In neural networks, task-relevant information is represented jointly by groups of neurons.
However, the specific way in which this mutual information about the classification label is …

We show that the ability of a neural network to integrate information from diverse sources
hinges critically on being exposed to properly correlated signals during the early phases of …

Recurrent neural networks (RNNs) trained on neuroscience-based tasks have been widely
used as models for cortical areas performing analogous tasks. However, very few tasks …

We propose a notion of common information that allows one to quantify and separate the
information that is shared between two random variables from the information that is unique …

We introduce the Redundant Information Neural Estimator (RINE), a method that allows
efficient estimation for the component of information about a target variable that is common …

Cognition emerges from the coordination of computations in multiple brain areas. However,
elucidating these coordinated computations within and across brain regions is challenging …

Decision-making emerges from distributed computations across multiple brain areas, but it is
unclear why the brain distributes the computation. In deep learning, artificial neural networks …

This paper presents a study on the computational complexity of coding for machines, with a
focus on image coding for classification. We first conduct a comprehensive set of …

In this paper, we present a comparative analysis of various self-supervised Vision
Transformers (ViTs), focusing on their local representative power. Inspired by large …

Motivated by rapid advances in neuroengineering, we recently proposed an interventional
way of reverse engineering neural circuits that is oriented towards treating disorders. The …