The situated HKB model

Together with Manuel Bedia, Xabier Barandiaran and Bruno Santos I just published in Frontiers in Computational Neuroscience a paper entitled ‘The situated HKB model: how sensorimotor spatial coupling can alter oscillatory brain dynamics’. This paper explores the role of a situated and embodied interaction with the world in the patterns of neural oscillation. This is explored in what we call the situated HKB model: a robotic model that performs a gradient climbing task and whose “brain” is modeled by the HKB equation. With this model we can precisely quantify and qualitatively describe how the properties of the system, when studied in coupled conditions, radically change in a manner that cannot be deduced from the decoupled ‘brain’ alone.

Aguilera, M, Bedia, M.G., Santos, B.A., Barandiaran, X.E. (2013). The Situated HKB Model: how sensorimotor spatial coupling can alter oscillatory brain dynamics. Frontiers in Computational Neuroscience 7 (2013): 117. doi:10.3389/fncom.2013.00117.

https://i1.wp.com/c431376.r76.cf2.rackcdn.com/46981/fncom-07-00117-HTML/image_m/fncom-07-00117-g006.jpg        https://i0.wp.com/c431376.r76.cf2.rackcdn.com/46981/fncom-07-00117-HTML/image_m/fncom-07-00117-g010.jpg

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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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