Decomposition of Superimposed Chaotic Spike Sequences by Using the Bifurcating Neuron

Yamaguchi, Akihiro; Yamaguti, Yutaka; Kubo, Michinori

doi:10.1007/978-981-10-8854-4_1

Cited by 2 publications

(5 citation statements)

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“…The bifurcating neuron was introduced by Lee and Farhat 7 . In our previous research 6 , we defined the bifurcating neuron as a form of the spike response model 8 to extend the coupling term based on the phase response.…”

Section: Network Model Of the Bifurcating Neuronmentioning

confidence: 99%

“…The dynamics of a single neuron without the coupling term is the same as with the original bifurcating neuron. The single neuron without coupling exhibits various chaotic dynamics in the inter-spike interval, and the phase of the next firing time is determined by the chaotic one dimensional map of the phase of the last firing time 6,7 . The shape of this chaotic map is determined by the phase shift value, .…”

Section: Network Model Of the Bifurcating Neuronmentioning

confidence: 99%

“…We designed the phase response to strengthen the chaotic synchronization between coupled neurons with the same phase shift value 6 . The positive phase response based on the predicted next firing time is defined as…”

Section: Coupling Model With Phase Responsementioning

confidence: 99%

“…By using the phase responses in Eq. 7and (9), the decomposition of the superimposed chaotic spike sequences with the different phase shift values is possible 6 .…”

Section: Coupling Model With Phase Responsementioning

confidence: 99%

“…The correlated firing among neurons has been observed in visual information processing in the brain 2,3 . The authors have studied the chaotic synchronization of the neural spike response and its application to the segmentation of input images 4 ; feature linking of the visual image of moving objects 5 ; and the decomposition of superimposed chaotic spike sequences 6 . In order to represent multiple information by chaotic synchronized cell assemblies, confirming the coexistence of synchronized assemblies is necessary.…”

Section: Introductionmentioning

confidence: 99%

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Selective synchronization of coupled bifurcating neurons for phase shift of background oscillation

Yamaguchi¹,

Yamaguti²,

Kubo³

2018

JRNAL

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Synchronization in coupled bifurcating neurons was studied from the view point of the selective formation of cell assembly. The bifurcating neuron is a simple chaotic neuron that exhibits chaotic inter-spike interval dynamics via the addition of sinusoidal background oscillation. In this research, we introduced the phase shift of the background oscillation and several types of phase response to the input spike sequence. Sixteen coupled bifurcating neurons with complete bidirectional coupling were numerically simulated and their synchronized behaviors were analyzed. As a result, the formation of chaotically synchronized cell assemblies for the phase shift value were observed.

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Section: Network Model Of the Bifurcating Neuronmentioning

confidence: 99%

Section: Network Model Of the Bifurcating Neuronmentioning

confidence: 99%

Section: Coupling Model With Phase Responsementioning

confidence: 99%

“…By using the phase responses in Eq. 7and (9), the decomposition of the superimposed chaotic spike sequences with the different phase shift values is possible 6 .…”

Section: Coupling Model With Phase Responsementioning

confidence: 99%