Animal behavior often involves a temporally ordered sequence of actions learned from experience. Here we describe simulations of interconnected networks of spiking neurons that learn to generate patterns of activity in correct temporal order. The simulation consists of large-scale networks of thousands of excitatory and inhibitory neurons that exhibit short-term synaptic plasticity and spike-timing dependent synaptic plasticity. The neural architecture within each area is arranged to evoke winner-take-all (WTA) patterns of neural activity that persist for tens of milliseconds. In order to generate and switch between consecutive firing patterns in correct temporal order, a reentrant exchange of signals between these areas was necessary. To demonstrate the capacity of this arrangement, we used the simulation to train a brain-based device responding to visual input by autonomously generating temporal sequences of motor actions.
Keywords: neurorobotics, sequence learning, spiking network, winner-take-all, motor control and learning/plasticity, spike-timing dependent plasticity, sensorimotor control, large-scale spiking neural networks
Citation: McKinstry JL and Edelman GM (2013) Temporal sequence learning in winner-take-all networks of spiking neurons demonstrated in a brain-based device. Front. Neurorobot. 7:10. doi: 10.3389/fnbot.2013.00010
Received: 21 March 2013; Accepted: 20 May 2013;
Published online: 06 June 2013.
Edited by:Jeffrey L. Krichmar, University of California, Irvine, USA
Reviewed by:Emre O. Neftci, Institute of Neuroinformatics, Switzerland
Copyright © 2013 McKinstry and Edelman. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.
*Correspondence: Jeffrey L. McKinstry, The Neurosciences Institute, 800 Silverado St., Suite 302, San Diego, 92037-4234 CA, USA e-mail: email@example.com