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Correlated Firing in a Feedforward Network with Mexican-Hat-Type Connectivity

hammer{at}brain.riken.jp, RIKEN, Brain Science Institute, Wako-shi, Saitama, 351-0198 Japan

Masato Okada

okada{at}k.u-tokyo.ac.jp, Department of Complexity Science and Engineering, University of Tokyo, Kashiwa, Chiba, 277-8561, Japan
Intelligent Cooperation and Control, PRESTO, JST, Shibuya-ku, Tokyo, 151-0065, Japan

Michiko Yamana

yamana{at}criepi.denken.or.jp, Central Research Institute of Electric Power Industry, System Engineering Research Laboratory, Komae-shi, Tokyo, 201-8511, Japan

Kazuyuki Aihara

aihara{at}sat.t.u-tokyo.ac.jp, Institute of Industrial Science, University of Tokyo, Meguro, Tokyo 153-8505, Japan, and ERATO Aihara Complexity Modeling Project, JST, Shibuya-ku, Tokyo, 151-0065, Japan

We report on deterministic and stochastic evolutions of firing states through a feedforward neural network with Mexican-hat-type connectivity. The prevalence of columnar structures in a cortex implies spatially localized connectivity between neural pools. Although feedforward neural network models with homogeneous connectivity have been intensively studied within the context of the synfire chain, the effect of local connectivity has not yet been studied so thoroughly. When a neuron fires independently, the dynamics of macroscopic state variables (a firing rate and spatial eccentricity of a firing pattern) is deterministic from the law of large numbers. Possible stable firing states, which are derived from deterministic evolution equations, are uniform, localized, and nonfiring. The multistability of these three states is obtained where the excitatory and inhibitory interactions among neurons are balanced. When the presynapse-dependent variance in connection efficacies is incorporated into the network, the variance generates common noise. Then the evolution of the macroscopic state variables becomes stochastic, and neurons begin to fire in a correlated manner due to the common noise. The correlation structure that is generated by common noise exhibits a nontrivial bimodal distribution. The development of a firing state through neural layers does not converge to a certain fixed point but keeps on fluctuating.

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