Shallow recurrent decoders for neural and behavioural dynamics
TL;DR
A SHallow REcurrent Decoder (SHRED) architecture for mapping the dynamics of population codes to individual neurons and other proxy measures of neural activity and behaviour and empirically demonstrate the capabilities of the method on a number of model organisms including Caenorhabditis elegans, mouse, zebrafish and human biolocomotion.
Abstract
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 behaviour is quantifying the relationship between the activity of neuronal population codes and individual neurons. We leverage a SHallow REcurrent Decoder (SHRED) architecture for mapping the dynamics of population codes to individual neurons and other proxy measures of neural activity and behaviour. 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 behavioural states. It 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, behaviour and individual neurons can be constructed with only a few neural time-series recordings. Several examples in this article further highlight the potential of leveraging non-invasive or minimally invasive measurements to estimate large-scale brain dynamics. We empirically demonstrate the capabilities of the method on a number of model organisms including Caenorhabditis elegans, mouse, zebrafish and human biolocomotion. This article is part of the discussion meeting issue ‘Digital healthcare for the management of functional neurological disorders’.