Aishwarya Balwani

The paper develops RNNs that incorporate biological constraints like Dale's law and structured connectivity to better model cortical function and validate insights through mouse calcium imaging data.

This study uses recurrent neural networks to analyze how inter-areal connections affect cortical microcircuit function during learning, finding feedback connections enhance modularization and predictive coding.

The paper provides a three-dimensional X-ray microtomography dataset of thalamocortical structures to study neural heterogeneity.

This paper introduces a deep learning approach to map brain microarchitecture by learning features from local image patches to identify anatomical regions and subdivisions.

This paper uses a deep generative model to analyze brain structure variability across mouse brains and validates the model through structured perturbations.