Michael Reimann

The paper shows that current source density estimation can produce incorrect results in dense electrode arrays due to small inter-electrode spacing.

The paper investigates the structural and functional roles of dendritic spines in cortical circuits, showing they support neuronal ensembles through specific connectivity patterns and synaptic organization.

The paper explains neuronal network architecture through spatial continuity and validates it with a stochastic algorithm matching reference data.

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 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 paper uses computational modeling to identify neural populations and connectivity patterns underlying the whisker-flick EEG waveform in rodents.

The paper develops a semi-analytic model to simulate compound action potentials in complex nerves and validates it against in vivo measurements, showing how fascicle activation affects signal shape and magnitude.

The paper uses simulations to study how synaptic plasticity interacts with network structure and dendritic organization to shape cortical microcircuit function and learning.

The paper introduces BlueRecording, a pipeline for efficiently calculating extracellular signals in large-scale neural simulations, and applies it to study signal contributions in a rat model.

The paper introduces a graph model that captures non-random connectivity in cortical circuits by incorporating physical constraints of axon-soma-dendrite bridging.

The paper shows that heterogeneous and higher-order cortical connectivity allows neural networks to balance efficiency and robustness by supporting subpopulations with different roles.

The paper evaluates spike sorters using a cortical model, finding that sorting biases reduce stimulus discrimination ability by nearly half.

The paper introduces a framework for manipulating neuronal connectomes in simulations to study structure-function relationships, finding causal shifts in neuronal activity from connectivity changes.

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 analyzes inhibition and excitation in a cortical connectome, finding that inhibition is structured through specific connections in large neuron motifs, not just pairwise interactions.

The paper presents a biophysically-detailed computational model of two interconnected cortical areas to study inter-areal interactions in sensory processing and their impact on stimulus discrimination and signal integration.

The paper enhances brain atlases with laminar coordinate systems and flatmaps to better represent layered brain regions, enabling improved visualization and modeling of structures like the barrel cortex.

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 uses a simulation-based approach to study how synaptic connectivity patterns shape functional cell assemblies in a cortical network model, showing that connectivity structure significantly influences assembly composition and activation.

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.

The paper develops a parcellation scheme for mouse isocortex based on connectivity gradient reversals, revealing 41 subregions with distinct connectivity patterns.

The paper shows that synaptic connectivity influences neuronal stimulus encoding, revealing two distinct coding strategies in a microcircuit.

The paper uses a biologically-detailed in-silico model of a neocortical microcircuit to study stimulus-specific adaptation in the auditory cortex and identifies key mechanisms underlying the response to surprising sensory inputs.

This study compares two neocortical microcircuit models with similar first-order connectivity but differing higher-order structure, finding that morphological diversity affects network topology and neuronal firing patterns.

The paper builds a statistical model of the mouse whole-neocortex micro-connectome using region-to-region connectivity and axon reconstructions, revealing a novel targeting principle for predicting individual axon innervation.

The paper quantifies intrinsic variability in cortical circuits and shows that recurrent connectivity enables millisecond spike-time reliability despite noise and chaos.