Sources of predictive information in dynamical neural networks

Abstract

Behavior involves the ongoing interaction between an organism and its environment. One of the prevailing theories of adaptive behavior is that organisms are constantly making predictions about their future environmental stimuli. However, how they acquire that predictive information is still poorly understood. Two complementary mechanisms have been proposed: predictions are generated from an agent’s internal model of the world or predictions are extracted directly from the environmental stimulus. In this work, we demonstrate that predictive information, measured using mutual information, cannot distinguish between these two kinds of systems. Furthermore, we show that predictive information cannot distinguish between organisms that are adapted to their environments and random dynamical systems exposed to the same environment. To understand the role of predictive information in adaptive behavior, we need to be able to identify where it is generated. To do this, we decompose information transfer across the different components of the organism-environment system and track the flow of information in the system over time. To validate the proposed framework, we examined it on a set of computational models of idealized agent-environment systems. Analysis of the systems revealed three key insights. First, predictive information, when sourced from the environment, can be reflected in any agent irrespective of its ability to perform a task. Second, predictive information, when sourced from the nervous system, requires special dynamics acquired during the process of adapting to the environment. Third, the magnitude of predictive information in a system can be different for the same task if the environmental structure changes.Significance StatementAn organism’s ability to predict the consequences of its actions on future stimuli is considered a strong indicator of its environmental adaptation. However, in highly structured natural environments, to what extent does an agent have to develop specialized mechanisms to generate predictions? To study this, we present an information theoretic framework to infer the source of predictive information in an organism: extrinsically from the environment or intrinsically from the agent. We find that predictive information extracted from the environment can be reflected in any agent and is therefore not a good indicator of behavioral performance. Studying the flow of predictive information over time across the organism-environment system enables us to better understand its role in behavior.