title: Self-regulation mechanism of temporally asymmetric Hebbian plasticity.
abstract: 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.
authors.affiliation: Graduate School of Science and Engineering, Saitama University, Saitama 338-8570, Japan. xmatumo@brain.riken.go.jp
authors.forename: Narihisa
authors.initials: N
authors.lastname: Matsumoto
authors.forename: Masato
authors.initials: M
authors.lastname: Okada
mesh_terms: D000818:Animals; D006801:Humans; D017774:Long-Term Potentiation / Q000502:physiology; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D009473:Neuronal Plasticity / Q000502:physiology*; D011930:Reaction Time / Q000502:physiology; D013569:Synapses / Q000502:physiology*