Biology subjects

Segev, I.

Publications and source records attributed to Segev, I..

30 records · Page 2Linked to original sources

bioRxiv2020

Morphometric reconstructions atlas shows insult-driven plasticity in cortical VIP/ChAT interneurons

We developed an automatic morphometric reconstruction pipeline, Pop-Rec, and used it to study the morphologies of cortical cholinergic VIP/ChAT interneurons (VChIs). Cholinergic networks control high cognitive functions, but their local modulation and stress-driven plasticity patterns remained elusive. Reconstructing thousands of local VChIs registered to their exact coordinates in multiple cleared murine cortices highlighted distinct populations of bipolar and multipolar VChIs which differed in their dendritic spatial organization. Following mild unilateral whisker deprivation, Pop-Rec found both ipsi-and contra-lateral VChI dendritic arborization changes. Furthermore, RNA-seq of FACS-sorted VChIs showed differentially expressed dendritic, synapse and axon-modulating transcripts in whisker-deprived mice. Indicating novel steady-state morphological roles, those genes also clustered distinctly in naive single cell VChIs. This VChIs "morpheome" atlas is the first example of unbiased analysis of neuronal populations and holds the possibility to compare neuronal structure-function relationships across experimental conditions.

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bioRxiv2020

Dense Computer Replica of Cortical Microcircuits Unravels Cellular Underpinnings of Auditory Surprise Response

The nervous system is notorious for its strong response evoked by a surprising sensory input, but the biophysical and anatomical underpinnings of this phenomenon are only partially understood. Here we utilized in-silico experiments of a biologically-detailed model of a neocortical microcircuit to study stimulus specific adaptation (SSA) in the auditory cortex, whereby the neuronal response adapts significantly for a repeated ("expected") tone but not for a rare ("surprise") tone. SSA experiments were mimicked by stimulating tonotopically-mapped thalamo-cortical afferents projecting to the microcircuit; the activity of these afferents was modeled based on our in-vivo recordings from individual thalamic neurons. The modeled microcircuit expressed naturally many experimentally-observed properties of SSA, suggesting that SSA is a general property of neocortical microcircuits. By systematically modulating circuit parameters, we found that key features of SSA depended on synergistic effects of synaptic depression, spike frequency adaptation and recurrent network connectivity. The relative contribution of each of these mechanisms in shaping SSA was explored, additional SSA-related experimental results were explained and new experiments for further studying SSA were suggested.

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bioRxiv2020

A calcium-based plasticity model predicts long-term potentiation and depression in the neocortex

Long-term potentiation (LTP) and long-term depression (LTD) of pyramidal cell connections are among the key mechanisms underlying learning and memory in the brain. Despite their important role, only a few of these connections have been characterized in terms of LTP/LTD dynamics, such as the one between layer 5 thick-tufted pyramidal cells (L5-TTPCs). Comparing the available evidence on different pyramidal connection types reveals a large variability of experimental outcomes, possibly indicating the presence of connection-type-specific mechanisms. Here, we show that a calcium-based plasticity rule regulating L5-TTPC synapses holds also for several other pyramidal-to-pyramidal connections in a digital model of neocortical tissue. In particular, we show that synaptic physiology, cell morphology and innervation patterns jointly determine LTP/LTD dynamics without requiring a different model or parameter set for each connection type. We therefore propose that a similar set of plasticity mechanisms is shared by seemingly very different neocortical connections and that only a small number of targeted experiments is required for generating a complete map of synaptic plasticity dynamics in the neocortex.

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bioRxiv2019

Axonal gap junctions in the fly visual system enable fast prediction for evasive flight maneuvers

The visual system must make predictions to compensate for inherent delays in its processing. Yet little is known, mechanistically, about how prediction aids natural behaviors. Here, we show that despite a 20-30ms intrinsic processing delay, the vertical motion sensitive (VS) network of the blowfly achieves maximally efficient prediction. This prediction enables the fly to fine-tune its complex, yet brief, evasive flight maneuvers according to its initial ego-rotation at the time of detection of the visual threat. Combining a rich database of behavioral recordings with detailed compartmental modeling of the VS network, we further show that the VS network has axonal gap junctions that are critical for optimal prediction. During evasive maneuvers, a VS subpopulation that directly innervates the neck motor center can convey predictive information about the flys future ego-rotation, potentially crucial for ongoing flight control. These results suggest a novel sensory-motor pathway that links sensory prediction to behavior. Author summarySurvival-critical behaviors shape neural circuits to translate sensory information into strikingly fast predictions, e.g. in escaping from a predator faster than the systems processing delay. We show that the fly visual system implements fast and accurate prediction of its visual experience. This provides crucial information for directing fast evasive maneuvers that unfold over just 40ms. Our work shows how this fast prediction is implemented, mechanistically, and suggests the existence of a novel sensory-motor pathway from the fly visual system to a wing steering motor neuron. Echoing and amplifying previous work in the retina, our work hypothesizes that the efficient encoding of predictive information is a universal design principle supporting fast, natural behaviors.

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bioRxiv2019

Neuron Geometry Underlies a Universal Local Architecture in Neuronal Networks

Why do cortical microcircuits in a variety of brain regions express similar, highly nonrandom, network motifs? To what extent this structure is innate and how much of it is molded by plasticity and learning processes? To address these questions, we developed a general network science framework to quantify the contribution of neurons geometry and their embedding in cortical volume to the emergence of three-neuron network motifs. Applying this framework to a dense in silico reconstructed cortical microcircuits showed that the innate asymmetric neurons geometry underlies the universally recurring motif architecture. It also predicted the spatial alignment of cells composing the different triplets-motifs. These predictions were directly validated via in vitro 12-patch whole-cell recordings (7,309 triplets) from rat somatosensory cortex. We conclude that the local geometry of neurons imposes an innate, already structured, global network architecture, which serves as a skeleton upon which fine-grained structural and functional plasticity processes take place.

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bioRxiv2019

Single Cortical Neurons as Deep Artificial Neural Networks

We introduce a novel approach to study neurons as sophisticated I/O information processing units by utilizing recent advances in the field of machine learning. We trained deep neural networks (DNNs) to mimic the I/O behavior of a detailed nonlinear model of a layer 5 cortical pyramidal cell, receiving rich spatio-temporal patterns of input synapse activations. A Temporally Convolutional DNN (TCN) with seven layers was required to accurately, and very efficiently, capture the I/O of this neuron at the millisecond resolution. This complexity primarily arises from local NMDA-based nonlinear dendritic conductances. The weight matrices of the DNN provide new insights into the I/O function of cortical pyramidal neurons, and the approach presented can provide a systematic characterization of the functional complexity of different neuron types. Our results demonstrate that cortical neurons can be conceptualized as multi-layered "deep" processing units, implying that the cortical networks they form have a non-classical architecture and are potentially more computationally powerful than previously assumed.

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bioRxiv2018

An efficient analytical reduction of nonlinear detailed neuron models

Detailed conductance-based nonlinear neuron models consisting of thousands of synapses are key for understanding of the computational properties of single neurons and large neuronal networks, and for interpreting experimental results. Simulations of these models are computationally expensive, considerably curtailing their utility. Neuron_Reduce is a new analytical approach to reduce the morphological complexity and computational time of nonlinear neuron models. Synapses and active membrane channels are mapped to the reduced model preserving their transfer impedance to the soma; synapses with identical transfer impedance are merged into one NEURON process still retaining their individual activation times. Neuron_Reduce accelerates the simulations by 40-250 folds for a variety of cell types and realistic number (10,000-100,000) of synapses while closely replicating voltage dynamics and specific dendritic computations. The reduced neuron-models will enable realistic simulations of neural networks at unprecedented scale, including networks emerging from micro-connectomics efforts and biologically-inspired \"deep networks\". Neuron_Reduce is publicly available and is straightforward to implement.

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bioRxiv2018

Using subthreshold events to characterize the functional architecture of electrically coupled networks

The electrical connectivity in the inferior olive (IO) nucleus plays an important role in generating well-timed spiking activity. Here we combined electrophysiological and computational approaches to assess the functional organization of mice IO nucleus. Spontaneous fast and slow subthreshold events were commonly encountered during in vitro recordings. We show that the fast events represent a regenerative response in unique excitable spine-like structures in the axon hillock, whereas the slow events reflect the electrical connectivity between neurons ( spikelets). Recordings from cell pairs revealed the synchronized occurrence of distinct groups of spikelets; their rate and distribution enabled an accurate estimation of the number of connected cells and is suggestive of a clustered organization. This study thus provides a new perspective on the functional and structural organization of the olivary nucleus, insights into two different subthreshold non-synaptic events, and a novel experimental and theoretical approach to the study of electrically-coupled networks.

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bioRxiv2018

Perceptron learning and classification in a modeled cortical pyramidal cell

The perceptron learning algorithm and its multiple-layer extension, the backpropagation algorithm, are the foundations of the present-day machine learning revolution. However, these algorithms utilize a highly simplified mathematical abstraction of a neuron; it is not clear to what extent real biophysical neurons with morphologically-extended nonlinear dendritic trees and conductance-based synapses could realize perceptron-like learning. Here we implemented the perceptron learning algorithm in a realistic biophysical model of a layer 5 cortical pyramidal cell. We tested this biophysical perceptron (BP) on a memorization task, where it needs to correctly binarily classify 100, 1000, or 2000 patterns, and a generalization task, where it should discriminate between two \"noisy\" patterns. We show that the BP performs these tasks with an accuracy comparable to that of the original perceptron, though the memorization capacity of the apical tuft is somewhat limited. We concluded that cortical pyramidal neurons can act as powerful classification devices.

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bioRxiv2018

Whole-neuron synaptic mapping reveals local balance between excitatory and inhibitory synapse organization

The balance between excitatory and inhibitory (E and I) synaptic inputs is thought to be critical for information processing in neural circuits. However, little is known about the principles of spatial organization of E and I synapses across the entire dendritic tree of mammalian neurons. We developed a new, open-source, reconstruction platform for mapping the size and spatial distribution of E and I synapses received by individual, genetically-labeled, layer 2/3 cortical pyramidal neurons (PNs) in vivo. We mapped over 90,000 E and I synapses across twelve L2/3 PNs and uncovered structured organization of E and I synapses across dendritic domains as well as within individual dendritic segments in these cells. Despite significant, domain-specific, variations in the absolute density of E and I synapses, their ratio is strikingly balanced locally across dendritic segments. Computational modeling indicates that this spatially-precise E/I balance dampens dendritic voltage fluctuations and strongly impacts neuronal firing output.

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bioRxiv2018

Human cortical pyramidal neurons: From spines to spikes via models

We present the first-ever detailed models of pyramidal cells from human neocortex, including models on their excitatory synapses, dendritic spines, dendritic NMDA- and somatic/axonal- Na+ spikes that provided new insights into signal processing and computational capabilities of these principal cells. Six human layer 2 and layer 3 pyramidal cells (HL2/L3 PCs) were modeled, integrating detailed anatomical and physiological data from both fresh and post mortem tissues from human temporal cortex. The models predicted particularly large AMPA- and NMDA- conductances per synaptic contact (0.88 nS and 1.31nS, respectively) and a steep dependence of the NMDA-conductance on voltage. These estimates were based on intracellular recordings from synaptically-connected HL2/L3 pairs, combined with extra-cellular current injections and use of synaptic blockers. A large dataset of high-resolution reconstructed HL2/L3 dendritic spines provided estimates for the EPSPs at the spine head (12.7 {+/-} 4.6 mV), spine base (9.7 {+/-} 5.0 mV) and soma (0.3 {+/-} 0.1 mV), and for the spine neck resistance (50 - 80 M{Omega}). Matching the shape and firing pattern of experimental somatic Na+-spikes provided estimates for the density of the somatic/axonal excitable membrane ion channels, predicting that 134 {+/-} 28 simultaneously activated HL2/L3- HL2/L3 synapses are required for generating (with 50% probability) a somatic Na+ spike. Dendritic NMDA spikes were triggered in the model when 20 {+/-} 10 excitatory spinous synapses were simultaneously activated on individual dendritic branches. The particularly large number of basal dendrites in HL2/L3 PCs and the distinctive cable elongation of their terminals imply that ~25 NMDA- spikes could be generated independently and simultaneously in these cells, as compared to ~14 in L2/3 PCs from the rat temporal cortex. These multi-sites nonlinear signals, together with the large (~30,000) excitatory synapses/cell, equip human L2/L3 PCs with enhanced computational capabilities. Our study provides the most comprehensive model of any human neuron to-date demonstrating the biophysical and computational distinctiveness of human cortical neurons.

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bioRxiv2017

Shaping dendritic NMDA spikes by timed synaptic inhibition: Implications for the I/O properties of cortical neurons

The pronounced, long lasting, regenerative NMDA-spike is initiated in individual dendritic branches of different types of neurons and is known to play a key role in dendritic computations and plasticity. Combining dynamic system theory and computational approaches, we systematically analyzed how timed synaptic inhibition activated during the NMDA-spike time-course, sculpts this spike and its associated current influx. When impinging on its early phase, individual GABAergic synapse activation transiently, but strongly, dampened the NMDA-spike; later inhibition prematurely terminated it. This inhibition reduced the NMDA-mediated Ca2+ current by up to 60%. NMDA-spikes in distal dendritic branches/spines are longer lasting and more resilient to inhibition, and thus enhance synaptic plasticity at these branches. Examination of this sensitivity of the NMDA-spike to well-timed synaptic inhibition suggests that NMDA-spikes are highly modifiable signals which enable sparse weak distal dendritic inhibition to finely tune both the neuron's output spikes as well as the branch's/spine's Ca2+ current associated with the local NMDA spike.

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