Self-organizing Network Learning of Sub-millisecond Temporal Coded Information
Ken‐ichi Amemori, Shin Ishii · 1998
In this article, we report a simulation result of unsupervised network learning characterized as temporally and spatially local. After the learning, the network preserves an input sequence whose intervals vary in sub-millisecond order, which is much smaller than the spike emission interval of the neurons. This is achieved by the forming of systematic local structures. This formation is done by selecting appropriate connections from the input neurons and other processing neurons. As a result, the network successfully becomes a sub-millisecond temporal information processing system in a self-organizing manner. KEYWORDS: sub-millisecond temporal coding, unsupervised learning, network dynamics. 1. Introduction A temporal structure of a neuronal activity has been considered to play an important role in defining function of neuronal assemblies. It has also been considered that the behavior of a single neuron is highly dependent on the timing of its input spikes [1, 2]. According to Softky...