Henry Markram

This paper presents a deep learning framework that classifies neuronal morphologies using topology and graph neural networks, achieving accuracy comparable to human experts.

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

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 improves the resolution of the Allen Mouse Brain Atlas to 25 micrometers cubed for 4,083 genes and provides an interactive platform for exploring gene expression patterns.

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 generates whole-brain connectivity by predicting detailed axonal morphologies from sparse data, enabling in silico experimentation of large brain regions.

This study develops a multiscale electro-metabolic model of a rat neocortex to show that middle cortical layers are more vulnerable to energy impairment with age.

The paper develops a molecular model of the aging brain's metabolic system, identifies key disruptions, and proposes interventions to restore metabolic flexibility and neuronal function.

This study introduces an algorithm combining algebraic topology and Steiner tree methods to generate biologically accurate long-range axonal morphologies that match experimental data.

The paper extends and improves the CCFv3 atlas to cover missing brain regions and enhance alignment for better cell type mapping and data integration.

The paper creates a 3D spatial atlas of transcriptomic, morphological, and electrophysiological cell types in the mouse brain using integrated data and computational methods.

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 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 maps molecular identities to morpho-electrically defined inhibitory neuron types in the mouse cortex using a latent space approach and existing datasets.

The paper estimates inhibitory neuron densities in the mouse brain by integrating diverse datasets and literature values, finding 20.3% of neurons are inhibitory with specific subtype proportions.

This study shows that overexpressing UCP4 in astrocytic mitochondria prevents metabolic and structural neuronal dysfunctions in a mouse model of Alzheimer's disease.

The paper quantifies neuromodulatory fiber length and varicosity density in rat somatosensory cortex and predicts their effects on network activity.

Ultraliser creates high-fidelity 3D models of neuroscientific structures for in silico simulations, enabling detailed structural analysis and scalable experiments.

This paper introduces a multi-modal fitting framework combining patch-clamp and HD-MEA data to build more accurate multicompartment neuron models.

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

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