Self-organizing rhythmic patterns with spatio-temporal spikes in class i and class II neural networks

Authors:
Ryosuke Hosaka;Tohru Ikeguchi;Kazuyuki Aihara
Affiliations:
Graduate School of Science and Engineering, Saitama University, Saitama, Japan;Graduate School of Science and Engineering, Saitama University, Saitama, Japan;ERATO, JST, Aihara Complexity Modelling Project, Tokyo, Japan
Venue:
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Year:
2006

Citing 3
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Abstract

Regularly spiking neurons are classified into two categories, Class I and Class II, by their firing properties for constant inputs. To investigate how the firing properties of single neurons affect to ensemble rhythmic activities in neural networks, we constructed different types of neural networks whose excitatory neurons are the Class I neurons or the Class II neurons. The networks were driven by random inputs and developed with STDP learning. As a result, the Class I and the Class II neural networks generate different types of rhythmic activities: the Class I neural network generates slow rhythmic activities, and the Class II neural network generates fast rhythmic activities.