Neuroimaging Series: Chethan Pandarinath

Chethan Pandarinath, Assistant Professor, Biomedical Engineering, Georgia Institute of Technology Title: Uncovering single-trial neural population dynamics with deep learning Abstract: Neuroscience is experiencing a revolution in which simultaneous recording of thousands of neurons is revealing population dynamics that are not apparent from single-neuron responses. This structure is typically extracted from data averaged across many trials, but deeper understanding requires studying phenomena detected in single trials, which is challenging due to incomplete sampling of the neural population, trial-to-trial variability, and fluctuations in action potential timing. We introduce latent factor analysis via dynamical systems (LFADS), a deep learning method to infer latent dynamics from single-trial neural spiking data. When applied to a variety of macaque and human motor cortical datasets, LFADS accurately predicts observed behavioral variables, extracts precise firing rate estimates of neural dynamics on single trials, infers perturbations to those dynamics that correlate with behavioral choices, and combines data from non-overlapping record- ing sessions spanning months to improve inference of underlying dynamics. Paper: Inferring single-trial neural population dynamics using sequential auto-encoders. Nature Methods, 2018. doi: http://doi.org/10.1038/s41592-018-0109-9 free access: https://rdcu.be/6Wji