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Chaos / Volume 22 / Issue 1 / REGULAR ARTICLES
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Chaos 22, 013104 (2012); http://dx.doi.org/10.1063/1.3676067 (7 pages)

Propagation of spiking regularity and double coherence resonance in feedforward networks

Cong Men1, Jiang Wang1, Ying-Mei Qin1, Bin Deng1, Kai-Ming Tsang2, and Wai-Lok Chan2

1School of Electrical Engineering and Automation, Tianjin University, Tianjin, China
2Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong, China

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(Received 13 September 2011; accepted 19 December 2011; published online 10 January 2012)

We investigate the propagation of spiking regularity in noisy feedforward networks (FFNs) based on FitzHugh-Nagumo neuron model systematically. It is found that noise could modulate the transmission of firing rate and spiking regularity. Noise-induced synchronization and synfire-enhanced coherence resonance are also observed when signals propagate in noisy multilayer networks. It is interesting that double coherence resonance (DCR) with the combination of synaptic input correlation and noise intensity is finally attained after the processing layer by layer in FFNs. Furthermore, inhibitory connections also play essential roles in shaping DCR phenomena. Several properties of the neuronal network such as noise intensity, correlation of synaptic inputs, and inhibitory connections can serve as control parameters in modulating both rate coding and the order of temporal coding.

© 2012 American Institute of Physics

Article Outline

  1. INTRODUCTION
  2. DESCRIPTION OF A FEEDFORWARD NETWORK
  3. PROPAGATION OF FIRING PATTERNS IN NOISY FFNs
  4. DOUBLE COHERENCE RESONANCE IN NOISY FFNs
    1. Propagation of spiking regularity in noisy FFNs
    2. Double coherence resonance in noisy FFNs
  5. DISCUSSION AND CONCLUSION

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KEYWORDS and PACS

Keywords

neural nets, noise, synchronisation

PACS

  • 05.45.Xt

    Synchronization; coupled oscillators

ARTICLE DATA

Digital Object Identifier
http://dx.doi.org/10.1063/1.3676067

PUBLICATION DATA

ISSN

1054-1500 (print)  
1089-7682 (online)

Publisher

$abstract.society.value is a Member of CrossRef American Institute of Physics

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Figures (click on thumbnails to view enlargements)

FIG.1
(Color online) A schematic of multilayer feedforward network with 200 FHN neurons in each layer. P denotes the connection probability between the nearby layers.

FIG.1 Download High Resolution Image (.zip file) | Export Figure to PowerPoint

FIG.2
(Color online) Output of firing rate rout (layer 8 as a whole) versus the input firing rate r1 (layer 1 as a whole) in the FFN without noise.

FIG.2 Download High Resolution Image (.zip file) | Export Figure to PowerPoint

FIG.3
(Color online) Effects of noise on propagation of firing rate and spiking regularity in the multi-layers with P = 0.3. (a) Average firing rate r in each layer (as a whole) of FFN with different noise intensities D. (b) Propagation of spiking regularity R in each layer of FFN with different D.

FIG.3 Download High Resolution Image (.zip file) | Export Figure to PowerPoint

FIG.4
(Color online) Effects of noise and linking probability on firing rate and spiking regularity. (a) Firing rates r versus linking probability with different noise intensities. (b) Spiking regularity R versus linking probability with different noise intensities.

FIG.4 Download High Resolution Image (.zip file) | Export Figure to PowerPoint

FIG.5
(Color online) Propagation of spiking regularity and synchrony in the noisy multi-layer network. (a) Spiking regularity R as a function of layers and noise intensity D with P = 0.2. (b) Spiking regularity R as a function of layers and noise intensity D with P = 0.4. (c) Synchronized states in different layers with P = 0.2. (d) Synchronized states in different layers with P = 0.4.

FIG.5 Download High Resolution Image (.zip file) | Export Figure to PowerPoint

FIG.6
(Color online) Comparison of raster plots in the FFNs with different noisy intensities and connections. (a) Network with lower noise intensity (D = 10-3.3) and connection probability P = 0.4. (b) Network with medium noise intensity (D = 10-2.8) and P = 0.4. (c) Network with higher noise intensity (D = 10-1.4) and P = 0.4. (d) Network with noise intensity D = 10-2.8 and P = 0.8.

FIG.6 Download High Resolution Image (.zip file) | Export Figure to PowerPoint

FIG.7
(Color online) Degrees of average temporal regularity R and spatial coherence K of 8th layer as a function of noise intensity D and linking probability P. (a) The color denotes the value of temporal regularity R. (b) The color denotes the value of spatial coherence K.

FIG.7 Download High Resolution Image (.zip file) | Export Figure to PowerPoint

FIG.8
(Color online) Degrees of average spiking regularity R of 8th layer as a function of noise intensity D and linking probability P with different ratios of inhibitory connections. The color denotes the value of R. (a) Pinh = 0.05; (b) Pinh = 0.1; and (c) Pinh = 0.2. (d) The maximal R value for each P versus linking probabilities P of the FFN with different Pinh.

FIG.8 Download High Resolution Image (.zip file) | Export Figure to PowerPoint



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