Mapping Information Flow in Sensorimotor Networks

By Max Lungarella, Olaf Sporns. PLoS Comput. Biol., 2006

334 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Global and Planetary Change, Social Psychology

View Paper: https://www.semanticscholar.org/paper/e5a9af7322e801c48117f1be7c74fd568fa749ab

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Citations in the corpus

JIDT: An Information-Theoretic Toolkit for Studying the Dynamics of Complex Systems (2014, background): “, (Lungarella and Sporns, 2006; Lizier et al., 2008c, 2011c; Obst et al., 2010; Williams and Beer, 2011; Barnett and Bossomaier, 2012; Boedecker et al., 2012), and in computational neuroscience, in particular, e.”
Wiener-Granger Causality: A well established methodology (2011, neutral): “First, modelling studies have demonstrated that the same underlying (physical) network structure can give rise to multiple distinct dynamical connectivity patterns (Seth, 2005, 2008; Lungarella and Sporns, 2006) depending on how the system interacts with its environment.”
Empowerment for continuous agent—environment systems (2011, neutral): “Other related work can be found in Kaplan & Oudeyer, 2004; Lungarella, Pegors, Bulwinkle, & Sporns, 2005; Lungarella & Sporns, 2005, 2006; Prokopenko, Gerasimov, & Tanev, 2006; Sporns & Lungarella, 2006; Steels, 2004.”
Analyzing information flow in brain networks with nonparametric Granger causality (2008, methodology): “Other techniques , including phase-dynamics approach (Rosenblum and Pikovsky, 2001) and transfer entropy (Schreiber, 2000; Lungarella and Sporns, 2006), have also been attempted for the same purpose.”
An information-theoretic primer on complexity, self-organization, and emergence (2009, background): “We are proposing here to clarify the applicability and exemplify how diﬀerent information channels can be identiﬁed and used.”
Active inference and epistemic value (2015, background): “This is particularly important because the resulting hierarchical representation (deep generative model) can account for sensorimotor regularities produced by action (Lungarella & Sporns, 2006; O’Regan & Noë, 2001).”
The Natural Statistics of Audiovisual Speech (2009, background): “signals that they encounter most often [4].”