Title
Learning by Stimulation Avoidance Scale to Large Neural Networks
Authors
Atsushi Masumori, Lana Sinapayen, Takashi Ikegami
Schedule
Date: Thursday 7 Sept
Talk Time: TBA
Session: Neural networks 10:30
Keywords
Learning by stimulation avoidance, LSA, Spiking neural networks, Spike-timing dependent plasticity, Adaptive behavior, Homeostasis
Abstract
Spiking neural networks with spike-timing dependent plasticity (STDP) can learn to avoid the external stimulations spontaneously. This principle is called “Learning by Stimulation Avoidance” (LSA) and can be used to reproduce learning experiments on cultured biological neural networks. LSA has promising potential, but its application and limitations have not be studied extensively. This paper focuses on the scalability of LSA for large networks and shows that LSA works well in small networks (100 neurons) and can be scaled to networks up to approximately 3,000 neurons.
