A5016582959- Neural dynamics and brain function
- Neuroscience and Neuropharmacology Research
- Neuroscience and Neural Engineering
- Advanced Memory and Neural Computing
- Electrochemical Analysis and Applications
- stochastic dynamics and bifurcation
- Memory and Neural Mechanisms
- Neural Networks and Applications
Hebrew University of Jerusalem
2015-2020
Institute of Neurobiology
2014
The size and shape of dendrites axons are strong determinants neuronal information processing. Our knowledge on structure function is primarily based brains laboratory animals. Whether it translates to human not known since quantitative data "full" morphologies lacking. Here, we obtained brain tissue during resection surgery reconstructed basal apical individual neurons across all cortical layers in temporal cortex (Brodmann area 21). Importantly, did correlate etiology, disease severity, or...
The advanced cognitive capabilities of the human brain are often attributed to our recently evolved neocortex. However, it is not known whether basic building blocks neocortex, pyramidal neurons, possess unique biophysical properties that might impact on cortical computations. Here we show layer 2/3 neurons from temporal cortex (HL2/3 PCs) have a specific membrane capacitance ( C m ) ~0.5 µF/cm 2 , half commonly accepted 'universal' value (~1 for biological membranes. This finding was...
This study highlights a new and powerful direct impact of the dendritic tree (the input region neurons) on encoding capability axon output region). We show that size arbors (its impedance load) strongly modulates shape action potential (AP) onset at initial segment; it is accelerated in neurons with larger surface area. AP rapidness key determining axonal spikes to encode (phase lock to) rapid changes synaptic inputs. Hence, our findings imply have improved capabilities. “dendritic effect”...
We present detailed models of pyramidal cells from human neocortex, including on their excitatory synapses, dendritic spines, NMDA- and somatic/axonal Na+ spikes that provided new insights into signal processing computational capabilities these principal cells. Six layer 2 3 (HL2/L3 PCs) were modeled, integrating anatomical physiological data both fresh postmortem tissues temporal cortex. The predicted particularly large AMPA- NMDA-conductances per synaptic contact (0.88 1.31 nS,...
Abstract There have been few quantitative characterizations of the morphological, biophysical, and cable properties neurons in human neocortex. We employed feature-based statistical methods on a rare data set 60 3D reconstructed pyramidal from L2 L3 temporal cortex (HL2/L3 PCs) removed after brain surgery. Of these cells, 25 were also characterized physiologically. Thirty-two morphological features analyzed (e.g., dendritic surface area, 36 333 ± 18 157 μm2; number basal trees, 5.55 1.47;...
Detailed conductance-based nonlinear neuron models consisting of thousands synapses are key for understanding the computational properties single neurons and large neuronal networks, interpreting experimental results. Simulations these computationally expensive, considerably curtailing their utility. Neuron_Reduce is a new analytical approach to reduce morphological complexity time models. Synapses active membrane channels mapped reduced model preserving transfer impedance soma; with...
Abstract We present the first-ever detailed models of pyramidal cells from human neocortex, including on their excitatory synapses, dendritic spines, NMDA- and somatic/axonal- Na + spikes that provided new insights into signal processing computational capabilities these principal cells. Six layer 2 3 (HL2/L3 PCs) were modeled, integrating anatomical physiological data both fresh post mortem tissues temporal cortex. The predicted particularly large AMPA- conductances per synaptic contact...
Abstract Detailed conductance-based nonlinear neuron models consisting of thousands synapses are key for understanding the computational properties single neurons and large neuronal networks, interpreting experimental results. Simulations these computationally expensive, considerably curtailing their utility. Neuron_Reduce is a new analytical approach to reduce morphological complexity time models. Synapses active membrane channels mapped reduced model preserving transfer impedance soma;...