Transformer-based language models spread FLOPs uniformly across input sequences. In
this work we demonstrate that transformers can instead learn to dynamically allocate FLOPs (…

The mammalian hippocampus contains a cognitive map that represents an animal’s position
in the environment 1 and generates offline “replay” 2 , 3 for the purposes of recall 4 , …

Little is understood about how engrams, sparse groups of neurons that store memories, are
formed endogenously. Here, we combined calcium imaging, activity tagging, and …

In recent years, many researchers have proposed new models for synaptic plasticity in the
brain based on principles of machine learning. The central motivation has been the …

Most recurrent neural networks (RNNs) do not include a fundamental constraint of real
neural circuits: Dale's Law, which implies that neurons must be excitatory (E) or inhibitory (I). …

Scientists have long conjectured that the neocortex learns patterns in sensory data to generate
top-down predictions of upcoming stimuli. In line with this conjecture, different responses …

Our ability to use deep learning approaches to decipher neural activity would likely benefit
from greater scale, in terms of both the model size and the datasets. However, the integration …

Highlights • Protein nanoparticles are capable of highly uniform multifunctionalization. •
Interior modifications of protein nanoparticles can improve loading capacity. • Exterior …

In real life, success is often contingent upon multiple critical steps that are distant in time from
each other and from the final reward. These critical steps are challenging to identify with …

Neurons in the primary cortex carry sensory- and behavior-related information, but it remains
an open question how this information emerges and intersects together during learning. …