Biology subjects

Sadeh, S.

Publications and source records attributed to Sadeh, S..

11 recordsLinked to original sources

bioRxiv2026

Inferring state-dependent functional circuit motifs using higher-order interactions analysis

Analysing higher-order interactions among simultaneously recorded neurons can provide crucial insights into neural network dynamics. Recent technological advances have enabled large-scale, long-term neuronal recordings, but analysis of such datasets often relies on simpler statistics due to computational and statistical challenges in assessing higher-order interactions. Here, we developed CHOIR, an efficient and reliable method for calculating higher-order interactions from large-scale neuronal recordings. We then used the inferred HOIs to uncover the underlying functional connectivity, differentiating between connectivity motifs in the space of pairwise and triplet-wise interactions. We found that this approach could successfully distinguish stationary and running states, sleep and awake states, and neuronal ensembles with distinct activity patterns in mice. Furthermore, we identified potential circuit architectures underlying different higher-order interactions, which we confirmed through simulations of large-scale spiking networks with specific subnetwork connectivity. Applying CHOIR to a causal manipulation dataset further confirmed the role of lateral inhibition, a key inhibitory motif, in generating specific HOI patterns. Our work provides a systematic analysis of higher-order interactions in diverse datasets and suggests that HOIs can reveal circuit motifs underlying neural dynamics across brain areas and brain states.

neuroscience↗

bioRxiv2025

Population activity of mossy fibre axon input to the cerebellar cortex during behaviours

The cerebellum gathers information from the neocortex via the cortico-ponto-cerebellar pathway, and forms sensorimotor associations for predicting movements and their sensory consequences. However, little is known about the functional properties of this major input to the cerebellar cortex. Recordings from individual cerebellar mossy fibre axons (MFAs) have shown that they convey sensory and motor information, but nothing is known about their population code. Here, we report that the population activity of pontine MFAs is heterogeneous, high-dimensional and that different subpopulations of MFAs are active during quiet and active behavioural states. Population activity occupied a substantial fraction of the state space and some MFAs are particularly informative about behaviour. Surprisingly, positively and negatively modulated MFAs are intermingled, suggesting granule cells integrate opposite-signed inputs to generate mixed bidirectional sensorimotor representations. Our results establish that neocortex and cerebellum can communicate with a low redundancy, high capacity, bidirectional population code, which is well-suited for forming sensorimotor associations. Highlights[bullet] Population activity of main mossy fibre axonal input to cerebellar cortex. [bullet]Ponto-cerebellar code is high dimensional during behaviours. [bullet]Behavioural information conveyed by bidirectional population code. [bullet]Modelling predicts heterogeneous response properties of granule cells.

neuroscience↗

bioRxiv2024

A Biologically Inspired Attention Model for Neural Signal Analysis

Understanding how the brain represents sensory information and triggers behavioural responses is a fundamental goal in neuroscience. Recent advances in neuronal recording techniques aim to progress towards this milestone, yet the resulting high-dimensional responses are challenging to interpret and link to relevant variables. Although existing machine learning models propose to do so, they often sacrifice interpretability for predictive power, effectively operating as black boxes. In this work, we introduce SPARKS, a biologically inspired model capable of high decoding accuracy and interpretable discovery within a single framework. SPARKS adapts the self-attention mechanism of large language models to extract information from the timing of single spikes and the sequence in which neurons fire using Hebbian learning. Trained with a criterion inspired by predictive coding to enforce temporal coherence, our model produces low-dimensional latent embeddings that are robust across sessions and animals. By directly capturing the underlying data distribution through a generative encoding-decoding framework, SPARKS exhibits state-of-the-art predictive capabilities across diverse electrophysiology and calcium imaging datasets from the motor, visual and entorhinal cortices. Crucially, the Hebbian coefficients learned by the model are interpretable, allowing us to infer the effective connectivity and recover the known functional hierarchy of the mouse visual cortex. Overall, SPARKS unifies representation learning, high-performance decoding and model interpretability in a single framework by bridging neuroscience and AI, providing a powerful and versatile tool for dissecting neural computations and marking a step towards the next generation of biologically inspired intelligent systems.

neuroscience↗

bioRxiv2023

Functional networks of inhibitory neurons orchestrate synchrony in the hippocampus

Inhibitory interneurons are pivotal components of cortical circuits. Beyond providing inhibition, they have been proposed to coordinate the firing of excitatory neurons within cell assemblies. While the roles of specific interneuron subtypes have been extensively studied, their influence on pyramidal cell synchrony in vivo remains elusive. Employing an all-optical approach, we simultaneously recorded hippocampal interneurons and pyramidal cells and probed the network influence of individual interneurons using optogenetics. We demonstrate that CA1 interneurons form a functionally interconnected network that promotes synchrony through disinhibition during awake immobility, while preserving endogenous cell assemblies. Our network model underscores the importance of both cell assemblies and dense, unspecific interneuron connectivity in explaining our experimental findings, suggesting that interneurons may operate not only via division of labor but also through concerted activity.

neuroscience↗

bioRxiv2022

Transcriptional control of adipogenesis by PATZ1

White adipose tissues (WAT) play a central role in lipid storage and systemic energy, lipid, and glucose homeostasis. Understanding the intricacies of adipocyte formation could inform therapies for obesity and metabolic disorders. We have identified the POZ/BTB and AT Hook Containing Zinc Finger 1 (PATZ1) protein as an adipogenic transcription factor through an unbiased high-throughput cDNA screen for modulators of adipogenesis. PATZ1 is expressed by both human and mouse adipocyte precursor cells (APC) and adipocytes, and its expression negatively correlates with obesity. In cell models, PATZ1 expression promotes adipogenesis through a mechanism dependent on protein-protein interaction and DNA binding. Adipose-specific ablation of PATZ1 in mice leads to decreased lipid storage and fat mass and protection from diet-induced obesity. Genome-wide PATZ1 DNA binding analyses using ChIP-Seq suggest that PATZ1 facilitates adipogenesis through interactions with transcription factor machinery at the promoter regions of key adipogenic factors and histone modifiers. Global PATZ1 protein interaction studies using immunoprecipitation-mass spectrometry (IP-MS) suggest that General Transcription Factor 2I (GTF2I) binding to PATZ1 forms a repressive complex, and knockdown of GTF2I augments PATZ1 adipogenic function. Collectively these findings identify PATZ1 as a regulator of the adipocyte differentiation programs and advance our understanding of the complex transcriptional mechanisms underlying adipose tissue development and homeostasis.

molecular biology↗

bioRxiv2022

Dynamic and selective engrams emerge with memory consolidation

Episodic memories are encoded by sparse populations of neurons activated during an experience.1 These neural ensembles constitute memory engrams that are both necessary and sufficient for inducing recall even long after memory acquisition.2 This suggests that following encoding, engrams are stabilized to reliably support memory retrieval. However, little is known about the temporal evolution of engrams over the course of memory consolidation or how it impacts mnemonic properties. Here we employed computational and experimental approaches to examine how the composition and selectivity of engrams change with memory consolidation. We modeled engram cells using a spiking recurrent neural network that yielded three testable predictions: memories transition from unselective to selective as neurons are removed from and added to the engram, inhibitory activity during recall is essential for memory selectivity, and inhibitory synaptic plasticity during memory consolidation is critical for engrams to become selective. Using the Cal-Light system to tag activated neurons in vivo with high spatiotemporal precision3 as well as optogenetic and chemogenetic techniques, we conducted contextual fear conditioning experiments that supported each of our models predictions. Our results reveal that engrams are dynamic even within hours of memory consolidation and that changes in engram composition mediated by inhibitory synaptic plasticity are crucial for the emergence of memory selectivity. These findings challenge classical theories of stable memory traces and point to a close link between engram state and memory expression.

neuroscience↗

bioRxiv2022

Contribution of behavioural variability to representational drift

Neuronal responses to similar stimuli change dynamically over time, raising the question of how internal representations can provide a stable substrate for neural coding. Recent work has suggested a large degree of drift in neural representations even in sensory cortices, which are believed to store stable representations of the external world. While the drift of these representations is mostly characterized in relation to external stimuli, the behavioural state of the animal (for instance, the level of arousal) is also known to strongly modulate the neural activity. We therefore asked how the variability of such modulatory mechanisms can contribute to representational changes. We analysed large-scale recording of neural activity from the Allen Brain Observatory, which was used before to document representational drift in the mouse visual cortex. We found that, within these datasets, behavioural variability significantly contributes to representational changes. This effect was broadcasted across various cortical areas in the mouse, including the primary visual cortex, higher order visual areas, and even regions not primarily linked to vision like hippocampus. Our computational modelling suggests that these results are consistent with independent modulation of neural activity by behaviour over slower time scales. Importantly, our analysis suggests that reliable but variable modulation of neural representations by behaviour can be misinterpreted as representational drift, if neuronal representations are only characterized in the stimulus space and marginalised over behavioural parameters.

neuroscience↗

bioRxiv2021

Excitatory-inhibitory balance modulates the formation and dynamics of neuronal assemblies in cortical networks

Repetitive activation of subpopulation of neurons in cortical networks leads to the formation of neuronal assemblies, which can guide learning and behavior. Recent technological advances have made the artificial induction of such assemblies feasible, yet how various patterns of activation can shape their emergence in different operating regimes is not clear. Here we studied this question in large-scale cortical networks composed of excitatory (E) and inhibitory (I) neurons. We found that the dynamics of the network in which neuronal assemblies are embedded is important for their induction. In networks with strong E-E coupling at the border of E-I balance, increasing the number of perturbed neurons enhanced the potentiation of ensembles. This was, however, accompanied by off-target potentiation of connections from unperturbed neurons. When strong E-E connectivity was combined with dominant E-I interactions, formation of ensembles became specific. Counter-intuitively, increasing the number of perturbed neurons in this regime decreased the average potentiation of individual synapses, leading to an optimal assembly formation at intermediate sizes. This was due to potent lateral inhibition in this regime, which also slowed down the formation of neuronal assemblies, resulting in a speed-accuracy trade-off in the performance of the networks in pattern completion and behavioral discrimination. Our results therefore suggest that the two regimes might be suited for different cognitive tasks, with fast regimes enabling crude detections and slow but specific regimes favoring finer discriminations. Functional connectivity inferred from recent experiments in mouse cortical networks seems to be consistent with the latter regime, but we show that recurrent and top-down mechanisms can dynamically modulate the networks to switch between different states. Our work provides a framework to study how neuronal perturbations can lead to network-wide plasticity under biologically realistic conditions, and sheds light on the design of future experiments to optimally induce behaviorally relevant neuronal assemblies.

neuroscience↗

bioRxiv2020

Coordinated hippocampal-thalamic-cortical communication crucial for engram dynamics underneath systems consolidation

Systems consolidation refers to the reorganization of memory over time across brain regions. Despite recent advancements in unravelling engrams and circuits essential for this process, the exact mechanisms behind engram cell dynamics and the role of associated pathways remain poorly understood. Here, we propose a computational model to address this knowledge gap that consists of a multi-region spiking recurrent neural network subject to biologically-plausible synaptic plasticity mechanisms. By coordinating the timescales of synaptic plasticity throughout the network and incorporating a hippocampus-thalamus-cortex circuit, our model is able to couple engram reactivations across these brain regions and thereby reproduce key dynamics of cortical and hippocampal engram cells along with their interdependencies. Decoupling hippocampal-thalamic-cortical activity disrupts engram dynamics and systems consolidation. Our modeling work also yields several testable predictions: engram cells in mediodorsal thalamus are activated in response to partial cues in recent and remote recall and are crucial for systems consolidation; hippocampal and thalamic engram cells are essential for coupling engram reactivations between subcortical and cortical regions; inhibitory engram cells have region-specific dynamics with coupled reactivations; inhibitory input to mediodorsal thalamus is critical for systems consolidation; and thalamocortical synaptic coupling is predictive of cortical engram dynamics and the retrograde amnesia pattern induced by hippocampal damage. Overall, our results suggest that systems consolidation emerges from concerted interactions among engram cells in distributed brain regions enabled by coordinated synaptic plasticity timescales in multisynaptic subcortical-cortical circuits.

neuroscience↗

bioRxiv2020

Theory of Neuronal Perturbome: Linking Connectivity to Coding via Perturbations

To unravel the functional properties of the brain, we need to untangle how neurons interact with each other and coordinate in large-scale recurrent networks. One way to address this question is to measure the functional influence of individual neurons on each other by perturbing them in vivo. Application of such single-neuron perturbations in mouse visual cortex has recently revealed feature-specific suppression between excitatory neurons, despite the presence of highly specific excitatory connectivity, which was deemed to underlie feature-specific amplification. Here, we studied which connectivity profiles are consistent with these seemingly contradictory observations, by modelling the effect of single-neuron perturbations in large-scale neuronal networks. Our numerical simulations and mathematical analysis revealed that, contrary to the prima facie assumption, neither inhibition-dominance nor broad inhibition alone were sufficient to explain the experimental findings; instead, strong and functionally specific excitatory-inhibitory connectivity was necessary, consistent with recent findings in the primary visual cortex of rodents. Such networks had a higher capacity to encode and decode natural images in turn, which was accompanied by the emergence of response gain nonlinearities at the population level. Our study provides a general computational framework to investigate how single-neuron perturbations are linked to cortical connectivity and sensory coding, and paves the road to map the perturbome of neuronal networks in future studies.

neuroscience↗

bioRxiv2017

Assessing the role of inhibition in stabilizing neocortical networks requires large-scale perturbation of the inhibitory population

Neurons within cortical microcircuits are interconnected with recurrent excitatory synaptic connections that are thought to amplify signals (Douglas and Martin, 2007), form selective subnetworks (Ko et al., 2011) and aid feature discrimination. Strong inhibition (Haider et al., 2013) counterbalances excitation, enabling sensory features to be sharpened and represented by sparse codes (Willmore et al., 2011). This \"balance\" between excitation and inhibition makes it difficult to assess the strength, or gain, of recurrent excitatory connections within cortical networks, which is key to understanding their operational regime and the computations they perform. Networks of neurons combining an unstable high-gain excitatory population with stabilizing inhibitory feedback are known as inhibition-stabilized networks (ISNs; Tsodyks et al. 1997). Theoretical studies using reduced network models predict that ISNs produce paradoxical responses to perturbation, but experimental perturbations failed to find evidence for ISNs in cortex (Atallah et al., 2012). We re-examined this question by investigating how cortical network models consisting of many neurons behave following perturbations, and found that results obtained from reduced network models fail to predict responses to perturbations in more realistic networks. Our models predict that a large proportion of the inhibitory network must be perturbed in order to robustly detect an ISN regime in cortex. We propose that wide-field optogenetic suppression of inhibition under a promoter targeting all inhibitory neurons may provide a perturbation of sufficient strength to reveal the operating regime of cortex. Our results suggest that detailed computational models of optogenetic perturbations are necessary to interpret the results of experimental paradigms.\n\nSignificance statementMany useful computational mechanisms proposed for cortex require local excitatory recurrence to be very strong, such that local inhibitory feedback is necessary to avoid epileptiform runaway activity (an \"inhibition-stabilized network\" or \"ISN\" regime). However, recent experimental results suggest this regime may not exist in cortex. We simulated activity perturbations in cortical networks of increasing realism, and found that in order to detect ISN-like properties in cortex, large proportions of the inhibitory population must be perturbed. Current experimental methods for inhibitory perturbation are unlikely to satisfy this requirement, implying that existing experimental observations are inconclusive about the computational regime of cortex. Our results suggest that new experimental designs, targetting a majority of inhibitory neurons, may be able to resolve this question.

neuroscience↗