Self-Organized Criticality, Plasticity and Sensorimotor Coupling. Explorations with a Neurorobotic Model in a Behavioural Preference Task

It’s been some time since I started to develop a model of relational in a robot’s oscillatory neural controller. For a couple of years I have been intermittently working in a simulated agent in a behavioural preference task controlled by a homeostatic oscillator network, in which the relational variable that is kept constant is the phase relation between one oscillator and its surroundings.

During the last year I have been working in different results around this model, and some of the first results are already published in this paper in PLOS ONE. In this paper (written together with Xabier Barandiaran, Manuel Bedia and Paco Serón), we analyse long-range correlations in the form of 1/f noise and self-organized criticality in the agent’s behaviour, and its relation with synaptic plasticity and sensorimotor coupling. We show that the emergence of self-organized criticality and 1/ƒ noise in our model is the result of three simultaneous conditions: a) non-linear interaction dynamics capable of generating stable collective patterns, b) internal plastic mechanisms modulating the sensorimotor flows, and c) strong sensorimotor coupling with the environment that induces transient metastable neurodynamic regimes. When one of these conditions is not met, a robust critical regime is unable to emerge.

About maguilera0

Miguel Aguilera is a Postdoctoral Research Fellow at the IAS Research Center for Life, Mind and Society at the University of the Basque Country. He has been a visiting researcher at the Cognitive Science Program at Indiana University and the Ikegami Lab in the Department of General Systems Studies at the University of Tokyo, and a postdoctoral fellow at the University of the University of Zaragoza and the University of the Balearic Islands. His research focuses on autonomy in biological and social systems from an interdisciplinary perspective, integrating insights from cognitive science, theoretical neuroscience, computational modeling, adaptive behaviour, and complex systems. It combines nonlinear and dynamical models, evolutionary algorithms, and mathematical analysis from dynamical systems, network and information theory, to generate and understand situated and embodied models of agency in the realms of artificial life and evolutionary robotics, computational neuroscience, collective intelligence practices and socio-technical systems.
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