Shallow Recurrent Decoders for Neural and Behavioral Dynamics

Abstract

Machine learning algorithms are affording new opportunities for building bio-inspired and data-driven models characterizing neural activity. Critical to understanding decision making and behavior is quantifying the relationship between the activity of neuronal population codes and individual neurons. We leverage a SHallow REcurrent Decoding (SHRED) architecture for mapping the dynamics of population codes to individual neurons and other proxy measures of neural activity and behavior. SHRED is a robust and flexible sensing strategy which allows for decoding the diversity of neural measurements with only a few sensor measurements. Thus estimates of whole brain activity, behavior and individual neurons can be constructed with only a few neural time-series recordings. This opens up the potential for using non-invasive, or minimally invasive, measurements for estimating difficult to achieve, or invasive, large scale brain and neural recordings. SHRED is constructed from a temporal sequence model, which encodes the temporal dynamics of limited sensor data in multiple scenarios, and a shallow decoder, which reconstructs the corresponding high-dimensional neuronal and/or behavioral states. We demonstrate the capabilities of the method on a number of model organisms including C. elegans, mouse, zebrafish, and human biolocomotion.