Inter-Layer Correlation in a Feed-Forward Network with Intra-Layer Common Noise

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概要

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• Neural networks generate correlated neural activities. In a multi-layer network, experimental studies have shown that spike correlations appear within a layer and between different layers. It is input common among neurons in each layer that realizes such correlated activities. Theoretical studies have demonstrated that common input given to neurons within a layer, which we call ``intra-layer common noise'', generates spike correlation within the layer, which is ``intra-layer correlation'', in a feed-forward network. However, it has not been studied whether the common noise can generate spike correlation between different layers, which is ``inter-layer correlation''. In this study, we constructed a theory of inter-layer correlation and calculated the theoretical values of the inter-layer correlation in a multi-layer feed-forward network with intra-layer common noise. Our theory revealed that the common noise generates the inter-layer correlation, which coincided with results of simulation.

• 2013-06-15

著者

• Okada Masato

  Graduate School Of Engineering Science Osaka University

• Igarashi Yasuhiko

  Graduate School of Frontier Science, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan

• Nagata Kenji

  Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan

• Karakida Ryo

  Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan

• Nagata Kenji

  Graduate School of Frontier Science, The University of Tokyo

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