David Sussillo

TL;DR: This work studies prefrontal cortex activity in macaque monkeys trained to flexibly select and integrate noisy sensory inputs towards a choice, and finds that the observed complexity and functional roles of single neurons are readily understood in the framework of a dynamical process unfolding at the level of the population.

TL;DR: The results reproduce data on premovement activity in motor and premotor cortex, and suggest that synaptic plasticity may be a more rapid and powerful modulator of network activity than generally appreciated.

TL;DR: This work explored the hypothesis that motor cortex reflects dynamics appropriate for generating temporally patterned outgoing commands and trained recurrent neural networks to reproduce the muscle activity of reaching monkeys to formalize this hypothesis.

TL;DR: The hypothesis that fixed points, both stable and unstable, and the linearized dynamics around them, can reveal crucial aspects of how RNNs implement their computations is explored.

TL;DR: LFADS, a deep learning method for analyzing neural population activity, can extract neural dynamics from single-trial recordings, stitch separate datasets into a single model, and infer perturbations, for example, from behavioral choices to these dynamics.