Recurrent neural circuits overcome partial inactivation by compensation and re-learning

Abstract

Technical advances in artificial manipulation of neural activity have precipitated a surge in studying the causal contribution of brain circuits to cognition and behavior. However, complexities of neural circuits challenge interpretation of experimental results, necessitating new theoretical frameworks for reasoning about causal effects. Here, we take a step in this direction, through the lens of recurrent neural networks trained to perform perceptual decisions. We show that understanding the dynamical system structure that underlies network solutions provides a precise account for the magnitude of behavioral effects due to perturbations. Our framework explains past empirical observations by clarifying the most sensitive features of behavior, and how complex circuits compensate and adapt to perturbations. In the process, we also identify strategies that can improve the interpretability of inactivation experiments.Significance StatementNeuroscientists heavily rely on artificial perturbation of the neural activity to understand the function of brain circuits. Current interpretations of experimental results often follow a simple logic, that the magnitude of a behavioral effect following a perturbation indicates the degree of involvement of the perturbed circuit in the behavior. We model a variety of neural networks with controlled levels of com­plexity, robustness, and plasticity, showing that perturbation experiments could yield counter-intuitive results when networks are complex enough-to allow unperturbed pathways to compensate for the per­turbed neurons-or plastic enough-to allow continued learning from feedback during perturbations. To rein in these complexities we develop a Functional Integrity Index that captures alterations in network computations and predicts disruptions of behavior with the perturbation.