Dimitri Yatsenko

The paper presents a functional connectomics dataset of mouse visual cortex, combining calcium imaging with EM reconstruction to study neural circuits and their connectivity.

Functionally similar neurons in mouse visual cortex connect preferentially across layers and areas, including feedback, and feature tuning, not receptive-field position, predicts synapses beyond axon-dendrite proximity.

Spyglass provides a reproducible and shareable framework for neuroscience research, enabling data reuse and analysis across labs using standardized formats and pipelines.

This protocol provides standardised measurements to assess and compare multiphoton microscope performance across users.

The paper introduces DataJoint Elements, a resource providing modular workflow designs for organizing data and computations in neurophysiology experiments.

This paper defines criteria for evaluating photonic probe designs to improve signal separability in densely labeled neural populations.

The paper introduces EASE, a method that matches calcium imaging signals to EM-derived cell segments in mouse visual cortex, enabling dendritic functional analysis and denoising.

The paper investigates how functional connectivity, synaptic connectivity, and external input variability can be distinguished in neural microcircuits using simulations, analysis, and calcium imaging data.

The paper uses advanced imaging techniques to show that mouse visual cortex has a global orientation tuning map, challenging previous assumptions about its structure.

A deep recurrent encoding model predicts spiking of thousands of mouse V1 neurons during natural video and carries over to noise stimuli when only its last layer is retrained.

DataJoint is an open-source toolbox for managing large scientific datasets using MATLAB or Python, enabling flexible data manipulation and processing.