@article{21895, abstract = {The mammalian brain organises knowledge about entities in the world and relationships between them using cognitive maps. When forming a cognitive map, there is a necessary trade-off between extending the map to make novel inferences, and storing a veridical copy of past experience. However, the neural mechanisms that control this trade-off remain unknown. Using a cross-scale approach that combines a pharmacological intervention in humans with neural network modelling, we show that the neuromodulator noradrenaline elicits a significant ‘spread of association’ across hippocampal cognitive maps. This neural spread of association can be explained by changes in synaptic plasticity that predict overgeneralisation in behaviour. Thus, elevated noradrenaline during learning increases the ‘smoothing kernel’ for plasticity across the cognitive map, allowing disparate memories to become linked and distorted.}, author = {Koolschijn, Renée S. and Parthasarathy, Prakriti and Browning, Michael and Przygodda, Xenia and Capitão, Liliana P. and Clarke, William T. and Vogels, Tim P and O’Reilly, Jill X. and Barron, Helen C.}, issn = {2041-1723}, journal = {Nature Communications}, publisher = {Springer Nature}, title = {{Noradrenaline causes a spread of association in the hippocampal cognitive map}}, doi = {10.1038/s41467-026-70659-x}, volume = {17}, year = {2026}, } @article{21378, abstract = {From insects to mammals, essential brain functions, such as forming long-term memories (LTMs), increase metabolic activity in stimulated neurons to meet the energetic demand associated with brain activation. However, while impairing neuronal metabolism limits brain performance, whether expanding the metabolic capacity of neurons boosts brain function remains poorly understood. Here, we show that LTM formation of flies and mice can be enhanced by increasing mitochondrial metabolism in central memory circuits. By knocking down the mitochondrial Ca2+ exporter Letm1, we favour Ca2+ retention in the mitochondrial matrix of neurons due to reduction of mitochondrial H+/Ca2+ exchange. The resulting increase in mitochondrial Ca2+ over-activates mitochondrial metabolism in neurons of central memory circuits, leading to improved LTM storage in training paradigms in which wild-type counterparts of both species fail to remember. Our findings unveil an evolutionarily conserved mechanism that controls mitochondrial metabolism in neurons and indicate its involvement in shaping higher brain functions, such as LTM.}, author = {Amrapali Vishwanath, Anjali and Comyn, Typhaine and Mira, Rodrigo G. and Brossier, Claire and Pascual-Caro, Carlos and Faour, Maya and Boumendil, Kahina and Chintaluri, Chaitanya and Ramon-Duaso, Carla and Fan, Ruolin and Ghosh, Kishalay and Farrants, Helen and Berwick, Jean-Paul and Sivakumar, Riya and Lopez-Manzaneda, Mario and Schreiter, Eric R. and Preat, Thomas and Vogels, Tim P and Rangaraju, Vidhya and Busquets-Garcia, Arnau and Plaçais, Pierre-Yves and Pavlowsky, Alice and de Juan-Sanz, Jaime}, issn = {2522-5812}, journal = {Nature Metabolism}, number = {2}, pages = {467--488}, publisher = {Springer Nature}, title = {{Mitochondrial Ca2+ efflux controls neuronal metabolism and long-term memory across species}}, doi = {10.1038/s42255-026-01451-w}, volume = {8}, year = {2026}, } @article{20986, abstract = {During complex vocal interactions, different features of acoustic stimuli are integrated to produce appropriate vocal responses,1 such as copying sounds during vocal matching behavior in some animals.2,3,4,5,6,7,8,9,10,11,12 However, little is known about the interplay and possible trade-offs between the different temporal and spectral acoustic features during these vocal exchanges.2,13,14 Nightingales can flexibly match the pitch of their tonal “whistle songs” in real time during counter-singing duels.15,16 Here, we show that the syllable duration of whistle playbacks could alter the song responses of wild nightingales, causing their whistle duration distribution to shift toward the presented stimulus duration. When exposed to whistle playbacks featuring unnatural combinations of pitch and duration, nightingales demonstrate a flexible trade-off between pitch matching and temporal imitation, yet they are constrained by their vocal repertoire. They selectively adapted their vocal responses to approximate these novel stimuli, aligning them with their natural whistle repertoire. We developed a computational model of nightingale whistle-matching behavior that revealed a hierarchical organization of acoustic feature production. During whistle matching, the feature integration process is constrained by the duration of syllables, and pitch matching follows within this temporal framework, forcing a trade-off between the two features. Our findings reveal a complex interplay between the spectral and temporal domains that shapes song-matching behavior.}, author = {Calderon Garcia, Juan Sebastian and Costalunga, Giacomo and Vogels, Tim P and Vallentin, Daniela}, issn = {1879-0445}, journal = {Current Biology}, number = {3}, pages = {791--798.e6}, publisher = {Elsevier}, title = {{Interplay between syllable duration and pitch during whistle matching in wild nightingales}}, doi = {10.1016/j.cub.2025.12.025}, volume = {36}, year = {2026}, } @article{22295, abstract = {Despite the functional diversity of over 100 causal genes1,2,3, phenotypic convergence across models may reveal common neurobiological processes in autism spectrum disorder (ASD). Here we profiled 251 samples from 11 monogenic mouse models of ASD using single-nucleus multi-omic sequencing across three developmental stages, both sexes and two brain regions. Despite genetic heterogeneity, ASD-linked mutations converged on perturbations of the radial glial cell lineage. These alterations reflect a transient developmental delay rather than lasting lineage misspecification and resolve by postnatal stages. Molecularly, the largest transcriptional differences emerged in neurons at early postnatal stages. These changes included downregulation of synaptic and ion channel-related genes, consistent with homeostatic adaptation or delayed maturation. Network analysis showed molecular convergence across models within each developmental stage, suggesting that diverse mutations linked to ASD impinge on common, stage-specific processes. Convergence becomes less pronounced by postnatal day 14, highlighting the dynamic nature of ASD-associated changes. Cross-genotype heterogeneity is superimposed on stage-specific effects. Electrophysiology corroborated this pattern: mutants generally showed altered neuronal excitability and synaptic properties with model-specific nuances. Our study also highlighted sex-specific gene expression alterations, with female mice often displaying larger effect sizes than male mice. Together, our findings provide a comprehensive view of developmental cellular and molecular dynamics across models of ASD.}, author = {Schwarz, Lena A and Dotter, Christoph and Isaev, Sergey and Lisi, Michela and Malzl, Daniel and Büschl, Christoph and Ladstätter, Sabrina and Oliveira, Bárbara and Barel, Matteo and Basilico, Bernadette and Chintaluri, Chaitanya and Gorkiewicz, Sarah and Goudarzi, Mohammad and Belinova, Tereza and Reichl, Stephan and Sendžikaitė, Gintarė and Arcot Jayaram, Satish and Koppensteiner, Peter and Sommer, Christoph M and Vogels, Tim P and Menche, Jörg and Adameyko, Igor and Kharchenko, Peter Vasili and Bock, Christoph and Novarino, Gaia}, issn = {1476-4687}, journal = {Nature}, publisher = {Springer Nature}, title = {{Cortical development dynamics across autism spectrum disorder mouse models}}, doi = {10.1038/s41586-026-10679-1}, year = {2026}, } @article{21746, abstract = {As vertebrates transitioned from water to land, locomotion shifted from undulatory swimming to limb-based movement. How spinal circuits and their cell types evolved to support this transition remains unclear. We leverage frog metamorphosis, which recapitulates this transition within a single organism, to define how spinal circuits generate aquatic versus terrestrial motor patterns. At swim stages, spinal architecture is uniform, with a transcriptionally and anatomically homogeneous motor and interneurons. As limbs develop and their movement complexifies, spinal circuits expand in neuron number and subtype diversity. This expansion is most pronounced for V1 inhibitory neurons, which increase ∼70-fold and diversify into transcriptionally distinct subtypes. Disrupting transcription factors defining emerging motor and V1 populations reveals molecular segregation between swim and limb circuits, highlighting the role of subtype diversity in motor coordination. A multifold increase in inhibitory neuron diversity thus underlies the tail-to-limb locomotor transition, providing a framework for spinal circuit adaptation during vertebrate evolution.}, author = {Vijatovic, David and Toma, Florina Alexandra and Ignatyev, Y and Harrington, Zoe P and Sommer, Christoph M and Hauschild, Robert and Smits, Matthijs Geert and Dalla Vecchia, Marco and Trevisan, Alexandra J. and Chapman, Phillip and Julseth, Mara and Brenner-Morton, Susan and Gabitto, Mariano I. and Dasen, Jeremy S. and Bikoff, Jay B. and Sweeney, Lora Beatrice Jaeger}, issn = {2211-1247}, journal = {Cell Reports}, number = {4}, publisher = {Elsevier}, title = {{Multifold increase in spinal inhibitory cell types with emergence of limb movement}}, doi = {10.1016/j.celrep.2026.117227}, volume = {45}, year = {2026}, } @article{19036, abstract = {Neuronal processing of external sensory input is shaped by internally generated top–down information. In the neocortex, top–down projections primarily target layer 1, which contains NDNF (neuron-derived neurotrophic factor)-expressing interneurons and the dendrites of pyramidal cells. Here, we investigate the hypothesis that NDNF interneurons shape cortical computations in an unconventional, layer-specific way, by exerting presynaptic inhibition on synapses in layer 1 while leaving synapses in deeper layers unaffected. We first confirm experimentally that in the auditory cortex, synapses from somatostatin-expressing (SOM) onto NDNF neurons are indeed modulated by ambient Gamma-aminobutyric acid (GABA). Shifting to a computational model, we then show that this mechanism introduces a distinct mutual inhibition motif between NDNF interneurons and the synaptic outputs of SOM interneurons. This motif can control inhibition in a layer-specific way and introduces competition between NDNF and SOM interneurons for dendritic inhibition onto pyramidal cells on different timescales. NDNF interneurons can thereby control cortical information flow by redistributing dendritic inhibition from fast to slow timescales and by gating different sources of dendritic inhibition.}, author = {Naumann, Laura B and Hertäg, Loreen and Müller, Jennifer and Letzkus, Johannes J. and Sprekeler, Henning}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences}, number = {4}, publisher = {National Academy of Sciences}, title = {{Layer-specific control of inhibition by NDNF interneurons}}, doi = {10.1073/pnas.2408966122}, volume = {122}, year = {2025}, } @article{19068, abstract = {Whether or not the neuron emits a spike in response to stimulation by an excitatory current pulse is determined by a strength-duration curve (SDC) for the pulse parameters. The SDC is a dependence of the minimal pulse amplitude required to elicit the spiking response on either the pulse duration or its decay time. Excitatory neurons affect the others through pulses of excitatory postsynaptic current. A simple yet plausible approximation for the time course of such a pulse is the alpha function, with linear rise at the start and exponential decay at the end. However, an exact analytical SDC for this case is hitherto not known, even for the leaky integrate-and-fire (LIF) neuron, the simplest spiking neuron model used in practice. We have obtained general SDC equations for the LIF neuron. Using the Lambert W function — a widely-implemented special function, we have found the exact analytical SDC for the spiking response of the LIF neuron stimulated by an excitatory current pulse in the form of the alpha function. To compare results in a unified way, we have also derived the analytical SDCs for (i) rectangular pulse, (ii) ascending ramp pulse, and (iii) instantly rising and exponentially decaying pulse. In the limit of no leakage, we show that the SDC is reduced to the classical hyperbola for all considered cases.}, author = {Paraskevov, Alexander}, issn = {2590-0374}, journal = {Results in Applied Mathematics}, publisher = {Elsevier}, title = {{Analytical strength-duration curve for the spiking response of the LIF neuron to an alpha-function-shaped excitatory current pulse}}, doi = {10.1016/j.rinam.2025.100548}, volume = {25}, year = {2025}, } @article{19640, abstract = {Synaptic plasticity is a key player in the brain’s life-long learning abilities. However, due to experimental limitations, the mechanistic link between synaptic plasticity rules and the network-level computations they enable remain opaque. Here we use evolutionary strategies (ES) to meta learn local co-active plasticity rules in large recurrent spiking networks with excitatory (E) and inhibitory (I) neurons, using parameterizations of increasing complexity. We discover rules that robustly stabilize network dynamics for all four synapse types acting in isolation (E-to-E, E-to-I, I-to-E and I-to-I). More complex functions such as familiarity detection can also be included in the search constraints. However, our meta learning strategy begins to fail for co-active rules of increasing complexity, as it is challenging to devise loss functions that effectively constrain network dynamics to plausible solutions a priori. Moreover, in line with previous work, we can find multiple degenerate solutions with identical network behaviour. As a local optimization strategy, ES provides one solution at a time and makes exploration of this degeneracy cumbersome. Regardless, we can glean the interdependecies of various plasticity parameters by considering the covariance matrix learned alongside the optimal rule with ES. Our work provides a proof of principle for the success of machine-learning-guided discovery of plasticity rules in large spiking networks, and points at the necessity of more elaborate search strategies going forward.}, author = {Confavreux, Basile J and Agnes, Everton J. and Zenke, Friedemann and Sprekeler, Henning and Vogels, Tim P}, issn = {1553-7358}, journal = {PLoS Computational Biology}, number = {4}, publisher = {Public Library of Science}, title = {{Balancing complexity, performance and plausibility to meta learn plasticity rules in recurrent spiking networks}}, doi = {10.1371/journal.pcbi.1012910}, volume = {21}, year = {2025}, } @article{19794, abstract = {Status epilepticus (SE), seizures lasting beyond five minutes, is a medical emergency commonly treated with benzodiazepines which enhance GABAA receptor (GABAAR) conductance. Despite widespread use, benzodiazepines fail in over one-third of patients, potentially due to seizure-induced disruption of neuronal chloride (Cl−) homeostasis. Understanding these changes at a network level is crucial for improving clinical translation. Here, we address this using a large-scale spiking neural network model incorporating Cl− dynamics, informed by clinical EEG and experimental slice recordings. Our simulations confirm that the GABAAR reversal potential (EGABA) dictates the pro- or anti-seizure effect of GABAAR conductance modulation, with high EGABA rendering benzodiazepines ineffective or excitatory. We show SE-like activity and EGABA depend non-linearly on Cl− extrusion efficacy and GABAAR conductance. Critically, cell-type specific manipulations reveal that pyramidal cell, not interneuron, Cl− extrusion predominantly determines the severity of SE activity and the response to simulated benzodiazepines. Leveraging these mechanistic insights, we develop a predictive framework mapping network states to Cl− extrusion capacity and GABAergic load, yielding a proposed decision-making strategy to guide therapeutic interventions based on initial treatment response. This work identifies pyramidal cell Cl− handling as a key therapeutic target and demonstrates the utility of biophysically detailed network models for optimising SE treatment protocols.}, author = {Currin, Christopher and Burman, Richard J. and Fedele, Tommaso and Ramantani, Georgia and Rosch, Richard E. and Sprekeler, Henning and Raimondo, Joseph V.}, issn = {1095-953X}, journal = {Neurobiology of Disease}, publisher = {Elsevier}, title = {{Network models incorporating chloride dynamics predict optimal strategies for terminating status epilepticus}}, doi = {10.1016/j.nbd.2025.106966}, volume = {212}, year = {2025}, } @article{8125, abstract = {Biological memory is known to be flexible—memory formation and recall depend on factors such as the behavioral context of the organism. However, this property is often ignored in associative memory models, leaving it unclear how memories can be organized and recalled when subject to contextual control. Because of the lack of a rigorous analytical framework, it is also unknown how contextual control affects memory stability, storage capacity, and information content. Here, we bring the dynamic nature of memory to the fore by introducing a novel model of associative memory, which we refer to as the context-modular memory network. In our model, stored memory patterns are associated to one of several background network states, or contexts. Memories are accessible when their corresponding context is active, and are otherwise inaccessible. Context modulates the effective network connectivity by imposing a specific configuration of neuronal and synaptic gating—gated neurons (synapses) have their activity (weights) momentarily silenced, thereby reducing interference from memories belonging to other contexts. Memory patterns are randomly and independently chosen, while neuronal and synaptic gates may be selected randomly or optimized through a process of contextual synaptic refinement. Through analytic and numerical results, we show that context-modular memory networks can exhibit both improved memory capacity and differential control of memory stability with random gating (especially for neuronal gating). For contextual synaptic refinement, we devise a method in which synapses are gated off for a given context if they destabilize the memory patterns in that context, drastically improving memory capacity and enabling even more precise control over memory stability. Notably, synaptic refinement allows for patterns to be accessible in multiple contexts, stabilizing memory patterns even for weight matrices that alone do not contain any information about the memory patterns, such as Gaussian random matrices. Overall, our model integrates recent ideas about context-dependent memory organization with classic associative memory models and proposes a rigorous theory which can act as a framework for future work. Furthermore, our work carries important implications for the understanding of biological memory storage and recall in the brain, such as highlighting an intriguing trade-off between memory capacity and accessibility.}, author = {Podlaski, William F. and Agnes, Everton J. and Vogels, Tim P}, issn = {2160-3308}, journal = {Physical Review X}, publisher = {American Physical Society}, title = {{High capacity and dynamic accessibility in associative memory networks with context-dependent neuronal and synaptic gating}}, doi = {10.1103/PhysRevX.15.011057}, volume = {15}, year = {2025}, } @phdthesis{20735, abstract = {Left–right alternation is a defining feature of spinal locomotor circuits, yet the level of neuronal detail required to generate and maintain this pattern remains unclear. This thesis investigates how models spanning multiple levels of abstraction—from biophysically detailed Hodgkin–Huxley (HH) neurons to adaptive integrate–and–fire (I&F) formulations and synfire-chain modules—can account for the generation of fictive swimming in the spinal cord of the Xenopus laevis tadpole. The guiding hypothesis is that a small set of neuronal mechanisms is sufficient to reproduce the essential features of rhythmic alternation, and that moving between modeling scales helps distinguish core principles from biological detail. A minimal bilateral HH network comprising only four canonical neuron classes—excitatory descending interneurons (dINs), inhibitory commissural interneurons (cINs), ipsilateral inhibitory interneurons (aINs) and motoneurons—served as a biophysical proof of concept. Tuned to reproduce experimentally observed firing modes, the model demonstrated that rebound-prone dIN excitability, contralateral inhibition and modest electrical coupling are sufficient to generate stable alternating activity, even in very small networks. These results motivated the transition to simpler models capable of efficient analysis and scaling. Adaptive exponential I&F (AdEx) neurons were calibrated to physiological recordings using simulation-based inference, yielding tonic and phasic/rebound templates that preserved the key dynamical signatures of the HH model. Phase-plane analysis clarified the mechanisms underlying single-spike responses and rebound firing in dINs. At network level, the I&F models robustly reproduced left–right alternation, while highlighting constraints on synaptic kinetics and adaptation needed to avoid multi-spike responses. Finally, a synfire-chain framework provided a complementary, timing-centric perspective, demonstrating how precise spike synchrony, synaptic delays and minimal inhibitory coupling can generate alternating left–right sequences in a feedforward setting. Together, these approaches converge on a common conclusion: rebound-prone ipsilateral excitation combined with precisely timed contralateral inhibition constitutes a sufficient substrate for alternating spinal rhythms. By integrating bottom-up and top-down modeling strategies, this thesis provides a unified, extensible framework for studying spinal pattern generation. The results show that essential locomotor dynamics can be captured across multiple abstraction levels, offering both mechanistic insight and practical tools for future data-driven investigations of spinal circuit development, robustness and modulation.}, author = {Wilson, Alexia C}, issn = {2791-4585}, pages = {110}, publisher = {Institute of Science and Technology Austria}, title = {{Modelling the spinal cord of a tadpole: Exploring different ways to model the spinal cord in the Xenopus frog}}, doi = {10.15479/AT-ISTA-20735}, year = {2025}, } @article{14841, abstract = {De novo heterozygous variants in KCNC2 encoding the voltage-gated potassium (K+) channel subunit Kv3.2 are a recently described cause of developmental and epileptic encephalopathy (DEE). A de novo variant in KCNC2 c.374G > A (p.Cys125Tyr) was identified via exome sequencing in a patient with DEE. Relative to wild-type Kv3.2, Kv3.2-p.Cys125Tyr induces K+ currents exhibiting a large hyperpolarizing shift in the voltage dependence of activation, accelerated activation, and delayed deactivation consistent with a relative stabilization of the open conformation, along with increased current density. Leveraging the cryogenic electron microscopy (cryo-EM) structure of Kv3.1, molecular dynamic simulations suggest that a strong π-π stacking interaction between the variant Tyr125 and Tyr156 in the α-6 helix of the T1 domain promotes a relative stabilization of the open conformation of the channel, which underlies the observed gain of function. A multicompartment computational model of a Kv3-expressing parvalbumin-positive cerebral cortex fast-spiking γ-aminobutyric acidergic (GABAergic) interneuron (PV-IN) demonstrates how the Kv3.2-Cys125Tyr variant impairs neuronal excitability and dysregulates inhibition in cerebral cortex circuits to explain the resulting epilepsy.}, author = {Clatot, Jerome and Currin, Christopher and Liang, Qiansheng and Pipatpolkai, Tanadet and Massey, Shavonne L. and Helbig, Ingo and Delemotte, Lucie and Vogels, Tim P and Covarrubias, Manuel and Goldberg, Ethan M.}, issn = {1091-6490}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, number = {3}, publisher = {National Academy of Sciences}, title = {{A structurally precise mechanism links an epilepsy-associated KCNC2 potassium channel mutation to interneuron dysfunction}}, doi = {10.1073/pnas.2307776121}, volume = {121}, year = {2024}, } @article{14887, abstract = {Episodic memories are encoded by experience-activated neuronal ensembles that remain necessary and sufficient for recall. However, the temporal evolution of memory engrams after initial encoding is unclear. In this study, we employed computational and experimental approaches to examine how the neural composition and selectivity of engrams change with memory consolidation. Our spiking neural network model yielded testable predictions: memories transition from unselective to selective as neurons drop out of and drop into engrams; inhibitory activity during recall is essential for memory selectivity; and inhibitory synaptic plasticity during memory consolidation is critical for engrams to become selective. Using activity-dependent labeling, longitudinal calcium imaging and a combination of optogenetic and chemogenetic manipulations in mouse dentate gyrus, we conducted contextual fear conditioning experiments that supported our model’s predictions. Our results reveal that memory engrams are dynamic and that changes in engram composition mediated by inhibitory plasticity are crucial for the emergence of memory selectivity.}, author = {Feitosa Tomé, Douglas and Zhang, Ying and Aida, Tomomi and Mosto, Olivia and Lu, Yifeng and Chen, Mandy and Sadeh, Sadra and Roy, Dheeraj S. and Clopath, Claudia}, issn = {1546-1726}, journal = {Nature Neuroscience}, pages = {561--572}, publisher = {Springer Nature}, title = {{Dynamic and selective engrams emerge with memory consolidation}}, doi = {10.1038/s41593-023-01551-w}, volume = {27}, year = {2024}, } @article{15171, abstract = {The brain’s functionality is developed and maintained through synaptic plasticity. As synapses undergo plasticity, they also affect each other. The nature of such ‘co-dependency’ is difficult to disentangle experimentally, because multiple synapses must be monitored simultaneously. To help understand the experimentally observed phenomena, we introduce a framework that formalizes synaptic co-dependency between different connection types. The resulting model explains how inhibition can gate excitatory plasticity while neighboring excitatory–excitatory interactions determine the strength of long-term potentiation. Furthermore, we show how the interplay between excitatory and inhibitory synapses can account for the quick rise and long-term stability of a variety of synaptic weight profiles, such as orientation tuning and dendritic clustering of co-active synapses. In recurrent neuronal networks, co-dependent plasticity produces rich and stable motor cortex-like dynamics with high input sensitivity. Our results suggest an essential role for the neighborly synaptic interaction during learning, connecting micro-level physiology with network-wide phenomena.}, author = {Agnes, Everton J. and Vogels, Tim P}, issn = {1546-1726}, journal = {Nature Neuroscience}, pages = {964--974}, publisher = {Springer Nature}, title = {{Co-dependent excitatory and inhibitory plasticity accounts for quick, stable and long-lasting memories in biological networks}}, doi = {10.1038/s41593-024-01597-4}, volume = {27}, year = {2024}, } @article{15258, abstract = {Inclusion at academic events is facing increased scrutiny as the communities these events serve raise their expectations for who can practically attend. Active efforts in recent years to bring more diversity to academic events have brought progress and created momentum. However, we must reflect on these efforts and determine which underrepresented groups are being disadvantaged. Inclusion at academic events is important to ensure diversity of discourse and opinion, to help build networks, and to avoid academic siloing. All of these contribute to the development of a robust and resilient academic field. We have developed these Ten Simple Rules both to amplify the voices that have been speaking out and to celebrate the progress of many Equity, Diversity, and Inclusivity practices that continue to drive the organisation of academic events. The Rules aim to raise awareness as well as provide actionable suggestions and tools to support these initiatives further. This aims to support academic organisations such as the Deep Learning Indaba, Neuromatch Academy, the IBRO-Simons Computational Neuroscience Imbizo, Biodiversity Information Standards (TDWG), Arabs in Neuroscience, FAIRPoints, and OLS (formerly Open Life Science). This article is a call to action for organisers to reevaluate the impact and reach of their inclusive practices.}, author = {Hall, Siobhan Mackenzie and Kochin, Daniel and Carne, Carmel and Herterich, Patricia and Lewers, Kristen Lenay and Abdelhack, Mohamed and Ramasubramanian, Arun and Michael Alphonse, Juno Felecia and Ung, Visotheary and El-Gebali, Sara and Currin, Christopher and Plomp, Esther and Thompson, Rachel and Sharan, Malvika}, issn = {1553-7358}, journal = {PLOS Computational Biology}, number = {3}, publisher = {Public Library of Science}, title = {{Ten simple rules for pushing boundaries of inclusion at academic events}}, doi = {10.1371/journal.pcbi.1011797}, volume = {20}, year = {2024}, } @article{15295, abstract = {Background: Benzodiazepines and antidepressants are effective agents for the treatment of generalized anxiety disorder (GAD), with the HAM-A frequently used as a primary outcome measure. The GAD literature is inconsistent regarding which medications are more effective for somatic versus psychic symptoms of GAD, and treatment guidelines do not advocate for prescribing based on subtype. This meta-analysis aimed to determine whether benzodiazepines and antidepressants have a differential impact on the somatic versus psychic subscales of the HAM-A in GAD. Methods: An electronic search was undertaken for randomized controlled trials of either benzodiazepines or antidepressants for GAD that reported treatment response using the HAM-A subscales. Data were extracted by independent reviewers. A random effects assessment of weighted mean difference with 95% confidence intervals and subgroup difference was applied. All analysis was done on SPSS 26. An assessment of bias, and of quality of evidence was performed. Results: 24 randomized controlled trials met the inclusion criteria: 18 antidepressant trials, 5 benzodiazepine trials and 1 of both. 14 studies were assessed as having between some and high risk of bias, while 10 were assessed as having low risk of bias. Benzodiazepines (WMD of 1.81 [CI 1.03, 2.58]) were significantly more effective than antidepressants (WMD of 0.83 [CI 0.64, 1.02]) for reducing somatic symptoms of GAD (Chi2 = 5.81, p = 0.02), and were also more effective (WMD of 2.46 [CI 1.83, 3.09]) in reducing psychic symptoms than antidepressants (WMD of 1.83 [CI 1.55, 2.10]), although this comparison did not reach statistical significance (Chi2 = 3.31, p = 0.07). Conclusion: The finding that benzodiazepines were significantly more effective than antidepressants for somatic symptoms needs to be weighed up against potential benefits of antidepressants over benzodiazepines. It may be useful for future treatment guidelines for GAD to explicitly consider symptom subtype.}, author = {Beyer, Chad and Currin, Christopher and Williams, Taryn and Stein, Dan J.}, issn = {1532-8384}, journal = {Comprehensive Psychiatry}, publisher = {Elsevier}, title = {{Meta-analysis of the comparative efficacy of benzodiazepines and antidepressants for psychic versus somatic symptoms of generalized anxiety disorder}}, doi = {10.1016/j.comppsych.2024.152479}, volume = {132}, year = {2024}, } @article{17092, abstract = {Memories are thought to be stored in neural ensembles known as engrams that are specifically reactivated during memory recall. Recent studies have found that memory engrams of two events that happened close in time tend to overlap in the hippocampus and the amygdala, and these overlaps have been shown to support memory linking. It has been hypothesized that engram overlaps arise from the mechanisms that regulate memory allocation itself, involving neural excitability, but the exact process remains unclear. Indeed, most theoretical studies focus on synaptic plasticity and little is known about the role of intrinsic plasticity, which could be mediated by neural excitability and serve as a complementary mechanism for forming memory engrams. Here, we developed a rate-based recurrent neural network that includes both synaptic plasticity and neural excitability. We obtained structural and functional overlap of memory engrams for contexts that are presented close in time, consistent with experimental and computational studies. We then investigated the role of excitability in memory allocation at the network level and unveiled competitive mechanisms driven by inhibition. This work suggests mechanisms underlying the role of intrinsic excitability in memory allocation and linking, and yields predictions regarding the formation and the overlap of memory engrams.}, author = {Delamare, Geoffroy and Feitosa Tomé, Douglas and Clopath, Claudia}, issn = {1529-2401}, journal = {Journal of Neuroscience}, number = {21}, publisher = {Society for Neuroscience}, title = {{Intrinsic neural excitability biases allocation and overlap of memory engrams}}, doi = {10.1523/JNEUROSCI.0846-23.2024}, volume = {44}, year = {2024}, } @unpublished{19520, abstract = {Vertebrates exhibit a wide range of motor behaviors, ranging from swimming to complex limb-based movements. Here we take advantage of frog metamorphosis, which captures a swim-to-limb-based movement transformation during the development of a single organism, to explore changes in the underlying spinal circuits. We find that the tadpole spinal cord contains small and largely homogeneous populations of motor neurons (MNs) and V1 interneurons (V1s) at early escape swimming stages. These neuronal populations only modestly increase in number and subtype heterogeneity with the emergence of free swimming. In contrast, during frog metamorphosis and the emergence of limb movement, there is a dramatic expansion of MN and V1 interneuron number and transcriptional heterogeneity, culminating in cohorts of neurons that exhibit striking molecular similarity to mammalian motor circuits. CRISPR/Cas9-mediated gene disruption of the limb MN and V1 determinants FoxP1 and Engrailed-1, respectively, results in severe but selective deficits in tail and limb function. Our work thus demonstrates that neural diversity scales exponentially with increasing behavioral complexity and illustrates striking evolutionary conservation in the molecular organization and function of motor circuits across species.}, author = {Vijatovic, David and Toma, Florina Alexandra and Harrington, Zoe P and Sommer, Christoph M and Hauschild, Robert and Trevisan, Alexandra J. and Chapman, Phillip and Julseth, Mara and Brenner-Morton, Susan and Gabitto, Mariano I. and Dasen, Jeremy S. and Bikoff, Jay B. and Sweeney, Lora Beatrice Jaeger}, booktitle = {bioRxiv}, title = {{Spinal neuron diversity scales exponentially with swim-to-limb transformation during frog metamorphosis}}, doi = {10.1101/2024.09.20.614050}, year = {2024}, } @article{17886, abstract = {Thin pancake-like neuronal networks cultured on top of a planar microelectrode array have been extensively tried out in neuroengineering, as a substrate for the mobile robot’s control unit, i.e., as a cyborg’s brain. Most of these attempts failed due to intricate self-organizing dynamics in the neuronal systems. In particular, the networks may exhibit an emergent spatial map of steady nucleation sites (“n-sites”) of spontaneous population spikes. Being unpredictable and independent of the surface electrode locations, the n-sites drastically change local ability of the network to generate spikes. Here, using a spiking neuronal network model with generative spatially-embedded connectome, we systematically show in simulations that the number, location, and relative activity of spontaneously formed n-sites (“the vitals”) crucially depend on the samplings of three distributions: (1) the network distribution of neuronal excitability, (2) the distribution of connections between neurons of the network, and (3) the distribution of maximal amplitudes of a single synaptic current pulse. Moreover, blocking the dynamics of a small fraction (about 4%) of non-pacemaker neurons having the highest excitability was enough to completely suppress the occurrence of population spikes and their n-sites. This key result is explained theoretically. Remarkably, the n-sites occur taking into account only short-term synaptic plasticity, i.e., without a Hebbian-type plasticity. As the spiking network model used in this study is strictly deterministic, all simulation results can be accurately reproduced. The model, which has already demonstrated a very high richness-to-complexity ratio, can also be directly extended into the three-dimensional case, e.g., for targeting peculiarities of spiking dynamics in cerebral (or brain) organoids. We recommend the model as an excellent illustrative tool for teaching network-level computational neuroscience, complementing a few benchmark models.}, author = {Zendrikov, Dmitrii and Paraskevov, Alexander}, issn = {1879-2782}, journal = {Neural Networks}, publisher = {Elsevier}, title = {{The vitals for steady nucleation maps of spontaneous spiking coherence in autonomous two-dimensional neuronal networks}}, doi = {10.1016/j.neunet.2024.106589}, volume = {180}, year = {2024}, } @article{15169, abstract = {Interpretation of extracellular recordings can be challenging due to the long range of electric field. This challenge can be mitigated by estimating the current source density (CSD). Here we introduce kCSD-python, an open Python package implementing Kernel Current Source Density (kCSD) method and related tools to facilitate CSD analysis of experimental data and the interpretation of results. We show how to counter the limitations imposed by noise and assumptions in the method itself. kCSD-python allows CSD estimation for an arbitrary distribution of electrodes in 1D, 2D, and 3D, assuming distributions of sources in tissue, a slice, or in a single cell, and includes a range of diagnostic aids. We demonstrate its features in a Jupyter Notebook tutorial which illustrates a typical analytical workflow and main functionalities useful in validating analysis results.}, author = {Chintaluri, Chaitanya and Bejtka, Marta and Sredniawa, Wladyslaw and Czerwinski, Michal and Dzik, Jakub M. and Jedrzejewska-Szmek, Joanna and Wojciki, Daniel K.}, issn = {1553-7358}, journal = {PLoS Computational Biology}, number = {3}, publisher = {Public Library of Science}, title = {{kCSD-python, reliable current source density estimation with quality control}}, doi = {10.1371/journal.pcbi.1011941}, volume = {20}, year = {2024}, }