The ongoing exponential rise in recording capacity calls for new approaches for analysing
and interpreting neural data. Effective dimensionality has emerged as an important property …

While neural plasticity has long been studied as the basis of learning, the growth of large-
scale neural recording techniques provides a unique opportunity to study how learning …

In this paper, we conduct an empirical study of the feature learning process in deep
classifiers. Recent research has identified a training phenomenon called Neural Collapse …

When training overparameterized deep networks for classification tasks, it has been widely
observed that the learned features exhibit a so-called" neural collapse'" phenomenon. More …

Understanding the neural basis of the remarkable human cognitive capacity to learn novel
concepts from just one or a few sensory experiences constitutes a fundamental problem. We …

Neural networks need the right representations of input data to learn. Here we ask how
gradient-based learning shapes a fundamental property of representations in recurrent …

A central question in deep learning is to understand the functions learned by deep networks.
What is their approximation class? Do the learned weights and representations depend on …

Geometric descriptions of deep neural networks (DNNs) have the potential to uncover core
representational principles of computational models in neuroscience. Here we examined the …

Deep learning algorithms are responsible for a technological revolution in a variety of tasks
including image recognition or Go playing. Yet, why they work is not understood. Ultimately …

Deep nonparametric regression on approximate manifolds: Nonasymptotic error bounds with
polynomial prefactors Page 1 The Annals of Statistics 2023, Vol. 51, No. 2, 691–716 …