Chandramouli Chandrasekaran

This study evaluates three pan-inhibitory viral constructs in macaques to assess their efficacy in targeting inhibitory neurons and modulating neural activity.

This study shows that prefrontal and premotor cortex use distinct neural dynamics to implement different computations during flexible perceptual decisions, with DLPFC using higher-dimensional representations and PMd reflecting only the selected action.

This paper introduces a doubly stochastic renewal framework to partition spiking variability in neurons and finds that spiking irregularity decreases from sensory to association areas.

This paper introduces PhysMAP, a framework that uses multimodal electrophysiological data to identify and study cell types in vivo, showing improved alignment with transcriptomic cell types and circuit dynamics.

This study identifies a population code in primate premotor cortex that encodes dynamic choice formation, revealing an attractor mechanism for decision computation.

The paper uses high-density Neuropixels recordings and machine learning to identify cell classes in monkey V1 and demonstrate structural-function relationships in the microcircuit.

The paper reaffirms the ubiquity of a spectrolaminar motif across multiple datasets and introduces an improved tool to identify it.

This paper demonstrates how to separate cognitive and motor processes in mice using a novel behavioral task and subspace decomposition, revealing distinct neural dynamics for each.

This paper introduces NEMO, a multimodal contrastive learning method for classifying cell types and brain regions from electrophysiological data, achieving state-of-the-art results on two opto-tagged datasets and the International Brain Laboratory Brain-wide Map dataset.

PhysMAP identifies neuronal cell types from electrophysiological data by integrating multiple modalities, outperforming single-modal approaches in mouse brain recordings.

The paper shows that brain areas form minimal sufficient representations of task inputs through preferential propagation of relevant information during decision-making.

This study uses a dynamical systems approach to show that initial neural states in PMd predict decision-related dynamics and behavior in a reaction time task.

This paper introduces WaveMAP, a method using non-linear dimensionality reduction and graph clustering to identify cell types in premotor cortex, revealing new physiological and functional diversity.

The paper decodes real-time decision variables from motor cortex activity and shows their link to choice behavior and decision dynamics.

This paper uses multi-area recurrent neural networks to model decision-making dynamics in the brain, finding that orthogonal representations of decision variables are preferentially propagated through inter-area connections.

CHaRTr is an R toolbox that allows users to implement and test various decision-making models, including modern extensions of the diffusion decision model.

The paper generalizes a coactivation model to explain audiovisual detection across different intensities and delays in monkeys.

The paper shows that decision-related activity in macaque PMd is linked to action selection, not perceptual processing, when specific stimulus-response associations are known.

This study investigates beta band activity in the premotor cortex of macaques during a visual reaction time task, finding frequency-dependent correlations with reaction time and laminar depth dependence.

The paper shows that model selection in neural data can be unreliable due to brittleness, where small data changes affect model rankings, and suggests ways to address this issue.

This study examines how decision variables are represented in neural populations of PMd and M1 during a motor task, showing differences in timing and strength between areas and replicating findings with a computational model.