Information theory of adaptation in neurons, behavior, and mood

Abstract

The ability to make accurate predictions of future stimuli and consequences of one's actions are crucial for the survival and appropriate decision-making. These predictions are constantly being made at different levels of the nervous system. This is evidenced by adaptation to stimulus parameters in sensory coding, and in learning of an up-to-date model of the environment at the behavioral level. This review will discuss recent findings that actions of neurons and animals are selected based on detailed stimulus history in such a way as to maximize information for achieving the task at hand. Information maximization dictates not only how sensory coding should adapt to various statistical aspects of stimuli, but also that reward function should adapt to match the predictive information from past to future.

Figure 1. Information maximization explains transition between tasks in the absence of new sensory stimuli

(a) Example infotaxis trajectory exhibits a transition from local search to global search. The background color-map shows the initial probability distribution of target locations. Search proceeds following an expectation of odor detection but eventually transitions towards straight motion away from the initial maximum. (b) Similar behavior is observed in the case of C. elegans once they are removed from a patch of bacteria. The initial trajectory has lots of turns. After approximately 15 minutes, the animal stops turning and moves away from the local area. (c) Information maximization accounts for the spatial distribution of worm positions at the end of the local search. (d) Information maximization accounts for the duration of local search in C. elegans.

Figure 2. Information theory approach to characterizing mood disorders

(a) Separate neural mechanisms for encoding rewards and punishments can be explained using similar information-theoretic arguments that were used in sensory coding to explain the separation into OFF and ON channels. (b) Following [**56], variations in positive and negative thresholds corresponds to four basic mood states. The setting of these thresholds can be explained by a Bayesian model based on prior outcomes of the animal-environment interactions [**56].