Object classification has been proposed as a principal objective of the primate ventral visual
stream and has been used as an optimization target for deep neural network models (DNNs) …

The brain has a well-known large scale organization at the level of brain regions, where
different regions have been argued to primarily serve different behavioral functions. This …

The pervasiveness of proprietary language models has raised privacy concerns for users'
sensitive data, emphasizing the need for private inference (PI), where inference is performed …

LayerNorm is a critical component in modern large language models (LLMs) for stabilizing
training and ensuring smooth optimization. However, it introduces significant challenges in …

Variational autoencoders (VAE) employ Bayesian inference to interpret sensory inputs,
mirroring processes that occur in primate vision across both ventral (Higgins et al., 2021) …

We use sensory information in remarkably flexible ways. We can generalize by ignoring task-
irrelevant features, report different features of a stimulus, and use different actions to report a …

Trained recurrent neural networks (RNNs) have become the leading framework for modeling
neural dynamics in the brain, owing to their capacity to mimic how population-level …

The progression of neuroscience relies on the discovery of structure in the brain. From the
discovery of neurons to the structure of the potassium channel, and, in recent years, the …

Models trained with self-supervised learning objectives have recently matched or surpassed
models trained with traditional supervised object recognition in their ability to predict neural …

Delayed generalization, also known as``grokking'', has emerged as a well-replicated
phenomenon in overparameterized neural networks. Recent theoretical works associated …