Stability Gap between Off- and On-Firing States in a Coupled Ginzburg-Landau Oscillator Neural Network
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概要
- 論文の詳細を見る
An oscillator neural network based on the Ginzburg-Landau equation is proposed in order to investigate associative memory. The embedded patterns include both off- and on-firing states with phase. Neurons encode not only the phase but also the state of the cell. The system has a Lyapunov function with wells corresponding to off- and on-firing states. These two states are characterized by mutual stabilities which are generally different. Each neuron falls into one of the wells, as determined by its dynamics. The storage capacity and the order parameter overlap are studied using numerical simulations and an ordinary self-consistent signal-to-noise analysis. We show that these properties depend crucially on both the potential parameter and the mean activity levels of the patterns. In the case of the sparse coding limit, the storage capacity diverges as in the case of the binary model.
- 理論物理学刊行会の論文
- 2000-10-25
著者
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Kawaguchi Satoshi
Laboratory For Advanced Brain Signal Processing Brain Science Institute Riken
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Kawaguchi Satoshi
Department of Orthopaedic Surgery, Sapporo Medical University School of Medicine
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KAWAGUCHI Satoshi
Laboratory for Advanced Brain Signal Processing Brain Science Institute, RIKEN
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