A Memory-Based STDP Rule for Stable Attractor Dynamics in Boolean Recurrent Neural Networks

Authors: Jérémie Cabessa, A. Villa
Publication year: 2019
Publication type: Konferenční příspěvek (mezinárodní konf.)
Periodical: IJCNN 2019. International Joint Conference on Neural Networks Proceedings
Projects: FoNeCo: Analytical Foundations of Neurocomputing
Bibliographic citation: CABESSA, Jérémie, VILLA, A. A Memory-Based STDP Rule for Stable Attractor Dynamics in Boolean Recurrent Neural Networks. In: IJCNN 2019. International Joint Conference on Neural Networks Proceedings. New York: IEEE, 2019, č. článku N-20311. ISBN 978-1-7281-1985-4
Dostupné z: doi:10.1109/IJCNN.2019.8852043
DOI: 10.1109/IJCNN.2019.8852043
ID: 503755

Abstract

We consider a simplified Boolean model of the basal ganglia-thalamocortical network, and study the effect of a spiketiming- dependent plasticity (STDP) rule on the stabilization ofits attractor dynamics. More precisely, we introduce an adaptive STDP rule which constantly updates its learning rate based on the attractors that the network encounters during a window of past time steps. This so-called network memory is assumed to be dynamic: its duration is step-wise increased every time a trigger input pattern is detected, and is decreased otherwise. In this context, we show that well-adjusted trigger inputs can fine tune the network memory and its associated STDP rule in such a way to drive the network into stable and rich attractor dynamics. We discuss how this feature might be related to reward learning processes in the neurobiological context