Ruth Benavides‐Piccione

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.

This study quantifies morphological differences in human pyramidal neurons across cortical areas and species, revealing distinct structural features compared to mouse neurons and hippocampal neurons.

This paper introduces a morphological repair algorithm inspired by dendritic regeneration to reconstruct incomplete human and non-human neuronal dendrites, restoring both morphology and electrophysiological function.

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 compares human and mouse Purkinje cells, finding human cells have greater dendritic complexity and computational capacity.

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

The paper analyzes 3D spine morphology in human and mouse neurons to show a morphological continuum, species differences, and age-related changes in spine size.

The paper compares basal dendrite morphology between human and mouse hippocampal CA1 pyramidal neurons using Bayesian networks to analyze inter-species differences and morphological determinants.

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