Biology subjects

Schneidman, E.

Publications and source records attributed to Schneidman, E..

12 recordsLinked to original sources

bioRxiv2026

Learning the wiring rules of a mammalian cortical column

Characterization of neural circuits architecture typically relies on measurable neuronal features such as morphology, molecular identity, and spatial location. While generative models lever-aging these properties have proven accurate, they remain constrained by available measurements and our assumptions regarding the prospective features. Here, we present an alternative approach using representational learning and use it to model the circuitry of a column of the mouse primary visual cortex. Our framework learns jointly low-dimensional embeddings of neurons in an abstract feature space alongside wiring rules that predict synaptic connectivity. These embedding-based models accurately predict individual synapses, connectivity degrees, and network motif statistics -- outperforming standard generative models that depend on detailed cell-type classifications -- using only a handful of embedding dimensions and wiring rules. Crucially, the learned representations prove interpretable, recapitulating cortical depth, cell type, and dendritic morphology. The resulting wiring blueprint is both simple and biologically meaningful, suggesting that cortical connectivity follows surprisingly parsimonious logic. This framework offers a general and exportable tool for learning minimal generative models of connectomes.

neuroscience↗

bioRxiv2024

A Blueprint of Sex-Specific Neuronal Regulation in the C. elegans Nervous System at Single-Cell Resolution

Sex-specific behaviors are often attributed to differences in neuronal wiring and molecular composition, yet how genetic sex shapes the molecular architecture of the nervous system at the individual neuron level remains unclear. Here, we use single-cell RNA sequencing to profile the transcriptome of sex-shared neurons in adult Caenorhabditis elegans males and hermaphrodites. We uncover widespread molecular dimorphism across the nervous system, including in previously unrecognized neuron-types such as the touch receptors. Neuropeptides and signaling-related genes exhibit strong sex-biased expression, particularly in males, reinforcing the notion that neuropeptides are crucial for diversifying connectome outputs. Despite these differences, neurotransmitter identities remain largely conserved, indicating that functional dimorphism arises through modulatory, not identity-defining, changes. We show that sex-biased expression of neurotransmitter-related genes correlates with bias in outgoing synaptic connectivity and identify regulatory candidates for synaptic wiring, including both shared and sex-specific genes. This dataset provides a molecular framework for understanding how subtle regulatory differences tune conserved circuits to drive sex-specific behaviors.

neuroscience↗

bioRxiv2024

Building a small brain with a simple stochastic generative model

The architectures of biological neural networks result from developmental processes shaped by genetically encoded rules, biophysical constraints, stochasticity, and learning. Understanding these processes is crucial for comprehending neural circuits structure and function. The ability to reconstruct neural circuits, and even entire nervous systems, at the neuron and synapse level, facilitates the study of the design principles of neural systems and their developmental plan. Here, we investigate the developing connectome of C. elegans using statistical generative models based on simple biological features: neuronal cell type, neuron birth time, cell body distance, reciprocity, and synaptic pruning. Our models accurately predict synapse existence, degree profiles of individual neurons, and statistics of small network motifs. Importantly, these models require a surprisingly small number of neuronal cell types, which we infer and characterize. We further show that to replicate the experimentally-observed developmental path, multiple developmental epochs are necessary. Validation of our models predictions of the synaptic connections using multiple reconstructions of adult worms suggests that our model identified the fundamental "backbone" of the connectivity graph. The accuracy of the generative statistical models we use here offers a general framework for studying how connectomes develop and the underlying principles of their design.

neuroscience↗

bioRxiv2023

The structure and function of neural connectomes are shaped by a small number of design principles

The map of synaptic connectivity among neurons in the brain shapes the computations that neural circuits may perform. Inferring the design principles of neural connectomes is, therefore, fundamental for understanding brain development and architecture, neural computations, learning, and behavior. Here, we learn probabilistic generative models for the connectomes of the olfactory bulb of zebrafish, part of the mouse visual cortex, and of C. elegans. We show that, in all cases, models that rely on a surprisingly small number of simple biological and physical features are highly accurate in replicating a wide range of properties of the measured circuits. Specifically, they accurately predict the existence of individual synapses and their strength, distributions of synaptic indegree and outdegree of the neurons, frequency of sub-network motifs, and more. Furthermore, we simulate synthetic circuits generated by our model for the olfactory bulb of zebrafish and show that they replicate the computation that the real circuit performs in response to olfactory cues. Finally, we show that specific failures of our models reflect missing design features that we uncover by adding latent features to the model. Thus, our results reflect surprisingly simple design principles of real connectomes in three different systems and species, and offer a novel general computational framework for analyzing connectomes and linking structure and function in neural circuits.

neuroscience↗

bioRxiv2023

Homeostatic synaptic scaling optimizes learning in network models of neural population codes

Studying and understanding the code of large neural populations hinge on accurate statistical models of population activity. A novel class of models, based on learning to weigh sparse non-linear Random Projections (RP) of the population, has demonstrated high accuracy, efficiency, and scalability. Importantly, these RP models have a clear and biologically-plausible implementation as shallow neural networks. We present a new class of RP models that are learned by optimizing the randomly selected sparse projections themselves. This "reshaping" of projections is akin to changing synaptic connections in just one layer of the corresponding neural circuit model. We show that Reshaped RP models are more accurate and efficient than the standard RP models in recapitulating the code of tens of cortical neurons from behaving monkeys. Incorporating more biological features and utilizing synaptic normalization in the learning process, results in accurate models that are more efficient. Remarkably, these models exhibit homeostasis in firing rates and total synaptic weights of projection neurons. We further show that these sparse homeostatic reshaped RP models outperform fully connected neural network models. Thus, our new scalable, efficient, and highly accurate population code models are not only biologically-plausible but are actually optimized due to their biological features. These findings suggest a dual functional role of synaptic normalization in neural circuits: maintaining spiking and synaptic homeostasis while concurrently optimizing network performance and efficiency in encoding information and learning.

neuroscience↗

bioRxiv2021

Sex-specific topology of the nociceptive circuit shapes dimorphic behavior in C. elegans

How sexually dimorphic behavior is encoded in the nervous system is poorly understood. Here, we characterize the dimorphic nociceptive behavior in C. elegans and study the underlying circuits, which are composed of the same neurons but are wired differently. We show that while sensory transduction is similar in the two sexes, the downstream network topology markedly shapes behavior. We fit a network model that replicates the observed dimorphic behavior in response to external stimuli, and use it to predict simple network rewirings that would switch the behavior between the sexes. We then show experimentally that these subtle synaptic rewirings indeed flip behavior. Strikingly, when presented with aversive cues, rewired males were compromised in finding mating partners, suggesting that network topologies that enable efficient avoidance of noxious cues have a reproductive "cost". Our results present a deconstruction of the design of a neural circuit that controls sexual behavior, and how to reprogram it.

neuroscience↗

bioRxiv2020

Learning the architectural features that predict functional similarity of neural networks

The mapping of the wiring diagrams of neural circuits promises to allow us to link structure and function of neural networks. Current approaches to analyzing connectomes rely mainly on graph-theoretical tools, but these may downplay the complex nonlinear dynamics of single neurons and networks, and the way networks respond to their inputs. Here, we measure the functional similarity of simulated networks of neurons, by quantifying the similitude of their spiking patterns in response to the same stimuli. We find that common graph theory metrics convey little information about the similarity of networks responses. Instead, we learn a functional metric between networks based on their synaptic differences, and show that it accurately predicts the similarity of novel networks, for a wide range of stimuli. We then show that a sparse set of architectural features - the sum of synaptic inputs that each neuron receives and the sum of each neurons synaptic outputs - predicts the functional similarity of networks of up to 100 cells, with high accuracy. We thus suggest new architectural design principles that shape the function of neural networks, which conform with experimental evidence of homeostatic mechanisms.

neuroscience↗

bioRxiv2019

Social interactions drive efficient foraging and income equality in groups of fish

The social interactions underlying group foraging and their benefits have been mostly studied using mechanistic models replicating qualitative features of group behavior, and focused on a single resource or a few clustered ones. Here, we tracked groups of freely foraging adult zebrafish with spatially dispersed food items and found that fish perform stereotypical maneuvers when consuming food, which attract neighboring fish. We then present a mathematical model, based on inferred functional interactions between fish, which accurately describes individual and group foraging of real fish. We show that these interactions allow fish to combine individual and social information to achieve near-optimal foraging efficiency and promote income equality within groups. We further show that the interactions that would maximize efficiency in these social foraging models depend on group size, but not on food distribution - suggesting that fish may adaptively pick the subgroup of neighbors they "listen to" to determine their own behavior. Impact statementAnalysis and modeling of group behavior of adult zebrafish shows that a specialized social interaction mechanism increases foraging efficiency and equality within groups, under a variety of environmental conditions.

animal behavior and cognition↗

bioRxiv2019

Strongly correlated spatiotemporal encoding and simple decoding in the prefrontal cortex

We studied the fine temporal structure of spiking patterns of groups of up to 100 simultaneously recorded units in the prefrontal cortex of monkeys performing a visual discrimination task. We characterized the vocabulary of population activity patterns using 10 ms time bins and found that different sets of population activity patterns (codebooks) are used in different task epochs and that spiking correlations between units play a large role in defining those codebooks. Models that ignore those correlations fail to capture the population codebooks in all task epochs. Further, we show that temporal sequences of population activity patterns have strong history-dependence and are governed by different transition probabilities between patterns and different correlation time scales, in the different task epochs, suggesting different computational dynamics governing each epoch. Together, the large impact of spatial and temporal correlations on the dynamics of the population code makes the observed sequences of activity patterns many orders of magnitude more likely to appear than predicted by models that ignore these correlations and rely only on the population rates. Surprisingly, however, models that ignore these correlations perform quite well for decoding behavior from population responses. The difference of encoding and decoding complexity of the neural codebook suggests that one of the goals of the complex encoding scheme in the prefrontal cortex is to accommodate simple decoders that do not have to learn correlations.

neuroscience↗

bioRxiv2019

A geometric representation unveils rule-learning dynamics in primate neurons

Primates can quickly and advantageously adopt new behaviors based on changing stimuli relationships. We studied acquisition of a classification task while recording single neurons in the dorsal-anterior-cingulate-cortex (dACC) and the Striatum. Monkeys performed trial-by-trial classification on a rich set of multi-cue patterns, allowing de-novo learning every few days. To examine neural dynamics during the learning itself, we represent each rule with a spanning set of the space formed by the stimuli features. Because neural preference can be expressed by feature combinations, we can track neural dynamics in geometrical terms in this space, allowing a compact description of neural trajectories by observing changes in either vector-magnitude and/or angle-to- rule. We find that a large fraction of cells in both regions follow the behavior during learning. Neurons in the dACC mainly rotate towards the policy, suggesting an increase in selectivity that approximates the rule; whereas in the Putamen we also find a prominent magnitude increase, suggesting strengthening of confidence. Additionally, magnitude increases in the striatum followed rotation in the dACC. Finally, the neural representation at the end of the session predicted next-day behavior. The use of this novel framework enables tracking of neural dynamics during learning and suggests differential yet complementing roles for these brain regions.

neuroscience↗

bioRxiv2018

Learning probabilistic representations with randomly connected neural circuits

The brain represents and reasons probabilistically about complex stimuli and motor actions using a noisy, spike-based neural code. A key building block for such neural computations, as well as the basis for supervised and unsupervised learning, is the ability to estimate the surprise or likelihood of incoming high-dimensional neural activity patterns. Despite progress in statistical modeling of neural responses and deep learning, current approaches either do not scale to large neural populations or cannot be implemented using biologically realistic mechanisms. Inspired by the sparse and random connectivity of real neuronal circuits, we present a new model for neural codes that accurately estimates the likelihood of individual spiking patterns and has a straightforward, scalable, efficiently learnable, and realistic neural implementation. This models performance on simultaneously recorded spiking activity of >100 neurons in the monkey visual and prefrontal cortices is comparable or better than that of current models. Importantly, the model can be learned using a small number of samples, and using a local learning rule that utilizes noise intrinsic to neural circuits. Slower, structural changes in random connectivity, consistent with rewiring and pruning processes, further improve the efficiency and sparseness of the resulting neural representations. Our results merge insights from neuroanatomy, machine learning, and theoretical neuroscience to suggest random sparse connectivity as a key design principle for neuronal computation.

neuroscience↗

bioRxiv2018

Dynamics of social representation in the mouse prefrontal cortex

The prefrontal cortex (PFC) plays an important role in regulating social functions in mammals, and impairments in this region have been linked with social dysfunction in psychiatric disorders. Yet little is known of how the PFC encodes social information and of how social representations may be altered in such disorders. Here, we show that neurons in the medial PFC (mPFC) of freely behaving mice preferentially respond to socially-relevant sensory cues. Population activity patterns in the mPFC differed considerably between social and nonsocial stimuli and underwent experience-dependent refinement. In Cntnap2 knockout mice, a genetic model of autism, both the categorization of sensory stimuli and the refinement of social representations were impaired. Noise levels in spontaneous population activity were higher in Cntnap2 mice, and correlated strongly with the degree to which social representations were disrupted. Our findings elucidate the encoding of social sensory cues in the mPFC, and provide an important link between altered prefrontal dynamics and autism-associated social dysfunction.

neuroscience↗