Anton S. Arkhipov

The paper uses a bio-realistic model of mouse primary visual cortex to show that cortical oscillations follow a spectrolaminar motif, with gamma in superficial layers and alpha/beta in deep layers, driven by specific cell types and synaptic properties.

This study uses biologically realistic simulations to show that cortical circuit stability and information integration increase with hierarchy, linked to increased connectivity and SST cell proportion.

The paper presents a database of spiking activity in the mouse visual system during an image change detection task, revealing task-specific coding patterns across brain regions.

The paper presents a deep-learning-assisted simulator of a mouse primary visual cortex model, integrating multimodal data to study circuit function and synaptic organization.

This paper introduces and validates Laminar Population Analysis (LPA), a method to decompose extracellular potentials in the mouse visual system by identifying contributions from different neural populations and pathways.

The paper uses population network models and public datasets to analyze how cortical circuits adapt to stimulus novelty, finding weaker connections and shifts in neural population dynamics.

The paper examines how different behavioral strategies in mice affect neural activity in the Vip-Sst disinhibitory circuit during a visual change detection task.

The paper investigates the synaptic connectivity of thick tufted layer 5 pyramidal neurons in the mouse visual cortex, revealing their local inhibitory and long-range excitatory connections.

The paper uses biophysical simulations of mouse primary visual cortex to show that current sinks and sources are shaped by both synaptic weights and dendritic placement, with distinct contributions from thalamocortical inputs and cortical feedback.

This paper uses Neuropixels recordings to analyze neurophysiological differentiation (ND) in mouse visual brain areas across timescales, finding higher ND for naturalistic stimuli and successful detection trials.

The paper identifies diverse functional neuron types in the visual cortex that process novel and familiar stimuli in mice.

This study shows that claustrum input affects cortical neurons differently based on brain area, layer, and cell type, suggesting functional modules in claustro-cortical circuits.

The paper presents a synaptic physiology dataset and analysis platform to study cortical microcircuit structure and synaptic properties in mouse and human neocortex, revealing layer-dependent connectivity and subclass-specific dynamics.

The paper measures stimulus-evoked neurophysiological differentiation in mouse visual cortex neurons across layers and areas, finding greater differentiation in specific layers and areas for unscrambled stimuli.

This paper shows that mixing diverse activation functions in CNNs improves image classification performance, suggesting a computational role for diverse cell types in biological circuits.

The paper develops coupled autoencoders to identify cortical neurons across modalities and finds consistent cell types in transcriptomic and electrophysiological data.

The paper presents a dataset and analysis of spiking activity across multiple mouse visual areas, revealing that functional connectivity mirrors anatomical hierarchy.

The paper introduces BMTK, an open-source software suite for multiscale brain circuit modeling, enabling simulations across different resolution levels with reduced coding requirements.

The paper characterizes transcriptomic and physiological properties of GABAergic cortical neurons in mice, identifying 20 interneuron met-types with consistent features across modalities.

The paper uses a mouse V1 model to study how perturbations affect coding efficiency and robustness, showing shifts in inhibition and excitation based on stimulus contrast.

The paper develops a multi-scale model of mouse primary visual cortex integrating structural and functional data to identify cell-class specific connectivity rules.

The paper introduces the SONATA data format to efficiently describe large-scale network models in neuroscience, enabling model construction, simulation, and sharing.

The paper identifies direction-reversing neurons in the mouse visual system that change their preferred motion direction under different spatiotemporal stimuli, supported by an asymmetric filter model.

The paper classifies neurons in mouse visual cortex into 17 electrophysiological and 35 morphological types using unsupervised methods and links them to transcriptomic types.

The paper develops a biophysically detailed in silico model of layer 4 of the mouse primary visual cortex, showing how functional synaptic connectivity reproduces experimental observations of neuronal responses to visual stimuli.