Rodrigo Perin

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 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 develops a computational model of mouse thalamoreticular microcircuitry to study sensory enhancement and sleep spindle properties, revealing mechanisms of network-level activity across brain states.

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

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

The paper estimates the size of the readily-releasable vesicle pool (NRRP) at synaptic connections in the neocortex, finding values between two to three for L5-TTPC connections and suggesting multi-vesicular release is common.

The paper objectively classifies neocortical pyramidal cells using algebraic topology, identifying seventeen types in the rat somatosensory cortex without expert input.

The paper shows that small sample sizes make it hard to distinguish different network topologies in cortical circuits and introduces a new statistic to overcome this limitation.