Burst Synchronization in Two Pulse-Coupled Resonate-and-Fire Neuron Circuits

Nakada, Kazuyoshi; Asai, Tetsuya; Hayashi, H.

doi:10.1007/978-0-387-34749-3_30

Professional Practice in Artificial Intelligence

DOI: 10.1007/978-0-387-34749-3_30

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Burst Synchronization in Two Pulse-Coupled Resonate-and-Fire Neuron Circuits

Kazuyoshi Nakada

Tetsuya Asai

H. Hayashi

Abstract: The present paper addresses burst synchronization in out of phase observed in two pulse-coupled resonate-and-fire neuron (RFN) circuits. The RFN circuit is a silicon spiking neuron that has second-order membrane dynamics and exhibits fast subthreshold oscillation of membrane potential. Due to such dynamics, the behavior of the RFN circuit is sensitive to the timing of stimuli. We investigated the effects of the sensitivity and the mutual interaction on the dynamic behavior of two pulse-coupled RFN circuits, an… Show more

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Cited by 3 publications

(2 citation statements)

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“…However, the influence of after-spike reset on the synchronization states were not considered. We found burst synchronization in out of phase and bifurcation phenomena depending on reset value [5]. We analyzed the stability of such burst synchronization in detail.…”

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confidence: 92%

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Synchronization properties of pulse-coupled resonate-and-fire neuron circuits and their application

Nakada

Asai

Hayashi

2007

International Congress Series

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Introduction: Recent advances in neuroscience suggest that spiking neurons in cortical area are classified into two categories: integrators and resonators [1]. These neurons are different in the sensitivity to the timing of stimulus, and therefore show different synchronization properties in pulse-coupled systems. It is interested that the distribution of such neurons are layer-specific and depends on whether excitatory or inhibitory from a point of view of information processing in the cortical area.Functional networks of silicon spiking neurons have been progressing in the field of neuromorphic engineering, most of which are constructed from electronic analogues of integrators, such as the integrate-and-fire neuron (IFN) circuit. In spite of possible contributions, it has been rarely considered to implement an analog integrated circuit for resonators into the silicon spiking neural networks.In this paper, we will first describe an analog integrated circuit for a resonate-and-fire neuron (RFN) model, which is the simplest resonator proposed by Izhikevich [1]. We will then describe curious synchronization phenomena in two pulse-coupled RFN circuits. We will further describe synchronization properties of a system of globally coupled RFN circuits with inhibitory coupling and its application for neuromorphic noise shaping. We will finally discuss about complementary roles of the RFN circuits in the IFN circuits in very large-scale integration (VLSI) of the silicon spiking neural networks.

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confidence: 92%

“…Burst Synchronization in Two Pulsed-Coupled RFN Circuits: We have investigated synchronization states in two pulse-coupled RFN circuits [5]. So far the existence of in phase and anti phase states in two pulse-coupled RFN model has been reported [2].…”

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confidence: 99%

Synchronization properties of pulse-coupled resonate-and-fire neuron circuits and their application

Nakada

Asai

Hayashi

2007

International Congress Series

Self Cite

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On a standing wave Central Pattern Generator and the coherence problem

Jonckheere

Lohsoonthorn

Musuvathy

et al. 2010

Biomedical Signal Processing and Control

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Silicon Modeling of the Mihalaş–Niebur Neuron

Folowosele

Hamilton

Etienne‐Cummings

2011

IEEE Trans. Neural Netw.

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There are a number of spiking and bursting neuron models with varying levels of complexity, ranging from the simple integrate-and-fire model to the more complex Hodgkin-Huxley model. The simpler models tend to be easily implemented in silicon but yet not biologically plausible. Conversely, the more complex models tend to occupy a large area although they are more biologically plausible. In this paper, we present the 0.5 μm complementary metal-oxide-semiconductor (CMOS) implementation of the Mihalaş-Niebur neuron model--a generalized model of the leaky integrate-and-fire neuron with adaptive threshold--that is able to produce most of the known spiking and bursting patterns that have been observed in biology. Our implementation modifies the original proposed model, making it more amenable to CMOS implementation and more biologically plausible. All but one of the spiking properties--tonic spiking, class 1 spiking, phasic spiking, hyperpolarized spiking, rebound spiking, spike frequency adaptation, accommodation, threshold variability, integrator and input bistability--are demonstrated in this model.

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Burst Synchronization in Two Pulse-Coupled Resonate-and-Fire Neuron Circuits

Cited by 3 publications

References 19 publications

Synchronization properties of pulse-coupled resonate-and-fire neuron circuits and their application

Synchronization properties of pulse-coupled resonate-and-fire neuron circuits and their application

On a standing wave Central Pattern Generator and the coherence problem

Silicon Modeling of the Mihalaş–Niebur Neuron

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