Latent computing by biological neural networks: A dynamical systems framework

TL;DR

This paper introduces a dynamical systems framework for neural networks emphasizing latent processing units, explaining how neural populations achieve stable computations despite individual variability and representational drift.

Contribution

It presents a novel theoretical framework that characterizes neural computation through latent units and collective dynamics, with testable predictions for neural coding and robustness.

Findings

Low-dimensional neural computations can produce high-dimensional dynamics.

Neural manifolds exhibit inherent coding redundancy.

Linear decoders can effectively interpret neural activity.

Abstract

Although individual neurons and neural populations exhibit the phenomenon of representational drift, perceptual and behavioral outputs of many neural circuits can remain stable across time scales over which representational drift is substantial. These observations motivate a dynamical systems framework for neural network activity that focuses on the concept of \emph{latent processing units,} core elements for robust coding and computation embedded in collective neural dynamics. Our theoretical treatment of these latent processing units yields five key attributes of computing through neural network dynamics. First, neural computations that are low-dimensional can nevertheless generate high-dimensional neural dynamics. Second, the manifolds defined by neural dynamical trajectories exhibit an inherent coding redundancy as a direct consequence of the universal computing capabilities of the underlying dynamical system. Third, linear readouts or decoders of neural population activity can suffice to optimally subserve downstream circuits controlling behavioral outputs. Fourth, whereas recordings from thousands of neurons may suffice for near optimal decoding from instantaneous neural activity patterns, experimental access to millions of neurons may be necessary to predict neural ensemble dynamical trajectories across timescales of seconds. Fifth, despite the variable activity of single cells, neural networks can maintain stable representations of the variables computed by the latent processing units, thereby making computations robust to representational drift. Overall, our framework for latent computation provides an analytic description and empirically testable predictions regarding how large systems of neurons perform robust computations via their collective dynamics.