Self-Regulation Mechanism of Temporally Asymmetric Hebbian Plasticity

• Published in: Neural computation Vol. 14; no. 12; pp. 2883 - 2902
• Main Authors: Matsumoto, Narihisa, Okada, Masato
• Format: Journal Article
• Language: English
• Published: One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 01.12.2002
• Subjects: Animals; Biological and medical sciences; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; Humans; Learning. Memory; Long-Term Potentiation - physiology; Mathematics; Models, Neurological; Nerve Net - physiology; Neuronal Plasticity - physiology; Numerical analysis; Numerical analysis. Scientific computation; Numerical simulation; Probability and statistics; Probability theory and stochastic processes; Psychology. Psychoanalysis. Psychiatry; Psychology. Psychophysiology; Reaction Time - physiology; Sciences and techniques of general use; Special processes (renewal theory, markov renewal processes, semi-markov processes, statistical mechanics type models, applications); Synapses - physiology; Theories; Spike; Computer simulation; Covariance analysis; Interference; Long term potentiation; Probability distribution; Long term variation; Neural network; Spatiotemporal pattern; Storage; Synaptic plasticity; Recursive method; Long term depression; Neural computition; Learning algorithm
• ISSN: 0899-7667; 1530-888X
• DOI: 10.1162/089976602760805322

Recent biological experimental findings have shown that synaptic plasticity depends on the relative timing of the pre- and postsynaptic spikes. This determines whether long-term potentiation (LTP) or long-term depression (LTD) is induced. This synaptic plasticity has been called temporally asymmetric Hebbian plasticity (TAH). Many authors have numerically demonstrated that neural networks are capable of storing spatiotemporal patterns. However, the mathematical mechanism of the storage of spatiotemporal patterns is still unknown, and the effect of LTD is particularly unknown. In this article, we employ a simple neural network model and show that interference between LTP and LTD disappears in a sparse coding scheme. On the other hand, the covariance learning rule is known to be indispensable for the storage of sparse patterns. We also show that TAH has the same qualitative effect as the covariance rule when spatiotemporal patterns are embedded in the network.