Title :
An LTP/LTD perspective on learning rules
Author :
Munro, Paul ; Hernández, Gerardina
Author_Institution :
Sch. of Inf. Sci., Pittsburgh Univ., PA, USA
Abstract :
A single framework is shown to encompass several existing learning rules, separating them into positive and negative terms, respectively corresponding to long-term potentiation (LTP) and long-term depression (LTD) phenomena. Each term is expressed as an integral of a Hebbian product over time, modulated by a kernel function. Carefully chosen kernel functions are shown to exhibit computational properties of temporal contrast enhancement and prediction. Some preliminary simulation results are presented for illustration purposes
Keywords :
Hebbian learning; bioelectric potentials; neural nets; time series; Hebbian product; LTP/LTD perspective; computational properties; kernel function; learning rules; long-term depression; long-term potentiation; negative terms; neurophysiology; positive terms; temporal contrast enhancement; Computational modeling; Frequency measurement; Hebbian theory; Intelligent systems; Kernel; Laboratories; Mathematical model; Nerve fibers; Neurons; Pulse width modulation;
Conference_Titel :
Neural Information Processing, 1999. Proceedings. ICONIP '99. 6th International Conference on
Conference_Location :
Perth, WA
Print_ISBN :
0-7803-5871-6
DOI :
10.1109/ICONIP.1999.843955
