Is a biological temporal learning rule compatible with learning Synfire chains?

• Published in: 9th International Conference on Artificial Neural Networks: ICANN '99 pp. 551 - 556
• Main Author: Sterratt, D.C
• Format: Conference Proceeding Journal Article
• Language: English
• Published: London IEE 1999
• Subjects: Bioelectricity; Electrical activity in neurophysiological processes; External and internal data communications, nerve conduction and synaptic transmission; General, theoretical, and mathematical biophysics; Neural nets; Systems theory applications in biology and medicine; neuronal firing threshold; biological temporal learning; synfire chains; bioelectric potentials; brain models; recall speed; supervised learning; neurophysiology; discrete system; neural nets
• ISBN: 0852967217; 9780852967218
• ISSN: 0537-9989
• DOI: 10.1049/cp:19991167

The author investigates how a biologically realistic temporal learning rule and the neuronal firing threshold jointly determine the recall speed of a synfire chain trained by sequential activation of its nodes. Numerical analysis of an idealised system of discrete spike response model neurons yields the relationship between threshold and speed of recall, in particular showing that recall is not possible at all speeds and that recall may not be possible at the speed at which the chain was trained. A continuous approximation to the discrete system is analytically more tractable but does not reflect the stability of the system accurately.