Transactions of the Institute of Systems, Control and Information Engineers
Online ISSN : 2185-811X
Print ISSN : 1342-5668
ISSN-L : 1342-5668
Self-Organization and Association for Fine Spatio-Temporal Spike Sequences
Kenichi AMEMORIShin ISHII
Author information
JOURNALS FREE ACCESS

2000 Volume 13 Issue 7 Pages 308-317

Details
Abstract

In this paper, we discuss unsupervised learning for a temporally precise sequence. A network of leaky integrate-and-fire neurons is able to learn a fine spatio-temporal pattern, when the neurons are provided many excitatory random inputs. This unsupervised learning is achieved by selecting appropriate connections in the network. After learning, the trained network works as an associative memory with high temporal precision. Namely, it distinguishes the training sequence through filtering the disarranged sequence according to its correlation value with the training sequence.

Information related to the author
© The Institute of Systems, Control and Information Engineers
Previous article Next article
feedback
Top