Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent

Abstract

Article (Accepted Version) http://sro.sussex.ac.uk Moioli, Renan C, Vargas, Patricia A and Husbands, Phil (2012) Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent. Biological Cybernetics, 106 (6-7). pp. 407-427. ISSN 0340-1200 This version is available from Sussex Research Online: http://sro.sussex.ac.uk/40960/ This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the URL above for details on accessing the published version. Copyright and reuse:Sussex Research Online is a digital repository of the research output of the University.Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available.Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Noname manuscript No. (will be inserted by the editor)Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent Renan C. Moioli, Patricia A. Vargas, Phil Husbands Abstract Oscillatory activity is ubiquitous in nervous systems, with solid evidence that synchronisation mechanisms underpin cognitive processes. Nevertheless, its informational content and relationship with behaviour are still to be fully understood. Additionally, cognitive systems cannot be properly appreciated without taking into account brain -body -environment interactions. In this paper, we developed a model based on the Kuramoto Model of coupled phase oscillators to explore the role of neural synchronisation in the performance of a simulated robotic agent in two different minimally cognitive tasks. We show that there is a statistically significant difference in performance and evolvability depending on the synchronisation regime of the network. In both tasks, a combination of information flow and dynamical analyses show that networks with a definite, but not too strong, propensity for synchronisation are more able to reconfigure, to organise themselves functionally and to adapt to different behavioural conditions. The results highlight the asymmetry of information flow and its behavioural correspondence. Importantly it also shows that neural synchronisation dynamics, when suitably flexible and reconfigurable, can generate minimally cognitive embodied behaviour.

Hidden information transfer in an autonomous swinging robot

“…The dynamics of embodied agent-environment systems are increasingly analysed using information theory (Lungarella and Sporns, 2006;Pfeifer et al, 2007b;Bertschinger et al, 2008;Klyubin et al, 2008;Pitti et al, 2009;Williams and Beer, 2010;Moioli et al, 2012;Schmidt et al, 2012). This paper adopts this approach and demonstrates a phenomenon that is consistent with the analogy to communications, but thus far seemingly overlooked in studies of information transfer in embodied agents.…”

Evolutionary Robotics and Neuroscience

“…This work is an initial step along a path that could eventually provide insights into the role of synchronised (and unsynchronised) neuronal dynamics in the generation of cognitive and sensorimotor behaviours. For an extended discussion of related work see (Moioli et al 2012, Santos et al 2012.…”

Active Role of Self-Sustained Neural Activity on Sensory Input Processing: A Minimal Theoretical Model

“…We implemented a brain-body-environment system where a simulated robot, controlled by a network of self-oscillating Kuramoto oscillators, performs a categorical perception task (Moioli et al, 2012;Beer, 2003). The central aim of this work is to investigate how much of the sensory-evoked oscillatory activity is generated by SSA in the neural controller, and how much is generated by sensory inputs, while the agent is engaged in the task.…”

“…There have been several previous investigations in this general area (Asai et al, 2003;Moioli et al, 2012;Moioli & Husbands, 2013;Lee et al, 2018;Zagha et al, 2013;Shim & Husbands, 2015), but here we introduce a different perspective. To our knowledge this is the first attempt to precisely quantify the relative contributions of sensory input and SSA to the evoked signals that are driving a sensorimotor behavior.…”