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Coding of Temporally Varying Signals in Networks of Spiking Neurons with Global Delayed Feedback

masuda{at}brain.riken.jp, Laboratory for Mathematical Neuroscience, RIKEN Brain Science Institute, Wako, Japan, and ERATO Aihara Complexity Modelling Project, Japan Science and Technology Agency, Tokyo, Japan

Brent Doiron

bdoiron{at}science.uottawa.ca, Physics Department, University of Ottawa, Ottawa, Canada, and Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, Canada

André Longtin

alongtin{at}physics.uottawa.ca, Physics Department, University of Ottawa, Ottawa, Canada

Kazuyuki Aihara

aihara{at}sat.t.u-tokyo.ac.jp, Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan, and ERATO Aihara Complexity Modelling Project, Japan Science and Technology Agency, Tokyo, Japan

Oscillatory and synchronized neural activities are commonly found in the brain, and evidence suggests that many of them are caused by global feedback. Their mechanisms and roles in information processing have been discussed often using purely feedforward networks or recurrent networks with constant inputs. On the other hand, real recurrent neural networks are abundant and continually receive information-rich inputs from the outside environment or other parts of the brain. We examine how feedforward networks of spiking neurons with delayed global feedback process information about temporally changing inputs. We show that the network behavior is more synchronous as well as more correlated with and phase-locked to the stimulus when the stimulus frequency is resonant with the inherent frequency of the neuron or that of the network oscillation generated by the feedback architecture. The two eigenmodes have distinct dynamical characteristics, which are supported by numerical simulations and by analytical arguments based on frequency response and bifurcation theory. This distinction is similar to the class I versus class II classification of single neurons according to the bifurcation from quiescence to periodic firing, and the two modes depend differently on system parameters. These two mechanisms may be associated with different types of information processing.

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