David Sussillo

This paper improves the interpretability of LFADS models by introducing a per-trial bias, enabling simpler contextual dynamics and better analysis of neural activity in simulated and real data.

The paper introduces CtDToolkit, a framework providing synthetic datasets and metrics to evaluate and improve neural dynamics models of neural computation.

This paper identifies dynamical motifs in recurrent neural networks that enable flexible multitask computation through modular reuse across different tasks.

A wrist-worn surface electromyography interface and generic neural network decoders translate neuromotor signals into computer input across uncalibrated users, with up to 30% higher bandwidth after personalization.

This paper develops a theoretical framework explaining individual variability in decision-making by analyzing neural and behavioral data from rats performing a context-dependent task.

This study develops ConvRNN models to simulate primate ventral visual stream processing, finding that recurrent networks achieve better task performance and efficiency than deeper feedforward networks.

This paper introduces LFADS, a deep learning method to infer single-trial neural population dynamics from spiking data, demonstrating improved accuracy in predicting behavior and extracting dynamics across datasets.