Mapping Information Flow in Sensorimotor Networks

Abstract

Biological organisms continuously select and sample information used by their neural structures for perception and action, and for creating coherent cognitive states guiding their autonomous behavior. Information processing, however, is not solely an internal function of the nervous system. Here we show, instead, how sensorimotor interaction and body morphology can induce statistical regularities and information structure in sensory inputs and within the neural control architecture, and how the flow of information between sensors, neural units, and effectors is actively shaped by the interaction with the environment. We analyze sensory and motor data collected from real and simulated robots and reveal the presence of information structure and directed information flow induced by dynamically coupled sensorimotor activity, including effects of motor outputs on sensory inputs. We find that information structure and information flow in sensorimotor networks (a) is spatially and temporally specific; (b) can be affected by learning, and (c) can be affected by changes in body morphology. Our results suggest a fundamental link between physical embeddedness and information, highlighting the effects of embodied interactions on internal (neural) information processing, and illuminating the role of various system components on the generation of behavior. How neurons encode and process information is a key problem in computational biology and neuroscience. In this paper, Lungarella and Sporns present a novel application of computational methods to the integration of neural and sensorimotor processes at the systems-level scale. The central result of their study is that sensorimotor interaction and body morphology can induce statistical regularities and information structure in sensory inputs and within the neural control architecture. The informational content of inputs is thus not independent of output, and the authors suggest that neural coding needs to be considered in the context of the “embeddedness” of the organism within its eco-niche. Using robots and nonlinear time-series analysis techniques, they investigate how the flow of information between sensors, neural units, and effectors is actively shaped by interaction with the environment. This study represents a first step towards the development of an explicit quantitative framework that unifies neural and behavioral processes. Such a framework could also shed significant new light on key constraints shaping the evolution and development of nervous systems and their behavioral and cognitive capacities. In addition, it could provide an important design principle to guide the construction of more efficient artificial cognitive systems.