The activity patterns of grid cells form distinctively regular triangular lattices over the
explored spatial environment and are largely invariant to visual stimuli, animal movement …

In this work, we formally prove that, under certain conditions, if a neural network is invariant
to a finite group then its weights recover the Fourier transform on that group. This provides a …

Large Language Models (LLMs) have demonstrated remarkable capabilities in performing
tasks across various domains without needing explicit retraining. This capability, known as …

The mammalian hippocampus contains a cognitive map that represents an animal's position
in the environment and generates offline “replay”, for the purposes of recall, planning,, and …

Representing and reasoning about physical space is fundamental to animal survival, and
the mammalian lineage expresses a wealth of specialized neural representations that …

A prominent methodology in computational neuroscience posits that the brain can be
understood by identifying which artificial neural network models most accurately predict …

Work on deep learning-based models of grid cells suggests that grid cells generically and
robustly arise from optimizing networks to path integrate, ie, track one's spatial position by …

Grid cells in the medial entorhinal cortex (MEC) of the mammalian brain exhibit a strikingly
regular hexagonal firing field over space. These cells are learned after birth and are thought …

The brain's ability to navigate is often attributed to spatial cells in the hippocampus and
entorhinal cortex. Grid cells, found in the entorhinal cortex, are known for their hexagonal …

A common technique for ameliorating the computational costs of running large neural
models is sparsification, or the removal of neural connections during training. Sparse …