Idan Segev

The paper investigates the structural and functional roles of dendritic spines in cortical circuits, showing they support neuronal ensembles through specific connectivity patterns and synaptic organization.

The paper demonstrates that detailed models of rat pyramidal neurons can perform complex computational tasks using a novel algorithm, highlighting dendritic nonlinearities as key to high-order feature binding.

This study shows that spine-neck resistance in cortical pyramidal neurons affects temporal precision and inhibitory gating through biophysical modeling and connectomics analysis.

The paper investigates how human neuronal morphology influences synaptic connectivity complexity and shows that human neuron shapes lead to more complex connectomes compared to rodents.

This study builds a detailed computational model of the human neocortex and compares it to a rat model, revealing differences in cell density, branching complexity, and synaptic connectivity.

The paper identifies faster signal propagation in human neocortical dendrites compared to rodents, attributed to larger dendritic structures and conductance properties.

The paper introduces a simple neuron model based on calcium control hypothesis that can implement various learning rules, including novel ones.

The paper introduces the Functional Complexity Index (FCI) to quantify neuronal functional complexity and finds human cortical pyramidal neurons are more complex than rat neurons due to morpho-electrical differences.

The paper maps morpho-electric and computational properties of human hippocampal CA1 pyramidal neurons and identifies three distinct cell types with unique biophysical features.

The paper compares dendritic architecture in human and mice neuronal networks, finding that human pyramidal cells have greater dendritic complexity, contributing to increased network complexity despite lower neuron density.

The paper introduces a generalized framework for calcium-based synaptic plasticity that explains weight-dependent plasticity and incorporates experimental findings.

The paper shows that asymmetric voltage attenuation in dendrites can enable hierarchical heterosynaptic plasticity through interactions between NMDA spikes and voltage-gated calcium channels.

The paper proposes a calcium-based plasticity model that predicts LTP and LTD dynamics across multiple pyramidal connections in the neocortex.

The paper develops a 'Filter-and-Fire' neuron model that shows increased memory capacity and ability to recognize spatio-temporal patterns through multiple synaptic contacts on dendrites.

The paper develops a retinal model to show that excitatory input kinetics and SAC-SAC inhibition are critical for SAC centrifugal preference and direction selectivity.

The paper identifies hub neurons in a rodent neocortical microcircuit and shows that their removal disrupts network topology and dynamics more than random neuron removal.

The paper trains deep neural networks to model the input-output behavior of cortical pyramidal neurons, revealing their functional complexity as multi-layered processing units.

The paper introduces the gradient clusteron (G-clusteron), a model neuron that uses gradient descent and structural plasticity to solve classification tasks, achieving comparable performance to logistic regression on MNIST.

The paper shows the blowfly's visual system uses efficient prediction to enable fast evasive maneuvers despite processing delays.

This study investigates the conditions and mechanisms underlying burst firing in neocortical layer 5 pyramidal cells, identifying two distinct burst types and their modulation by dendritic inhibition.

The paper develops a morphometric reconstruction pipeline to analyze cortical VIP/ChAT interneurons and finds insult-driven plasticity in their morphology and gene expression.

The paper uses a biologically-detailed in-silico model of a neocortical microcircuit to study stimulus-specific adaptation in the auditory cortex and identifies key mechanisms underlying the response to surprising sensory inputs.

The paper shows that neuron geometry in cortical microcircuits explains universal network motifs and predicts spatial cell alignment, validated through in vitro recordings.

This study implements a perceptron learning algorithm in a biophysical model of a cortical pyramidal cell and shows it can perform classification tasks with accuracy comparable to the original perceptron.

This study characterizes subthreshold events in the inferior olive nucleus of mice to understand electrical coupling and network organization.

The paper introduces Neuron_Reduce, an analytical method that significantly speeds up nonlinear neuron simulations while preserving key dynamics and dendritic computations.

The paper maps excitatory and inhibitory synapses across dendritic trees of cortical neurons and finds local balance in their ratio despite domain-specific density variations.

The paper presents detailed biophysical models of human cortical pyramidal neurons, revealing enhanced computational capabilities through large synaptic conductances and multi-site nonlinear signaling.

This study shows how timed synaptic inhibition modifies NMDA spikes in dendrites, affecting calcium currents and synaptic plasticity.