Information transformation from a spatiotemporal pattern to synchrony through STDP network

Ryosuke Hosaka; Tohru Ikeguchi; Hikoichiro Nakamura; Osamu Araki

doi:10.1109/IJCNN.2004.1380170

Information transformation from a spatiotemporal pattern to synchrony through STDP network

Ryosuke Hosaka, Tohru Ikeguchi, Hikoichiro Nakamura, Osamu Araki

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Recent experimental results show the sensitivity of synaptic plasticity to the timing of synaptic events and postsynaptic firings (spike timing dependent plasticity: STDP). Although a number of studies have been made on STDP, little is known about the effect of STDP on the relation between external input patterns to the recurrent neural network and its output spikes. In this study, we examine this relation by computer simulations of a spiking neural network with STDP. We have found that STDP organizes the neural network to transform an external spatiotemporal input pattern into a synchronous firing, and the synchrony is much dependent on the spatiotemporal structure of the external input. This result suggests that the original of the synchrony propagate through feed-forward network may be generated by the recurrent neural network respond to the external input.

Original language	English
Title of host publication	2004 IEEE International Joint Conference on Neural Networks - Proceedings
Pages	1475-1480
Number of pages	6
DOIs	https://doi.org/10.1109/IJCNN.2004.1380170
Publication status	Published - 2004
Externally published	Yes
Event	2004 IEEE International Joint Conference on Neural Networks - Proceedings - Budapest, Hungary Duration: 2004 Jul 25 → 2004 Jul 29

Publication series

Name	IEEE International Conference on Neural Networks - Conference Proceedings
Volume	2
ISSN (Print)	1098-7576

Conference

Conference	2004 IEEE International Joint Conference on Neural Networks - Proceedings
Country/Territory	Hungary
City	Budapest
Period	04/7/25 → 04/7/29

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10.1109/IJCNN.2004.1380170

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Hosaka, R., Ikeguchi, T., Nakamura, H., & Araki, O. (2004). Information transformation from a spatiotemporal pattern to synchrony through STDP network. In 2004 IEEE International Joint Conference on Neural Networks - Proceedings (pp. 1475-1480). (IEEE International Conference on Neural Networks - Conference Proceedings; Vol. 2). https://doi.org/10.1109/IJCNN.2004.1380170

Information transformation from a spatiotemporal pattern to synchrony through STDP network. / Hosaka, Ryosuke; Ikeguchi, Tohru; Nakamura, Hikoichiro et al.

2004 IEEE International Joint Conference on Neural Networks - Proceedings. 2004. p. 1475-1480 (IEEE International Conference on Neural Networks - Conference Proceedings; Vol. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Hosaka, R, Ikeguchi, T, Nakamura, H & Araki, O 2004, Information transformation from a spatiotemporal pattern to synchrony through STDP network. in 2004 IEEE International Joint Conference on Neural Networks - Proceedings. IEEE International Conference on Neural Networks - Conference Proceedings, vol. 2, pp. 1475-1480, 2004 IEEE International Joint Conference on Neural Networks - Proceedings, Budapest, Hungary, 04/7/25. https://doi.org/10.1109/IJCNN.2004.1380170

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abstract = "Recent experimental results show the sensitivity of synaptic plasticity to the timing of synaptic events and postsynaptic firings (spike timing dependent plasticity: STDP). Although a number of studies have been made on STDP, little is known about the effect of STDP on the relation between external input patterns to the recurrent neural network and its output spikes. In this study, we examine this relation by computer simulations of a spiking neural network with STDP. We have found that STDP organizes the neural network to transform an external spatiotemporal input pattern into a synchronous firing, and the synchrony is much dependent on the spatiotemporal structure of the external input. This result suggests that the original of the synchrony propagate through feed-forward network may be generated by the recurrent neural network respond to the external input.",

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AB - Recent experimental results show the sensitivity of synaptic plasticity to the timing of synaptic events and postsynaptic firings (spike timing dependent plasticity: STDP). Although a number of studies have been made on STDP, little is known about the effect of STDP on the relation between external input patterns to the recurrent neural network and its output spikes. In this study, we examine this relation by computer simulations of a spiking neural network with STDP. We have found that STDP organizes the neural network to transform an external spatiotemporal input pattern into a synchronous firing, and the synchrony is much dependent on the spatiotemporal structure of the external input. This result suggests that the original of the synchrony propagate through feed-forward network may be generated by the recurrent neural network respond to the external input.

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Information transformation from a spatiotemporal pattern to synchrony through STDP network

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