Information Flows Reveal Computational Mechanisms of RNNs in Contextual Decision-making

Understanding the information flow of different task-relevant messages within recurrent circuits is crucial to comprehending how the brain works, and in turn, for diagnosing and treating brain disorders.While several information flow methods have focused on functional connectivity and modalities of communication, we do not yet have a principled approach for understanding what information flows can tell us about the effects of causal interventions.In this paper, we consider a measure called $M$-information flow, proposed by Venkatesh et al. (2020), within an artificial recurrent network trained on a contextual decision-making task studied by Mante et al. (2013).We show that $M$-information flow recapitulates the dynamics of information integration, showing specialization of individual units, and revealing how context information is incorporated to select the appropriate response without affecting the underlying circuit dynamics.We also show how $M$-information flow predicts the ``behavioral outcome'' of causal interventions within the network.This leads us to believe that understanding $M$-information flow within a recurrent network can inform the design of intervention studies, and in future, of stimulation-based treatments for brain disorders.