Biology subjects

Segev, I.

Publications and source records attributed to Segev, I..

At least 19 recordsLinked to original sources

bioRxiv2026

Connectivity Logic of Dendritic Spines in Cortex: Increased Inputs and Ensemble Formation

Dendritic spines are the principal postsynaptic targets of excitatory synapses, yet the network logic governing their organization and contribution to cortical computation remains unclear. Here, we combine the MICrONS ultrastructural reconstruction of mouse visual cortex with matched in vivo two-photon calcium imaging to explore the network roles of dendritic spines. Structurally, we show that spine density predicts the number and diversity of both excitatory and inhibitory presynaptic partners, supporting the long-standing "connectivity and diversity" hypothesis. Beyond expanding input space, spines exhibit distinct network-level principles: excitatory axons preferentially target spines with increasing axonal distance from the soma; highly broadcasting ("hub") neurons preferentially innervate spines; and neurons sharing presynaptic partners preferentially route their own outputs onto spines, revealing an input-to-spine/output-to-spine wiring correlations. Functionally, we find that these same structural motifs are found in neuronal ensembles, defined as groups of neurons with correlated calcium activity. Ensemble members are interconnected at more than twice the rate of spatially matched controls; these synapses connecting them are routed almost exclusively onto dendritic spines; and their shared presynaptic partners are almost exclusively inhibitory neurons that themselves also preferentially target spines. We suggest that local recurrent spine-targeted excitation effectively binds ensemble members into a coactive subnetwork, while shared spine-directed inhibition gates, synchronizes, and stabilizes their collective activity, providing a circuit-level architecture that may support attractor dynamics, pattern completion, and other cortical computations.

neuroscience↗

bioRxiv2026

What can a neuron compute

Cortical pyramidal neurons possess elaborate dendritic trees with diverse nonlinear membrane conductances and thousands of plastic synapses, suggesting substantial computational capabilities at the single-cell level. Yet, what can a neuron compute remains an open question, largely due to the lack of a systematic framework to quantify its computational capabilities. We introduce TwinProp, a digital-twin-based backpropagation algorithm that enables gradient-based optimization of synaptic strengths and dendritic locations in detailed neuron models via a millisecond-accurate deep neural network (DNN). Using TwinProp, we demonstrate that a detailed model of rat layer 5 pyramidal cell (L5PC) can perform naturalistic image and audio classification tasks at a remarkably high accuracy, significantly surpassing perceptron and leaky integrate-and-fire baselines. The same neuron solves high-dimensional nonlinear problems, including exclusive-or (XOR), 10-bit parity, and random Boolean tasks, demonstrating capabilities typically attributed to multilayer networks. Mechanistically, increasing task complexity recruits distributed dendritic nonlinearities, including NMDA- and voltage-dependent mechanisms; removing these or collapsing dendritic structure markedly impairs performance. These findings identify dendrites as a substrate for high-order feature binding and position single cortical pyramidal neurons as powerful, noise-robust, general-purpose analog computational units. Our results offer testable in vivo predictions and provide a systematic framework linking cellular morpho-electrical properties to computation in both brains and artificial systems.

neuroscience↗

bioRxiv2026

Spine-neck electrical bottlenecks tune temporal precision and inhibitory gating in cortical pyramidal neurons: A connectomics-based biophysical study

Dendritic spines are nano-scale compartments that host the majority of excitatory synapses on cortical pyramidal neurons (PNs); hundreds of spines/PN also receive an inhibitory synapse (dually-innervated spines, DiSs). Using analytic theory and detailed biophysical models of [~]2,000 densely reconstructed spines, we show that recurrent spine-neck narrowing substantially increases spine-neck resistance (Rneck), and that elevated Rneck accelerates spine-head voltage dynamics, shortening spinous postsynaptic potentials by up to [~]3-fold. Rneck improves the tracking of high-frequency synaptic inputs and strongly modulates Ca2+ signaling and the potency and temporal precision of inhibitory gating in DiSs. This work identifies Rneck as a dynamic "knob", directly linking spine ultrastructure to information processing and plasticity in cortical PNs and circuits, yielding testable experimental predictions. Our "biophysics of connectomics" paradigm naturally raises computational-oriented questions, including how inhibitory "gates" in dendritic spines expand context-dependent computations, implement single-cell and network-level routing, and enable selective encoding of precise temporal patterns.

neuroscience↗

bioRxiv2026

Uncovering the Basis of Human ConnectomeComplexity: The Role of Neuronal Morphology

Comparative studies have established differences between the electrophysiology and anatomy of human and rodent cortical circuits. A consistent finding is that human neuronal morphologies display more elaborate neurite shapes than those of rodents, a feature that cannot be accounted for merely by their larger size according to recent findings. Here, we study the impact of these neurite shapes on the structure of synaptic connectivity in their local microcircuitry. Our approach is based on the idea that axonal and dendritic geometries constrain the locations of afferent and efferent synaptic contacts (potential connectivity). Although the mechanisms by which potential connectivity translates into actual synaptic connectivity are manifold and complex, the potential connectivity is nevertheless highly informative for the final structure of a biological connectome. We found that connectomes predicted from human reconstructed morphologies have higher complexity according to several measures that have been demonstrated to be functionally relevant. Going beyond a simple comparison, we demonstrate mechanistically how the shapes of neuron morphologies give rise to non-random and clustered structures observed in experimentally measured connectomes, and how the specific shapes of human neurons strengthen the process. Finally, we conceptually examine how synapse formation processes may interact with potential connectivity, showing that a process compatible with Hebbian plasticity leads to the highest complexity and best match experimentally observed patterns.

neuroscience↗

bioRxiv2026

A Detailed Model for Understanding the Human Neocortex

The neocortex underlies cognitive abilities that set humans apart from other species. Although Ramon y Cajal initiated its study in the 19th century, much about its fundamental properties remain poorly understood. Biologically detail modeling, has been shown to serve as a tool to understand the modeled system better. By comparing computational models for different species we can highlight functional differences between them, find their anatomical or physiological basis and thus improve our understanding of cortical function. In this study we built a detailed model of a human cortical microcircuit following an established workflow. We compared the human data and results against a previously published reconstruction of rat cortical circuitry. To parametrize the human model, we gathered new original data on human morphological reconstructions, axonal bouton densities, single cell and synaptic recordings. We combined them with data available in the literature and open-sourced databases. We also developed various strategies to overcome the missing data, such as generalizing or adapting data from rodents. The resulting model consists of seven columnar units with similar characteristics. Each column has a radius of 476 {micro}m, a height of 2622 {micro}m, a volume of 1.86 mm3, a total cell density of 24,186 cells/mm3, on the order of 35,000 cells, around 12 million connections and approximately 47 million synapses. Comparing the rat and the human model showed that the human cortex is less dense in terms of cell bodies than the rodent cortex. Human cells have more complex branching, but lower bouton densities than rodent cells. However, the number of connections between cell types is similar.

neuroscience↗

bioRxiv2024

What makes human cortical pyramidal neurons functionally complex

Humans exhibit unique cognitive abilities within the animal kingdom, but the neural mechanisms driving these advanced capabilities remain poorly understood. Human cortical neurons differ from those of other species, such as rodents, in both their morphological and physiological characteristics. Could the distinct properties of human cortical neurons help explain the superior cognitive capabilities of humans? Understanding this relationship requires a metric to quantify how neuronal properties contribute to the functional complexity of single neurons, yet no such standardized measure currently exists. Here, we propose the Functional Complexity Index (FCI), a generalized, deep learning-based framework to assess the input-output complexity of neurons. By comparing the FCI of cortical pyramidal neurons from different layers in rats and humans, we identified key morpho-electrical factors that underlie functional complexity. Human cortical pyramidal neurons were found to be significantly more functionally complex than their rat counterparts, primarily due to differences in dendritic membrane area and branching pattern, as well as density and nonlinearity of NMDA-mediated synaptic receptors. These findings reveal the structural-biophysical basis for the enhanced functional properties of human neurons.

neuroscience↗

bioRxiv2024

The Calcitron: A Simple Neuron Model That Implements Many Learning Rules via the Calcium Control Hypothesis

Theoretical neuroscientists and machine learning researchers have proposed a variety of learning rules for linear neuron models to enable artificial neural networks to accomplish supervised and unsupervised learning tasks. It has not been clear, however, how these theoretically-derived rules relate to biological mechanisms of plasticity that exist in the brain, or how the brain might mechanistically implement different learning rules in different contexts and brain regions. Here, we show that the calcium control hypothesis, which relates plastic synaptic changes in the brain to calcium concentration [Ca2+] in dendritic spines, can reproduce a wide variety of learning rules, including some novel rules. We propose a simple, perceptron-like neuron model that has four sources of [Ca2+]: local (following the activation of an excitatory synapse and confined to that synapse), heterosynaptic (due to activity of adjacent synapses), postsynaptic spike-dependent, and supervisor-dependent. By specifying the plasticity thresholds and amount of calcium derived from each source, it is possible to implement Hebbian and anti-Hebbian rules, one-shot learning, perceptron learning, as well as a variety of novel learning rules.

neuroscience↗

bioRxiv2023

Morpho-electric and computational properties of three types of human hippocampal CA1 pyramidal neurons

Hippocampal pyramidal neuron activity underlies episodic memory and spatial navigation. Although extensively studied in rodents, extremely little is known about human hippocampal pyramidal neurons, even though human hippocampus underwent strong evolutionary reorganization and shows lower theta rhythm frequencies. To test whether biophysical and computational properties of human CA1 pyramidal neurons can explain observed rhythms, we map the morpho-electric and computational properties of individual CA1 pyramidal neurons in human, non-pathological hippocampal slices from neurosurgery. Human CA1 pyramidal neurons have extensive dendrites and resonate at 2.9 Hz, optimally tuned to human theta frequencies. Morphological and biophysical properties reveal three cell types with distinct dendrite bifurcations and physiology. Data-driven biophysical models show that human CA1 pyramidal neurons use i) computationally independent dendritic compartments, ii) preferential routing of electrical activity towards soma or dendritic tree and iii) non-linear input-output transformations. Across cell types, morpho-electric properties consistently increase computational richness in human CA1 pyramidal neurons. TeaserHuman CA1 pyramidal neurons have large and intricate morphologies, which translates to complex computational properties.

neuroscience↗

bioRxiv2023

Of mice and men: Increased dendritic complexity gives rise to unique human networks

The organizational principles that distinguish the human brain from other species have been a long-standing enigma in neuroscience. Focusing on the uniquely evolved human cortical layers 2 and 3, we computationally reconstruct the cortical architecture for mice and humans. We show that human pyramidal cells form highly complex networks, demonstrated by the increased number and simplex dimension compared to mice. This is surprising because human pyramidal cells are much sparser in the cortex. We show that the number and size of neurons fail to account for this increased network complexity, suggesting that another morphological property is a key determinant of network connectivity. Topological comparison of dendritic structure reveals much higher perisomatic (basal and oblique) branching density in human pyramidal cells. Using topological tools we quantitatively show that this neuronal structural property directly impacts network complexity, including the formation of a rich subnetwork structure. We conclude that greater dendritic complexity, a defining attribute of human L2 and 3 neurons, may provide the human cortex with enhanced computational capacity and cognitive flexibility. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/557170v3_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@c0adadorg.highwire.dtl.DTLVardef@1ca4aaeorg.highwire.dtl.DTLVardef@1ecf1b5org.highwire.dtl.DTLVardef@f0d44d_HPS_FORMAT_FIGEXP M_FIG C_FIG A. A multiscale analysis was performed to compare the mouse and human brains: from the anatomical properties of brain regions to the morphological details of single neurons. B. Human circuits are larger than mice in terms of size and number of neurons, but present decreased neuron density, resulting in increased distances between neurons, particularly among pyramidal cells. C. Greater network complexity emerges within the human brain. Network complexity is defined by larger groups of neurons forming complex interconnections throughout the network. D. The topological analysis of layer 2/3 pyramidal cells in the temporal cortex reveals an intriguing difference: human neurons exhibit a significantly larger number of dendritic branches, especially near the cell body compared to mice. This phenomenon is termed "higher topological complexity" in dendrites. Our findings suggest that dendritic complexity wields a more substantial influence on network complexity than neuron density.

neuroscience↗

bioRxiv2023

A Generalized Framework for Calcium-Based Plasticity Describes Weight-Dependent Synaptic Changes in Behavioral Time Scale Plasticity

The brain modifies synaptic strengths to store new information via long-term potentiation (LTP) and long-term depression (LTD). Evidence has mounted that long-term plasticity is controlled via concentrations of calcium ([Ca2+]) in postsynaptic spines. Several mathematical models describe this phenomenon, including those of Shouval, Bear, and Cooper (SBC) (Shouval et al., 2002, 2010) and Graupner and Brunel (GB)(Graupner & Brunel, 2012). Here we suggest a generalized version of the SBC and GB models, based on a fixed point - learning rate (FPLR) framework, where the synaptic [Ca2+] specifies a fixed point toward which the synaptic weight approaches asymptotically at a [Ca2+]-dependent rate. The FPLR framework offers a straightforward phenomenological interpretation of calcium-based plasticity: the calcium concentration tells the synaptic weight where it is going and how fast it goes there. The FPLR framework can flexibly incorporate various experimental findings, including the existence of multiple regions of [Ca2+] where no plasticity occurs, or plasticity in cerebellar Purkinje cells, where the directionality of calcium-based synaptic changes is thought to be reversed relative to cortical and hippocampal neurons. We also suggest a modeling approach that captures the dependency of late-phase plasticity stabilization on protein synthesis. We demonstrate that due to the asymptotic, saturating nature of synaptic changes in the FPLR rule, the result of frequency- and spike-timing-dependent plasticity protocols are weight-dependent. Finally, we show how the FPLR framework can explain plateau potential-induced place field formation in hippocampal CA1 neurons, also known as behavioral time scale plasticity (BTSP).

neuroscience↗

bioRxiv2022

Accelerated signal propagation speed in human neocortical microcircuits

Human-specific cognitive abilities depend on information processing in the cerebral cortex, where the neurons are significantly larger and their processes longer and sparser compared to rodents. We found that, in synaptically connected layer 2/3 pyramidal cells (L2/3 PCs), the delay in signal propagation from soma to soma is similar in humans and rodents. To compensate for the longer processes of neurons, membrane potential changes in human axons and/or dendrites must propagate faster. Dual somato-dendritic and somato-axonal patch recordings show that the propagation speed of action potentials (APs) is similar in human and rat axons, but the forward propagation of excitatory postsynaptic potentials (EPSPs) and the backward propagation of APs are 26 and 47% faster in human dendrites, respectively. Accurate biophysical models of human and rat L2/3 PCs, combined with pharmacological manipulations of membrane properties, have shown that various factors enhance EPSP propagation in humans; the key factor is the large conductance load imposed by the large basal dendritic tree in humans, the key factor responsible for the accelerated signal propagation in human cortical dendrites. Larger dendritic diameter in humans as well as differences in cable and ion channel properties, also contribute to the enhancement of signal propagation in human L2/3 dendrites.

neuroscience↗

bioRxiv2022

Asymmetric voltage attenuation in dendrites enables hierarchical heterosynaptic plasticity

Long-term synaptic plasticity has been shown to be mediated via cytosolic calcium concentrations ([Ca2+]). Using a synaptic model which implements calcium-based long-term plasticity via two sources of Ca2+, NMDA receptors and voltage-gated calcium channels (VGCCs), we show in dendritic cable simulations, that the interplay between these two calcium sources can result in a diverse array of heterosynaptic effects. When spatially clustered synaptic input produces a local NMDA spike, the resulting dendritic depolarization can activate VGCCs at non-activated spines, resulting in heterosynaptic plasticity. NMDA spike activation at a given dendritic location will tend to depolarize dendritic regions that are located distally to the input site more than dendritic sites that are proximal to it. This asymmetry produces a hierarchical effect in branching dendrites, where an NMDA spike at a proximal branch can induce heterosynaptic plasticity primarily at branches that are distal to it. We also explored how simultaneously activated synaptic clusters located at different dendritic locations synergistically affect the plasticity at these locations, as well as the heterosynaptic plasticity of an inactive synapse "sandwiched" between them. We conclude that the inherent electrical asymmetry of dendritic trees enables sophisticated schemes for spatially targeted supervision of heterosynaptic plasticity.

neuroscience↗

bioRxiv2022

Multiple Synaptic Contacts combined with Dendritic Filtering enhance Spatio-Temporal Pattern Recognition capabilities of Single Neurons

A cortical neuron typically makes multiple synaptic contacts on the dendrites of its postsynaptic target neuron. The functional implications of this apparent redundancy are unclear. Due to dendritic cable filtering, proximal dendritic synapses generate brief somatic postsynaptic potentials (PSPs) whereas distal synapses give rise to broader PSPs. Consequently, with multiple synaptic contacts, a single presynaptic spike results in a somatic PSP composed of multiple temporal profiles. We developed a "Filter-and-Fire" (F&F) neuron model that incorporates multiple contacts and cable filtering; it demonstrates threefold increase in memory capacity as compared to a leaky Integrate-and-Fire (I&F) neuron, when trained to emit precisely timed spikes for specific input patterns. Furthermore, the F&F neuron can learn to recognize spatio-temporal input patterns, e.g., MNIST digits, where the I&F model completely fails. We conclude that "dendro-plexing" single input spikes by multiple synaptic contacts enriches the computational capabilities of cortical neurons and can dramatically reduce axonal wiring.

neuroscience↗

bioRxiv2021

Realistic retinal modeling unravels the differential role of excitation and inhibition to starburst amacrine cells in direction selectivity

Retinal direction-selectivity originates in starburst amacrine cells (SACs), which display a centrifugal preference, responding with greater depolarization to a stimulus expanding from soma to dendrites than to a collapsing stimulus. Various mechanisms were hypothesized to underlie SAC centrifugal preference, but dissociating them is experimentally challenging and the mechanisms remain debatable. To address this issue, we developed the Retinal Stimulation Modeling Environment (RSME), a multifaceted data-driven retinal model that encompasses detailed neuronal morphology and biophysical properties, retina-tailored connectivity scheme and visual input. Using a genetic algorithm, we demonstrated that spatiotemporally diverse excitatory inputs - sustained in the proximal and transient in the distal processes - are sufficient to generate experimentally validated centrifugal preference in a single SAC. Reversing these input kinetics did not produce any centrifugal-preferring SAC. We then explored the contribution of SAC-SAC inhibitory connections in establishing the centrifugal preference. SAC inhibitory network enhanced the centrifugal preference, but failed to generate it in its absence. Embedding a direction selective ganglion cell (DSGC) in a SAC network showed that the known SAC-DSGC asymmetric connectivity by itself produces direction selectivity. Still, this selectivity is sharpened in a centrifugal-preferring SAC network. Finally, we use RSME to demonstrate the contribution of SAC-SAC inhibitory connections in mediating direction selectivity and recapitulate recent experimental findings. Thus, using RSME, we obtained a comprehensive mechanistic understanding of SACs centrifugal preference and its contribution to direction selectivity.

neuroscience↗

bioRxiv2021

The role of hub neurons in modulating cortical dynamics

Many neurodegenerative diseases are associated with the death of specific neuron types in particular brain regions. What makes the death of specific neuron types particularly harmful for the integrity and dynamics of the respective network is not well understood. To start addressing this question we used the most up-to-date biologically-realistic dense neocortical microcircuit (NMC) of rodent, which has reconstructed a volume of 0.3 mm3 and containing 31,000 neurons, 36 million synapses, and 55 morphological cell types arranged in 6 cortical layers. Using modern network science tools, we identified "hub-neurons" in the NMC, that are connected synaptically to a large number of their neighbors and systematically examined the impact of abolishing these cells. In general, the structural integrity of the network is robust to cells attack; yet, attacking hub neurons strongly impacted the "small worldness" topology of the network, whereas similar attacks on random neurons have a negligible effect. Such hub-specific attacks are also impactful on the network dynamics, both when the network is at its spontaneous synchronous state and when it was presented with synchronized thalamo-cortical visual-like input. We found that attacking layer 5 hub neurons are most harmful to the structural and functional integrity of the NMC. The significance of our results for understanding the role of specific neuron types and cortical layers for disease manifestation is discussed.

neuroscience↗

bioRxiv2021

Burst Control

The output of neocortical layer 5 pyramidal cells (L5PCs) is expressed by a train of single spikes with intermittent bursts of multiple spikes at high frequencies. The bursts are the result of nonlinear dendritic properties, including Na+, Ca2+, and NMDA spikes, that interact with the [~]10,000 synapses impinging on the neurons dendrites. Output spike bursts are thought to implement key dendritic computations, such as coincidence detection of bottom-up inputs (arriving mostly at the basal tree) and top-down inputs (arriving mostly at the apical tree). In this study we used a detailed nonlinear model of L5PC receiving excitatory and inhibitory synaptic inputs to explore the conditions for generating bursts and for modulating their properties. We established the excitatory input conditions on the basal versus the apical tree that favor burst and show that there are two distinct types of bursts. Bursts consisting of 3 or more spikes firing at < 200 Hz, which are generated by stronger excitatory input to the basal versus the apical tree, and bursts of [~]2-spikes at [~]250 Hz, generated by prominent apical tuft excitation. Localized and well-timed dendritic inhibition on the apical tree differentially modulates Na+, Ca2+, and NMDA spikes and, consequently, finely controls the burst output. Finally, we explored the implications of different burst classes and respective dendritic inhibition for regulating synaptic plasticity.

neuroscience↗

bioRxiv2020

The Gradient Clusteron: A model neuron that learnsvia dendritic nonlinearities, structural plasticity, and gradient descent

Synaptic clustering on neuronal dendrites has been hypothesized to play an important role in implementing pattern recognition. Neighboring synapses on a dendritic branch can interact in a synergistic, cooperative manner via the nonlinear voltage-dependence of NMDA receptors. Inspired by the NMDA receptor, the single-branch clusteron learning algorithm (Mel 1991) takes advantage of location-dependent multiplicative nonlinearities to solve classification tasks by randomly shuffling the locations of "under-performing" synapses on a model dendrite during learning ("structural plasticity"), eventually resulting in synapses with correlated activity being placed next to each other on the dendrite. We propose an alternative model, the gradient clusteron, or G-clusteron, which uses an analytically-derived gradient descent rule where synapses are "attracted to" or "repelled from" each other in an input- and location- dependent manner. We demonstrate the classification ability of this algorithm by testing it on the MNIST handwritten digit dataset and show that, when using a softmax activation function, the accuracy of the G-clusteron on the All-vs-All MNIST task ([~]85%) approaches that of logistic regression ([~]93%). In addition to the location update rule, we also derive a learning rule for the synaptic weights of the G-clusteron ("functional plasticity") and show that a G-clusteron that utilizes the weight update rule can achieve [~]89% accuracy on the MNIST task. We also show that a G-clusteron with both the weight and location update rules can learn to solve the XOR problem from arbitrary initial conditions.

neuroscience↗

bioRxiv2020

Conserved interactions required for in vitro inhibition of the main protease of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

The COVID-19 pandemic caused by the SARS-CoV-2 requires a fast development of antiviral drugs. SARS-CoV-2 viral main protease (Mpro, also called 3C-like protease, 3CLpro) is a potential target for drug design. Crystal and co-crystal structures of the SARS-CoV-2 Mpro have been solved, enabling the rational design of inhibitory compounds. In this study we analyzed the available SARS-CoV-2 and the highly similar SARS-CoV-1 crystal structures. We identified within the active site of the Mpro, in addition to the inhibitory ligands interaction with the catalytic C145, two key H-bond interactions with the conserved H163 and E166 residues. Both H-bond interactions are present in almost all co-crystals and are likely to occur also during the viral polypeptide cleavage process as suggested from docking of the Mpro cleavage recognition sequence. We screened in silico a library of 6,900 FDA-approved drugs (ChEMBL) and filtered using these key interactions and selected 29 non-covalent compounds predicted to bind to the protease. Additional screen, using DOCKovalent was carried out on DrugBank library (11,414 experimental and approved drugs) and resulted in 6 covalent compounds. The selected compounds from both screens were tested in vitro by a protease activity inhibition assay. Two compounds showed activity at the 50M concentration range. Our analysis and findings can facilitate and focus the development of highly potent inhibitors against SARS-CoV-2 infection.

microbiology↗