Arna Ghosh

Distinct CA1 inputs support shifts in neural dimensionality and memory resolution

The paper shows that distinct inputs to the CA1 region of the hippocampus support different memory resolutions by altering neural dimensionality and feedforward inhibition.

Brain-like learning with exponentiated gradients

This paper introduces exponentiated gradient (EG) as a learning algorithm for artificial neural networks that better aligns with biological principles than gradient descent.

Stochastic Wiring of Cell Types Enhances Fitness by Generating Phenotypic Variability

The paper uses a computational model to show that developmental stochasticity in neural wiring increases population fitness by generating phenotypic variability in unpredictable environments.

The feature landscape of visual cortex

The paper identifies visual feature preferences of neurons in mouse visual cortical areas using generative AI and closed-loop recordings, revealing how these preferences are organized to encode own-stimuli more effectively.

Learning better with Dale's Law: A Spectral Perspective

This paper investigates how incorporating Dale's Law into recurrent neural networks affects learning, finding that spectral properties of the weight matrix are more critical than sign constraints for performance.

The effects of the NMDAR co-agonist D-serine on the structure and function of the optic tectum

This study examines how chronic D-serine administration affects the structure and function of the developing optic tectum in Xenopus, showing changes in dendritic morphology and retinotopic map organization.

Estimating brain age from structural MRI and MEG data: Insights from dimensionality reduction techniques

This study combines structural MRI and MEG data to predict brain age using dimensionality reduction techniques, finding that a stacked model outperforms individual modalities.