Sparse and Dense Encoding in Layered Associative Network of Spiking Neurons

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  • Ishibashi Kazuya
    Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo
  • Hamaguchi Kosuke
    Brain Science Institute
  • Okada Masato
    Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo Brain Science Institute

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A synfire chain is a simple neural network model which can transmit stable synchronous spikes called a pulse packet. However how synfire chains coexist in one network remains to be elucidated. We have studied the activity of a layered associative network of leaky integrate-and-fire neurons which connections are embedded with memory patterns by the Hebbian learning rule. We analyze their activity by the Fokker–Planck method. In our previous report, when a half of neurons belongs to each memory pattern (pattern rate F=0.5), the temporal profiles of the network activity is split into temporally clustered groups called sublattices under certain input conditions. In this study, we show that when the network is sparsely connected (F<0.5), synchronous firings of the memory pattern are promoted. On the contrary, the densely connected network (F>0.5) inhibit synchronous firings. The basin of attraction and the storage capacity of the embedded memory patterns also depend on the sparseness of the network. We show that the sparsely (densely) connected networks enlarge (shrink) the basion of attraction and increase (decrease) the storage capacity.

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