Kenichi AMEMORI and Shin ISHII
Unsupervised Learning of Sub-Millisecond
Temporal Coded Sequence by a Network of
"Coincidence Detector" Neurons
Abstract:In this paper, we examine unsupervised learning for sequence of sub-millisecond temporal
coded information in a network of neurons, which are assumed to have high temporal
resolution. The learning scheme is based on a spatially and temporally local one, i.e.,
unsupervised Hebbian learning. The input sequence is temporal information that needs
an accuracy on the order of sub-milliseconds. Through the learning, segregation of the
synaptic connections occurs to form systematic structures in the network. Namely, the
network develops in a self-organizing manner. The trained network works like an "associative
memory" of the learned sequence, namely, the network responds when a newly
input sequence is similar to the learned sequence. Consequently, the assembly of neurons
is able to learn and distinguish an input sequence that carries information on the order
of sub-milliseconds, although the spike emission intervals of the neurons are on the order
of milliseconds.