Alexis Arnaudon

This study shows that current mechanisms for orientation selectivity in the visual cortex fail in realistic models, suggesting homeostatic plasticity may be necessary.

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 creates a detailed computational model of rat somatosensory cortex anatomy, including 4.2 million neurons and 14.2 billion synapses, and identifies limitations in connectivity prediction from morphology.

The paper presents a large-scale biophysically detailed model of somatosensory cortex to simulate and validate cortical dynamics and experimental results.

This study uses biophysical models and statistical methods to show that neuronal firing patterns depend on synergistic interactions of multiple ionic currents, contrasting with gene expression redundancy observed in single-cell RNAseq data.

The paper develops biophysically detailed models of thalamic reticular neurons to study their firing properties and impact on network dynamics.

This study models blood flow dynamics in the rat somatosensory cortex, showing that astrocytic activity causes localized vasodilation and increased blood flow, particularly in capillaries.

The paper presents a full-scale in silico model of rat hippocampal CA1 integrating diverse experimental data and validates it through simulations of theta rhythm and other experiments.

The paper develops a method to generate populations of detailed biophysical neuron models that reproduce experimental variability using Markov chain Monte Carlo techniques.

The paper introduces a workflow for creating and validating detailed neuronal models using rat somatosensory cortex data, showing it can generate models with varied responses.

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 synthesizes cortical dendrites using a topological descriptor to create statistically similar digital reconstructions, enabling the study of neuronal morphology and brain function.

The paper digitally reconstructs the neuro-glia-vascular system in rat somatosensory cortex to understand astrocyte-vascular-neuron interactions and their anatomical constraints.

The paper introduces HCGA, a framework for analyzing graph data by computing thousands of graph features and using statistical learning tools to improve classification and discovery.