BOLD signal changes in both white and gray matters. We aimed to evaluate whether white matter also displays time-locked BOLD signals across all structural pathways in response to a stimulus. We find that both white and gray matter show time-locked activations across the whole-brain, with a majority of both tissue types showing statistically significant signal changes for all task stimuli investigated. We observed a wide range of signal responses to tasks, with different regions showing very different BOLD signal changes to the same task. Moreover, we find that each region may display different BOLD responses to different stimuli. Overall, we present compelling evidence that the whole brain, including both white and gray matter, show time-locked activation to multiple stimuli, not only challenging the idea of sparse functional localization, but also the prevailing wisdom of treating white matter BOLD signals as artefacts to be removed. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.06.527223v1?rss=1 Authors: Willbrand, E. H., Maboudian, S. A., Kelly, J. P., Parker, B. J., Foster, B. L., Weiner, K. S. Abstract: Recent studies identify a surprising coupling between evolutionarily new sulci and the functional organization of human posteromedial cortex (PMC). Yet, no study has compared this modern PMC sulcal patterning between humans and non-human hominoids. To fill this gap in knowledge, we first manually defined 918 sulci in 120 chimpanzee (Pan Troglodytes) hemispheres and 1619 sulci in 144 human hemispheres. We uncovered four new PMC sulci, and quantitatively identified species differences in incidence, depth, and surface area. Interestingly, some PMC sulci are more common in humans and others, in chimpanzees. Further, we found that the prominent marginal ramus of the cingulate sulcus differs significantly between species. Contrary to classic observations, the present results reveal that the surface anatomy of PMC substantially differs between humans and chimpanzees, findings which lay a foundation for better understanding the evolution of neuroanatomical-functional and neuroanatomical-behavioral relationships in this highly expanded region of the human cerebral cortex. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.04.527118v1?rss=1 Authors: Noonan, M. J., Greshi, N., Mills, L., de Almeida Monteiro Melo Ferraz, M. Abstract: Recent years have seen considerable scientific attention devoted towards documenting the presence of microplastics (MPs) in environmental samples. Due to omnipresence of environmental microplastics, however, disentangling environmental MPs from sample contamination is a challenge. Hence, the environmental (collection site and laboratory) microplastics contamination of samples during processing is a reality that we must address, in order to generate reproducible and reliable data. Here we investigated published literature and have found that around 1/5 of studies failed to use blank controls in their experiments. Additionally, only 34% of the studies used a controlled air environment for their samples processing (laminar flow, fume hood, closed laboratory, clean room, etc.). In that regard, we have also shown that preparing samples in the fume hood, leads to more microplastics contamination than preparing it in the laboratory bench and the laminar flow. Although it did not completely prevent microplastics contamination, the processing of sample inside the laminar flow is the best option to reduce sample contamination during processing. Overall, we showed that blank controls are a must in microplastics sample preparation, but it is often overlooked by researchers. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.04.527142v1?rss=1 Authors: Park, S. A., Zolfaghar, M., Russin, J., Miller, D. S., O'Reilly, R. C., Boorman, E. D. Abstract: Recent work has shown that abstract, non-spatial relationships between entities or task states are organized into representations called cognitive maps. Here we investigated how cognitive control enables flexible top-down selection of goal-relevant information from multidimensional cognitive maps retrieved from memory. We examined the relationship between cognitive control and representational geometry by conducting parallel analyses of fMRI data and recurrent neural network (RNN) models trained to perform the same task. We found both 2D map-like representations in a medial temporal lobe and orbitofrontal cortical network and simultaneous 1D orthogonal representations of relevant task dimensions in a frontoparietal network, supporting representational stability and flexibility, respectively. These representational motifs also emerged with distinct temporal profiles over the course of training in the RNN. We further show that increasing control demands due to incongruence (conflicting responses) between current task-relevant and irrelevant dimensions produce warping along the context-invariant axis in subjective representations, and the degree of warping further accounts for individual differences in cognitive control. Together, our findings show how complementary representational geometries balance generalization and behavioral flexibility, and reveal an intricate bidirectional relationship between cognitive control and cognitive map geometry. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

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

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.01.27.525832v1?rss=1 Authors: Abbaszadeh, M., Panjehpour, A., Alemohammad, S. M. A., Ghavampour, A., Ghazizadeh, A. Abstract: Recent evidence shows that long-term object value association can lead to efficient visual search. However, the neural mechanism of this value pop-out has yet to be understood. Given the known role of the ventrolateral prefrontal cortex (vlPFC) in visual search and value memory, we recorded its single-unit activity (n=526) in two macaque monkeys while they engaged in the value-driven search. Monkeys had to determine whether a high-value target was present within a variable number of low-value objects. Interestingly, differential neural firing, as well as gamma-band power, indicated the presence of a target within ~150ms of display onset. This differential activity was negatively correlated with search time and became less display size-dependent for more efficient searches. On the other hand, neural firing and its variability were higher in inefficient searches. These findings reveal the neural code within vlPFC for rapid detection of valuable targets, which can be crucial for animals faced with competition. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.01.27.525963v1?rss=1 Authors: Johnsen, K. A., Cruzado, N. A., Willats, A. A., Rozell, C. J. Abstract: Recent advances in neurotechnology enable exciting new experiments, including novel paradigms such as closed-loop optogenetic control that achieve powerful causal interventions. At the same time, improved computational models are capable of reproducing behavior and neural activity with increasing fidelity. However, the complexities of these advances can make it difficult to reap the benefits of bridging model and experiment, such as in-silico experiment prototyping or direct comparison of model output to experimental data. We can bridge this gap more effectively by incorporating the simulation of the experimental interface into our models, but no existing tool integrates optogenetics, electrode recording, and flexible closed-loop processing with neural population simulations. To address this need, we have developed the Closed-Loop, Electrophysiology, and Optogenetics experiment simulation testbed (Cleo). Cleo is a Python package built on the Brian 2 simulator enabling injection of recording and stimulation devices as well as closed-loop control with realistic latency into a spiking neural network model. Here we describe the design, features, and use of Cleo, including validation of the individual system components. We further demonstrate its utility in three case studies using a variety of existing models and discuss potential applications for advancing causal neuroscience. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.01.13.523934v1?rss=1 Authors: Leech, R., Vos de Wael, R., Vasa, F., Xu, T., Benn, R. A. B., Scholz, R., Braga, R., Milham, M., Royer, J., Bernhardt, B., Jones, E. J., Jefferies, E., Margulies, D. S., Smallwood, J. Abstract: Recent theories of cortical organisation maintain that important features of brain function emerge through the spatial arrangement of regions of cortex. For example, areas of association cortex are located in regions of cortex furthest from sensory and motor cortex. Association cortex is also interdigitated since adjacent regions can have relatively different patterns of functional connectivity. It is assumed that topographic properties such as distance between cortical regions constrain their functions. For example, large distances between association and sensory and motor systems may enable these areas of cortex to maintain differentiable neural patterns, while an interdigitated organisation may enable association cortex to contain many functional systems in a relatively compact space. We currently lack a formal understanding of how spatial organisation impacts brain function, limiting the ability to leverage cortical topography to facilitate better interpretations of a regions function. Here we use variograms, a quantification of spatial autocorrelation, to develop a cortex-wide profile of how functional similarity changes as a function of the distance between regions. We establish that function changes gradually within sensory and motor cortex as the distance between regions increases, while in association cortex function changes rapidly over shorter distances. Subsequent analysis suggests these differential classes of spatial dependency are related to variation in intracortical myelin between sensory motor and association cortex. Our study suggests primary and association cortex are differentiated by the degree to which function varies over space, emphasising the need to formally account for spatial properties when estimating a system's contribution to cognition and behaviour. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.12.21.521454v1?rss=1 Authors: Wierzbinski, M., Falco-Roget, J., Crimi, A. Abstract: Recent advancements in network neuroscience are pointing in the direction of considering the brain as a small-world system with segregated regions integrated to facilitate different cognitive tasks and functions. In this context, community detection is a pivotal issue in computational neuroscience. In this paper we explore community detection within brain connectomes using the power of quantum annealers, and in particular the Leap's Hybrid Solver. Our results shows that quantum annealers can achieve higher modularity index compared to classical annealer while computing communities of brain connectomes. Those promising preliminary results points out that quantum annealers might be the better choice compared to classical computing optimization process. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.12.21.521366v1?rss=1 Authors: Gajwani, M., Oldham, S. J., Pang, J. C., Arnatkeviciute, A., Tiego, J., Bellgrove, M. A., Fornito, A. Abstract: Recent years have seen a surge in the use of diffusion MRI to map connectomes in humans, paralleled by a similar increase in pre-processing and analysis choices. Yet these different steps and their effects are rarely compared systematically. Here, in a healthy young adult population (n = 294), we characterized the impact of a range of analysis pipelines on one widely studied property of the human connectome; its degree distribution. We evaluated the effects of 40 pipelines (comparing common choices of parcellation, streamline seeding, tractography algorithm, and streamline propagation constraint) and 44 group-representative connectome reconstruction schemes on highly connected hub regions. We found that hub location is highly variable between pipelines. The choice of parcellation has a major influence on hub architecture, and hub connectivity is highly correlated with regional surface area in most of the assessed pipelines ({rho} greater than 0.70 in 69% of the pipelines), particularly when using weighted networks. Overall, our results demonstrate the need for prudent decision-making when processing diffusion MRI data, and for carefully considering how different pre-processing choices can influence connectome organization. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.12.16.520838v1?rss=1 Authors: Yokoyama, H., Kitajo, K. Abstract: Recent neuroscience studies have suggested that control of the excitation and inhibition (E/I) balance is important to maintain normal brain function. However, an efficient method to evaluate the time-varying changes in E/I balance of the brain has yet to be established. To tackle this issue, we propose a new approach to estimate E/I balance changes, by applying the method of neural-mass model-based tracking of the brain state using the Ensemble Kalman Filter. In this method, the parameters regarding the synaptic E/I gains of the model are estimated from observed electroencephalography (EEG). Moreover, the index of E/I balance was defined by calculating the ratio between synaptic E/I gains based on estimated parameters. To validate this method, we applied it to numerical and human EEG data. As a result, we confirmed that our proposed method could estimate the E/I balance changes from observed human EEG. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.12.09.519772v1?rss=1 Authors: Troidl, J., Warchol, S., Choi, J., Matelsky, J., Dhanysai, N., Wang, X., Wester, B., Wei, D., Lichtman, J. W., Pfister, H., Beyer, J. Abstract: Recent advances in high-resolution connectomics provide researchers access to accurate reconstructions of vast neuronal circuits and brain networks for the first time. Neuroscientists anticipate analyzing these networks to gain a better understanding of information processing in the brain. In particular, scientists are interested in identifying specific network motifs, i.e., repeating subgraphs of the larger brain network that are believed to be neuronal building blocks. To analyze these motifs, it is crucial to review instances of a motif in the brain network and then map the graph structure to the detailed 3D reconstructions of the involved neurons and synapses. We present Vimo, an interactive visual approach to analyze neuronal motifs and motif chains in large brain networks. Experts can sketch network motifs intuitively in a visual interface and specify structural properties of the involved neurons and synapses to query large connectomics datasets. Motif instances (MIs) can be explored in high-resolution 3D renderings of the involved neurons and synapses. To reduce visual clutter and simplify the analysis of MIs, we designed a continuous focus&context metaphor inspired by continuous visual abstractions that allows the user to transition from the highly-detailed rendering of the anatomical structure to views that emphasize the underlying motif structure and synaptic connectivity. Furthermore, Vimo supports the identification of motif chains where a motif is used repeatedly to form a longer synaptic chain. We evaluate Vimo in a user study with seven domain experts and an in-depth case study on motifs in the central complex (CX) of the fruit fly brain. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.12.09.519776v1?rss=1 Authors: Quezada, A., Ward, C., Bader, E. R., Zolotavin, P., Altun, E., Hong, S., Killian, N., Xie, C., Batista-Brito, R., Hebert, J. M. Abstract: Recent progress in cortical stem cell transplantation has demonstrated its potential to repair the brain. However, current transplant models have yet to demonstrate that the circuitry of transplant-derived neurons can encode useful function to the host. This is likely due to missing cell types within the grafts, abnormal proportions of cell types, abnormal cytoarchitecture, and inefficient vascularization. Here, we devised a transplant platform for testing neocortical tissue prototypes. Dissociated mouse embryonic telencephalic cells in a liquid scaffold were transplanted into aspiration-lesioned adult mouse cortices. The donor neuronal precursors differentiated into upper and deep layer neurons that exhibited synaptic puncta, projected outside of the graft to appropriate brain areas, became electrophysiologically active within one month post-transplant, and responded to visual stimuli. Interneurons and oligodendrocytes were present at normal densities in grafts. Grafts became fully vascularized by 1-week post-transplant and vessels in grafts were perfused with blood. With this paradigm, we could also organize cells into layers. Overall, we have provided proof of concept for an in vivo platform that can be used for developing and testing neocortical-like tissue prototypes. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.12.05.519094v1?rss=1 Authors: Ding, X., Froudist-Walsh, S., Jaramillo, J., Jiang, J., Wang, X.-J. Abstract: Recent advances in connectomic and neurophysiological tools make it possible to probe whole-brain mechanisms in the mouse that underlie cognition and behavior. Based on experimental data, we developed a large-scale model of the mouse brain for a cardinal cognitive function called working memory, the brain's ability to internally hold and process information without sensory input. In the model, interregional connectivity is constrained by mesoscopic connectome data. The density of parvalbumin-expressing interneurons in the model varies systematically across the cortex. We found that the long-range cell type-specific targeting and density of cell classes define working memory representations. A core cortical subnetwork and the thalamus produce distributed persistent activity, and the network exhibits numerous attractor states. Novel cell type-specific graph theory measures predicted the activity patterns and core subnetwork. This work highlights the need for cell type-specific connectomics, and provides a theory and tools to interpret large-scale recordings of brain activity during cognition. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.11.22.517546v1?rss=1 Authors: Xu, S., Gallivan, J., Blohm, G. Abstract: Recent neural and behavioural findings provide support that the medial intraparietal sulcus (mIPS) and dorsal premotor (PMd) activity reflect aspects of a kinematic plan for reaching movements. However, it is unclear how these two regions differentially contribute to reach planning. Here, we used high-definition transcranial direct current stimulation (HD-tDCS; 4 x 1 electrode placement; 2 mA for 20 min; 3 cm radius) to investigate the functional roles of mIPS and PMd in the left hemisphere of humans. We examined the changes in endpoint error in reaching task with different initial hand positions and different target locations spanning both visual hemi-fields. Participants completed the task with (stimulation, post-stimulation) and without stimulation (pre-stimulation) of individually fMRI-localized cortical areas mIPS and PMd. We found a significant interaction effect between initial hand position (IHP) and target position on the difference in horizontal endpoint error after cathodal left mIPS stimulation and significant IHP and target position main effects after cathodal left PMd stimulation, suggesting that IHP and target position are not yet integrated into a movement vector at the input of the mIPS, but are integrated in the input of PMd. Hence, these findings reveal a distinction between mIPS and PMd in the stages of movement vector formation for reaching movements and indicate that HD-tDCS is a viable method for perturbing localized cortical activity to elucidate localized cortical function. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.11.18.517011v1?rss=1 Authors: Toyoshima, Y., Sato, H., Nagata, D., Kanamori, M., Jang, M. S., Kuze, K., Oe, S., Teramoto, T., Iwasaki, Y., Yoshida, R., Ishihara, T., Iino, Y. Abstract: Recent development of large-scale activity imaging of neuronal ensembles provides opportunities for understanding how activity patterns are generated in the brain and how information is transmitted between neurons or neuronal ensembles. However, methodologies for extracting the component properties that generate overall dynamics are still limited. In this study, the results of time-lapse 3D imaging (4D imaging) of head neurons of the nematode C. elegans were analyzed by hitherto unemployed methodologies. By combining time-delay embedding with independent component analysis, the whole-brain activities were decomposed to a small number of component dynamics. Results from multiple samples, where different subsets of neurons were observed, were further combined by matrix factorization, revealing common dynamics from neuronal activities that are apparently divergent across sampled animals. By this analysis, we could identify components that show common relationships across different samples and those that show relationships distinct between individual samples. We also constructed a network model building on time-lagged prediction models of synaptic communications. This was achieved by dimension reduction of 4D imaging data using the general framework gKDR (gradient kernel dimension reduction). The model is able to decompose basal dynamics of the network. We further extended the model by incorporating probabilistic distribution, resulting in models that we call gKDR-GMM and gKDR-GP. The models capture the overall relationships of neural activities and reproduce the stochastic but coordinated dynamics in the neural network simulation. By virtual manipulation of individual neurons and synaptic contacts in this model, information flow could be estimated from whole-brain imaging results. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.11.17.516971v1?rss=1 Authors: Thambyrajah, R., Fadlullah, Z., Proffitt, M., Neo, W. H., Guillen, Y., Casado-Pelaez, M., Herrero-Molinero, P., Brujas, C., Castelluccio, N., Gonzalez, J., Iglesias, A., Marruecos, L., Ruiz-Herguido, C., Esteller, M., Mereu, E., Lacaud, G., Espinosa, L., Bigas, A. Abstract: Recent findings are challenging the classical hematopoietic model in which long-term hematopoietic stem cells (LT-HSC) are the base of the hematopoietic system. Clonal dynamics analysis of the hematopoietic system indicate that LT-HSC are not the main contributors of normal hemapoiesis in physiological conditions and the hematopoietic system is mainly maintained by multipotent progenitors (MPPs, hereafter HPC) and LT-HSCs are mostly in a non-active state. The first HSCs emerge from the aorta-gonad and mesonephros (AGM) region along with hematopoietic progenitors (HPC) within hematopoietic clusters. Molecular pathways that determine the HSC fate instead of HPC are still unknown, although inflammatory signaling, including NF-KB has been implicated in the development of HSCs. Here, we identify a chromatin binding function for IKB (also known as the inhibitor of NF-KB) that is Polycomb repression complex 2 (PRC2)- dependent and specifically determines dormant vs proliferating HSCs from the onset of their emergence in the AGM. We find a specific reduction of LT-HSCs in the IKB knockout new-born pups. This defect is manifested at the FL stage already, and traceable to the first emerging HSCs in the E11.5 AGM, without affecting the general HPC population. IKB-deficient LT-HSCs express dormancy signature genes, are less proliferative and can robustly respond to activation stimuli such as in vitro culture and serial transplantation. At the molecular level, we find decreased PRC2-dependent H3K27me3 at the promoters of several retinoic acid signaling elements in the IKB- deficient aortic endothelium and E14.5 FL LT-HSCs. Additionally, IKB binding itself is found in the promoters of retinoic acid receptors rar in the AGM, and rar{gamma} in the LT-HSC of FL. Overall, we demonstrate that the retinoic acid pathway is over-activated in the hematopoietic clusters of IKB-deficient AGMs leading to premature dormancy of LT- HSCs that persists in the FL LT-HSCs. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.11.14.516537v1?rss=1 Authors: Sorscher, B., Mel, G. C., Nayebi, A., Giocomo, L., Yamins, D., Ganguli, S. Abstract: Recent work has claimed that the emergence of grid cells from trained path-integrator circuits is a more fragile phenomenon than previously reported. In this note we critically assess the main analysis and simulation results underlying this claim, within the proper context of previously published theoretical work. Our assessment reveals that the emergence of grid cells is entirely consistent with this prior theory: hexagonal grid cells robustly emerge precisely when prior theory predicts they should, and don't when prior theory predicts they should not. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.11.14.516396v1?rss=1 Authors: Lopopolo, A., Rabovsky, M. Abstract: Recent research has shown that the internal dynamics of an artificial neural network model of sentence comprehension displayed a similar pattern to the amplitude of the N400 in several conditions known to modulate this event-related potential. These results led Rabovsky, Hansen, and McClelland (2018) to suggest that the N400 might reflect change in an implicit predictive representation of meaning corresponding to semantic prediction error. This explanation stands as an alternative to the hypothesis that the N400 reflects lexical-prediction error as estimated by word Surprisal (Frank, Otten, Galli, & Vigliocco, 2015). In the present study, we directly model the amplitude of the N400 elicited during naturalistic sentence processing by using as predictor the update of the distributed representation of sentence meaning generated by a Sentence Gestalt model (McClelland, St. John, & Taraban, 1989) trained on a large-scale text corpus. This enables a quantitative prediction of N400 amplitudes based on a cognitively motivated model, as well as quantitative comparison of this model to alternative models of the N400. Specifically, we compare the update measure from the SG model to Surprisal estimated by a comparable language model trained on next-word prediction. The results reported in this paper corroborate the hypothesis that N400 amplitudes correspond to the change in an implicit predictive representation of meaning after every word presentation. Furthermore, we argue that a comparison of the Sentence Gestalt update and Surprisal might also uncover two distinct but probably closely related sub-processes that contribute to the processing of a sentence. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.11.04.515251v1?rss=1 Authors: Nicholas, J., Amlang, C., Lin, C.-Y., Montaser-Kouhsari, L., Desai, N., Pan, M.-K., Kuo, S.-H., Shohamy, D. Abstract: Recent findings in animals have challenged the traditional view of the cerebellum solely as the site of motor control, suggesting that the cerebellum may also be important for learning to predict reward from trial-and-error feedback. Yet, evidence for the role of the cerebellum in reward learning in humans is lacking. Moreover, open questions remain about which specific aspects of reward learning the cerebellum may contribute to. Here we address this gap through an investigation of multiple forms of reward learning in individuals with cerebellum dysfunction, represented by cerebellar ataxia cases. Nineteen participants with cerebellar ataxia and 57 age- and sex-matched healthy controls completed two separate tasks that required learning about reward contingencies from trial-and-error. To probe the selectivity of reward learning processes, the tasks differed in their underlying structure: while one task measured incremental reward learning ability alone, the other allowed participants to use an alternative learning strategy based on episodic memory alongside incremental reward learning. We found that individuals with cerebellar ataxia were profoundly impaired at reward learning from trial-and-error feedback on both tasks, but retained the ability to learn to predict reward based on episodic memory. These findings provide evidence from humans for a specific and necessary role for the cerebellum in incremental learning of reward associations based on reinforcement. More broadly, the findings suggest that alongside its role in motor learning, the cerebellum likely operates in concert with the basal ganglia to support reinforcement learning from reward. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.11.03.515121v1?rss=1 Authors: Syeda, A., Zhong, L., Tung, R., Long, W., Pachitariu, M., Stringer, C. Abstract: Recent studies in mice have shown that orofacial behaviors drive a large fraction of neural activity across the brain. To understand the nature and function of these signals, we need better computational models to characterize the behaviors and relate them to neural activity. Here we developed Facemap, a framework consisting of a keypoint tracking algorithm and a deep neural network encoder for predicting neural activity. We used the Facemap keypoints as input for the deep neural network to predict the activity of ~50,000 simultaneously-recorded neurons and in visual cortex we doubled the amount of explained variance compared to previous methods. Our keypoint tracking algorithm was more accurate than existing pose estimation tools, while the inference speed was several times faster, making it a powerful tool for closed-loop behavioral experiments. The Facemap tracker was easy to adapt to data from new labs, requiring as few as 10 annotated frames for near-optimal performance. We used Facemap to find that the neuronal activity clusters which were highly driven by behaviors were more spatially spread-out across cortex. We also found that the deep keypoint features inferred by the model had time-asymmetrical state dynamics that were not apparent in the raw keypoint data. In summary, Facemap provides a stepping stone towards understanding the function of the brainwide neural signals and their relation to behavior. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.11.03.515111v1?rss=1 Authors: Zhu, Y., Wang, G., Kolluru, C., Gu, Y., Gao, H., Zhang, J., Wang, Y., Wilson, D. L., Zhu, X., Flask, C. A., Yu, X. Abstract: Recent studies have suggested the glymphatic system as a solute transport pathway and waste removal mechanism in the brain. Imaging intracisternally administered tracers provides the opportunity of assessing various aspects of the glymphatic function in vivo. Dynamic contrast-enhanced MRI allows the evaluation of both the kinetics and spatial distribution of tracer transport throughout the brain. In this study, we investigated the impact of the molecular size of intracisternal tracers on the transport kinetics and distribution in the healthy mouse brain. Three MRI contrast agents with drastically different molecular weights (MWs): 1) Gd-DTPA (MW=661.8 Da), 2) GadoSpin (MW=200 kDa), and 3) oxygen-17 enriched water (H217O, MW=19 Da), were administered via cisterna magna infusion and their transport was dynamically assessed. Our results show that the transport of H217O was significantly faster and more extensive than the two gadolinium-based contrast agents. Time-lagged correlation analysis and clustering analysis comparing the kinetics of Gd-DTPA and H217O transport also showed different cluster patterns and lag time between different regions of the brain. Further, there were also significant differences in the transport kinetics of the three tracers to the lateral ventricles. These observations suggest the size-dependent differences in forces that drive tracer transport in the brain. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.21.513067v1?rss=1 Authors: Billen, L. S., Corneil, B. D., Weerdesteyn, V. Abstract: Recent work has described express visuomotor responses (EVRs) on the upper limb. EVRs are directionally-tuned bursts of muscle activity that occur within 100 ms of visual stimulus appearance, facilitating rapid reaching. Rapid stepping responses are also important in daily life, and while there is evidence of EVR expression on lower limbs, it is unknown whether lower-limb EVRs are influenced by increased postural demands. Here, we investigate the interaction between stepping-related EVRs and anticipatory postural adjustments (APAs) that typically precede step initiation. 16 healthy young subjects rapidly stepped towards visual targets presented in front of the left or right foot. We recorded bilateral surface EMG of gluteus medius (GM), a muscle involved in both APAs and stepping, and bilateral ground reaction forces. Two conditions were introduced: a lateral or medial stepping condition with reduced or increased postural demands, respectively. In the lateral stepping condition, EVRs were robustly and strongly present in stance-side GM, and ground reaction forces revealed the absence of APAs. Larger EVRs preceded shorter RTs, consistent with EVRs facilitating step initiation. In contrast, in the medial stepping condition, EVRs were largely absent, and ground reaction forces revealed the consistent presence of APAs. When occasionally present, EVRs in the medial stepping condition preceded larger APAs and longer RTs. Thus, while EVRs in lower limbs can facilitate rapid stepping, their expression is normally suppressed when postural stability is low. Failing to appropriately suppress EVRs in such situations disrupts postural stability, necessitating larger compensatory APAs and leading to longer stepping RTs. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.20.512802v1?rss=1 Authors: Fotiadis, P., Cieslak, M., He, X., Caciagli, L., Ouellet, M., Satterthwaite, T. D., Shinohara, R. T., Bassett, D. S. Abstract: Recent work has demonstrated that the relationship between structural and functional connectivity varies regionally across the human brain, with reduced coupling emerging along the sensory-association cortical hierarchy. The biological underpinnings driving this expression, however, remain largely unknown. Here, we postulated that intracortical myelination and excitation-inhibition (EI) balance mediate the heterogeneous expression of structure-function coupling (SFC) and its temporal variance across the cortical hierarchy. We employed atlas- and voxel-based connectivity approaches to analyze neuroimaging data acquired from two groups of healthy participants. Our findings were consistent across processing pipelines: 1) increased myelination and lower EI-ratio associated with more rigid SFC and restricted moment-to-moment SFC fluctuations; 2) a gradual shift from EI-ratio to myelination as the principal predictor of SFC occurred when traversing from granular to agranular cortical regions. Collectively, our work delivers a novel framework to conceptualize structure-function relationships in the human brain, paving the way for an improved understanding of how demyelination and/or EI-imbalances induce reorganization in brain disorders. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.16.512398v1?rss=1 Authors: Shoham, A., Grosbard, I., Patashnik, O., Cohen-Or, D., Yovel, G. Abstract: Recent studies show significant similarities between the representations humans and deep neural networks (DNNs) generate for faces. However, two critical aspects of human face recognition are overlooked by these networks. First, human face recognition is mostly concerned with familiar faces, which are encoded by visual and semantic information, while current DNNs solely rely on visual information. Second, humans represent familiar faces in memory, but representational similarities with DNNs were only investigated for human perception. To address this gap, we combined visual (VGG-16), visual-semantic (CLIP), and natural language processing (NLP) DNNs to predict human representations of familiar faces in perception and memory. The visual-semantic network substantially improved predictions beyond the visual network, revealing a new visual-semantic representation in human perception and memory. The NLP network further improved predictions of human representations in memory. Thus, a complete account of human face recognition should go beyond vision and incorporate visual-semantic, and semantic representations. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.10.511670v1?rss=1 Authors: Wen, C., Matsumoto, M., Sawada, M., Sawamoto, K., Kimura, K. D. Abstract: Recent advances in microscopy techniques, especially in electron microscopy, are transforming biomedical studies by acquiring large quantities of high-precision 3D cell image stacks. However, to study cell morphology and connectivity in organs such as brains, scientists must first perform cell segmentation, which involves extracting individual cell regions of various shapes and sizes from a 3D image. This remains a great challenge because automatic cell segmentation can contain numerous errors, even with advanced deep learning methods. For biomedical research that requires cell segmentation in large 3D image stacks, an efficient semi-automated software solution is still needed. We created Seg2Link, which generates automatic segmentations based on deep learning predictions and allows users to quickly correct errors in the segmentation results. It can perform automatic instance segmentation of 2D cells in each slice, 3D cell linking across slices, and various manual corrections, in order to efficiently transform inaccurate deep learning predictions into accurate segmentation results. Seg2Link's data structure and algorithms were also optimized to process 3D images with billions of voxels on a personal computer quickly. Thus, Seg2Link offers a simple and effective way for scientists to study cell morphology and connectivity in 3D image stacks. Copy rights belong to original authors. Visit the link for more info Podcast created by PaperPlayer

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.03.510672v1?rss=1 Authors: Sakon, J. J., Halpern, D. J., Schonhaut, D. R., Kahana, M. J. Abstract: Recent human electrophysiology work has uncovered the presence of high frequency oscillatory events, termed ripples, during awake behavior. This work focuses on ripples in the medial temporal lobe (MTL) during memory retrieval. Few studies, however, investigate ripples during item encoding. Many studies have found neural activity during encoding that predicts later recall, termed subsequent memory effects (SMEs), but it is unclear if encoding ripples also predict subsequent recall. Detecting ripples in 124 neurosurgical participants performing an episodic memory task, we do not find ripple SMEs in any MTL region, even as these regions exhibit robust high frequency activity (HFA) SMEs. Instead, hippocampal ripples increase during encoding of items leading to recall of temporally or semantically associated items, a phenomenon known as clustering. This subsequent clustering effect (SCE) arises specifically when hippocampal ripples occur during both encoding and retrieval, suggesting that ripples mediate the encoding and future reinstatement of episodic memories. Copy rights belong to original authors. Visit the link for more info Podcast created by PaperPlayer

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.26.508928v1?rss=1 Authors: Handy, G., Borisyuk, A. Abstract: Recent experimental works have implicated astrocytes as a significant cell type underlying several neuronal processes in the mammalian brain, from encoding sensory information to neurological disorders. Despite this progress, it is still unclear how astrocytes are communicating with and driving their neuronal neighbors. While previous computational modeling works have helped propose mechanisms responsible for driving these interactions, they have primarily focused on interactions at the synaptic level, with microscale models of calcium dynamics and neurotransmitter diffusion. Since it is computationally infeasible to include the intricate microscale details in a network-scale model, little computational work has been done to understand how astrocytes may be influencing spiking patterns and synchronization of large networks. We overcome this issue by first developing an "effective" astrocyte that can be easily implemented to already established network frameworks. We do this by showing that the astrocyte proximity to a synapse makes synaptic transmission faster, weaker, and less reliable. Thus, our "effective" astrocytes can be incorporated by considering heterogeneous synaptic time constants, which are parametrized only by the degree of astrocytic proximity at that synapse. We then apply our framework to large networks of exponential integrate-and-fire neurons with various spatial structures. Depending on key parameters, such as the number of synapses ensheathed and the strength of this ensheathment, we show that astrocytes can push the network to a synchronous state and exhibit spatially correlated patterns. Copy rights belong to original authors. Visit the link for more info Podcast created by PaperPlayer

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.16.508275v1?rss=1 Authors: Kalyanasundar, B., Blonde, G., Spector, A. C., Travers, S. P. Abstract: Recent findings from our laboratory demonstrated that the rostral nucleus of solitary tract (rNST) retains some responsiveness to glutamate (MSG+amiloride-MSGa) and sugars in mice lacking the canonical T1R receptors for these tastants. Here, we recorded from the parabrachial nucleus (PBN) in mice lacking the T1R1+T1R3 heterodimer (KO1+3), using warm stimuli to optimize sugar responses and employing extended concentrations and pharmacological agents to probe mechanisms. MSGa+IMP responses were not synergized in KO1+3 mice but responses to MSGa were similar to those in B6 (WT) mice. Glutamate responses in the neurons tested were unaffected by topical application of an mGluR4 antagonist. PBN T1R-independent sugar responses, including those to concentrated glucose, were more evident than in rNST. Sugar responses were undiminished by phlorizin, an inhibitor of SGLT, a component of a hypothesized alternative glucose-sensing mechanism. There were no sugar/umami "best" neurons in KO1+3 mice, and instead, sugars activated cells that displayed acid and amiloride-insensitive NaCl responses. In WTs, concentrated sugars activated "sugar/umami" cells but also electrolyte-sensitive neurons. The efficacy of hyperosmotic sugars for driving neurons broadly responsive to electrolytes implied an origin from Type III taste bud cells. To test this, we used the carbonic anhydrase (CA) inhibitor dorzolamide (DRZ), previously shown to inhibit amiloride-insensitive sodium responses arising from Type III cells. Dorzolamide had no effect on sugar-elicited responses in WT sugar/umami PBN neurons but strongly suppressed them in WT and KO electrolyte-generalist neurons. These findings suggest a novel T1R-independent mechanism for hyperosmotic sugars, involving a CA-dependent mechanism in Type-III taste bud cells. Copy rights belong to original authors. Visit the link for more info Podcast created by PaperPlayer

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.16.508350v1?rss=1 Authors: Gao, Y. Abstract: Recent experimental observations have shown that the reactivation of hippocampal place cells (PC) during sleep or immobility depicts trajectories that can go around barriers and can flexibly adapt to a changing maze layout. Such layout-conforming replay sheds a light on how the activity of place cells supports the learning of flexible navigation in a dynamically changing maze. However, existing computational models of replay fall short of generating layout-conforming replay, restricting their usage to simple environments, like linear tracks or open fields. In this paper, we propose a computational model that generates layout-conforming replay and explains how such replay drives the learning of flexible navigation in a maze. First, we propose a Hebbian-like rule to learn the inter-PC synaptic strength during exploring a maze. Then we use a continuous attractor network (CAN) with feedback inhibition to model the interaction among place cells and hippocampal interneurons. The activity bump of place cells drifts along a path in the maze, which models layout-conforming replay. During replay in rest, the synaptic strengths from place cells to striatal medium spiny neurons (MSN) are learned by a novel dopamine-modulated three-factor rule to store place-reward associations. During goal-directed navigation, the CAN periodically generates replay trajectories from the animal's location for path planning, and the trajectory leading to a maximal MSN activity is followed by the animal. We have implemented our model into a high-fidelity virtual rat in the MuJoCo physics simulator. Extensive experiments have demonstrated that its superior flexibility during navigation in a maze is due to a continuous re-learning of inter-PC and PC-MSN synaptic strength. Copy rights belong to original authors. Visit the link for more info Podcast created by PaperPlayer

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: Military Artificial Intelligence as Contributor to Global Catastrophic Risk, published by MMMaas on June 27, 2022 on The Effective Altruism Forum. Note: the below is a chapter by Matthijs Maas, Kayla Matteucci, and Di Cooke (CSER), for a forthcoming book on new avenues of research on Global Catastrophic Risk. The chapter explores whether, or in what ways, the military use of AI could constitute or contribute to Global Catastrophic Risk. A PDF of the chapter is also available at SSRN (see also CSER publication page). TLDR: The chapter focuses primarily on two distinct proposed scenarios, (1) the use of swarms of Lethal Autonomous Weapons Systems, and potential barriers and disincentives; and (2) the intersection of military AI and nuclear weapons. We specifically outline six ways in which the use of AI systems in-, around-, or against- nuclear weapons and their command infrastructures could increase the likelihood of nuclear escalation and global catastrophe This research dovetails with themes explored by others in the community, such as in Christian Ruhl's recent Founders Pledge report on 'risks from autonomous weapons and military AI'. We welcome your thoughts both on the content of the chapter, as well as suggestions for future research in this area could be valuable and interesting, as this chapter is a condensed version of a larger research project that dives into a broader set of ways in which military AI systems could intersect with, contribute to, or compound global catastrophic risk. (Length note: half of this page's length consists of references & endnotes). Abstract: Recent years have seen growing attention for the use of AI technologies in warfare, which has been rapidly advancing. This chapter explores in what ways such military AI technologies might contribute to Global Catastrophic Risks (GCR). After reviewing the GCR field's limited previous engagement with military AI, and giving an overview of recent advances in military AI, this chapter focuses on two risk scenarios that have been proposed. First, we discuss arguments around the use of swarms of Lethal Autonomous Weapons Systems, and suggest that while these systems are concerning, they appear not yet likely to be a GCR in the near-term, on the basis of current and anticipated production limits and costs which make these systems still uncompetitive with extant systems for mass destruction. Second, we delve into the intersection of military AI and nuclear weapons, which we argue has a significantly higher GCR potential. We review historical debates over when, where, and why nuclear weapons could lead to GCR, along with recent geopolitical developments that could raise these risks further. We then outline six ways in which the use of AI systems in-, around-, or against- nuclear weapons and their command infrastructures could increase the likelihood of nuclear escalation and global catastrophe. The chapter concludes with suggestions for a research agenda that can gain a more comprehensive and multidisciplinary understanding of the potential risks from military AI, both today and in the future. 1. Introduction It should hardly be surprising that military technologies have featured prominently in public discussions of global catastrophic risk (GCR).1 The prospect of uncontrolled global war stands as one of the oldest and most pervasive scenarios of what total societal disaster would look like. Conflict has always been able to devastate individual societies: in the modern era, technological and scientific progress has steadily increased the ability of state militaries, and possibly others, to inflict catastrophic violence.2–4 There are many such technologies, with Artificial Intelligence (AI) becoming an even more notable one in recent years. Increasingly, experts from numerous fields have begun to focus ...