The brain is the perfect place to look for inspiration to develop more efficient neural
networks. The inner workings of our synapses and neurons provide a glimpse at what the …

The utility of machine learning in understanding the motor system is promising a revolution
in how to collect, measure, and analyze data. The field of movement science already …

The rise and success of large language models undermines virtually every strong claim for
the innateness of language that has been proposed by generative linguistics. Modern …

Research seeking to build artificial systems capable of reproducing elements of human
intelligence may benefit from a deeper consideration of the architecture and learning …

The visual system of mammals is comprised of parallel, hierarchical specialized pathways.
Different pathways are specialized in so far as they use representations that are more …

Recognition of objects from sensory stimuli is essential for survival. To that end, sensory
networks in the brain must form object representations invariant to stimulus changes, such …

Materials and machines are often designed with particular goals in mind, so that they exhibit
desired responses to given forces or constraints. Here we explore an alternative approach …

Lifelong learning and adaptability are two defining aspects of biological agents. Modern
reinforcement learning (RL) approaches have shown significant progress in solving complex …

We introduce Brain-Inspired Modular Training (BIMT), a method for making neural networks
more modular and interpretable. Inspired by brains, BIMT embeds neurons in a geometric …

Neurons in the dorsal visual pathway of the mammalian brain are selective for motion
stimuli, with the complexity of stimulus representations increasing along the hierarchy. This …