Competitive stdp-based spike pattern learning
Neural Computation
Coexistence of Cell Assemblies and STDP
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
A stimulus-response neural network model prepared by top-down signals
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
A reafferent and feed-forward model of song syntax generation in the Bengalese finch
Journal of Computational Neuroscience
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Spike-timing-dependent synaptic plasticity (STDP), which depends on the temporal difference between pre-and postsynaptic action potentials, is observed in the cortices and hippocampus. Although several theoretical and experimental studies have revealed its fundamental aspects, its functional role remains unclear. To examine how an input spatiotemporal spike pattern is altered by STDP, we observed the output spike patterns of a spiking neural network model with an asymmetrical STDP rule when the input spatiotemporal pattern is repeatedly applied. The spiking neural network comprises excitatory and inhibitory neurons that exhibit local interactions. Numerical experiments show that the spiking neural network generates a single global synchrony whose relative timing depends on the input spatiotemporal pattern and the neural network structure. This result implies that the spiking neural network learns the transformation from spatiotemporal to temporal information. In the literature, the origin of the synfire chain has not been sufficiently focused on. Our results indicate that spiking neural networks with STDP can ignite synfire chains in the cortices.