Extracting behavioral measurements non-invasively from video is stymied by the fact that it is
a hard computational problem. Recent advances in deep learning have tremendously …

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 …

We can now measure the connectivity of every neuron in a neural circuit, but we are still
blind to other biological details, including the dynamical characteristics of each neuron. The …

Convolutional neural networks trained on object recognition derive inspiration from the
neural architecture of the visual system in mammals, and have been used as models of the …

Dexterous motor control requires feedback from proprioceptors, internal mechanosensory
neurons that sense the body's position and movement. An outstanding question in …

Interest in biologically inspired alternatives to backpropagation is driven by the desire to
both advance connections between deep learning and neuroscience and address …

The cuneate nucleus (CN) is among the first sites along the neuraxis where proprioceptive
signals can be integrated, transformed, and modulated. The objective of the study was to …

Control of forces is essential in both animals and walking machines. Insects measure forces
as strains in their exoskeletons via campaniform sensilla (CS). Deformations of cuticular …

In this study, we propose a new data augmentation technique, Koopman-mixup (K-mixup), to
improve the learning stability and final performance of offline reinforcement learning (RL) …

It is unknown precisely how the nervous system of invertebrates combines multiple sensory
inputs to calculate more abstract quantities, eg, combining the angle of multiple leg joints to …