How else can we define Information Flow in Neural Circuits?

Recently, we developed a systematic framework for defining and inferring flows of information about a specific message in neural circuits [2], [3]. We defined a computational model of a neural circuit consisting of computational nodes and transmissions being sent between these nodes over time. We then gave a formal definition of information flow pertaining to a specific message, which was capable of identifying paths along which information flowed in such a system. However, this definition also had some non-intuitive properties, such as the existence of "orphans"—nodes from which information flowed out, even though no information flowed in. In part, these non-intuitive properties arose because we restricted our attention to measures that were functions of transmissions at a single time instant, and measures that were observational rather than counterfactual. In this paper, we consider alternative definitions, including one that is a function of transmissions at multiple time instants, one that is counterfactual, and a new observational definition. We show that a definition of information flow based on counterfactual causal influence (CCI) guarantees the existence of information paths while also having no orphans. We also prove that no observational definition of information flow that satisfies the information path property can match CCI in every instance. Furthermore, each of the definitions we examine (including CCI) is shown to have examples in which the information flow can take a non-intuitive path. Nevertheless, we believe our framework remains more amenable to drawing clear interpretations than classical tools used in neuroscience, such as Granger Causality.The full version of this paper is available online [1].