Controlling the spontaneous spiking regularity via channel blocking on Newman-Watts networks of Hodgkin-Huxley neurons

Özer, Mahmut; Perc, Matjaž; Uzuntarla, Muhammet

doi:10.1209/0295-5075/86/40008

Cited by 118 publications

(57 citation statements)

References 44 publications

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“…8,[10][11][12] Array-enhanced coherence resonance is found in an array of coupled FitzHugh-Nagumo neurons. 8,9 For more complex coupling, optimal configuration of network exists when predefined topology structures are given [13][14][15][16] or self-organized connections are developed based on spike-timing-dependent plasticity (STDP) learning rules. 17 Multilayer feedforward network (FFN) structure, which is one of the most extensively studied network structure, can characterize the properties of spiking activities in propagation.…”

Section: Introductionmentioning

confidence: 99%

Propagation of spiking regularity and double coherence resonance in feedforward networks

Men

Wang

Qin

et al. 2012

Chaos: An Interdisciplinary Journal of Nonlinear Science

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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.

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Section: Introductionmentioning

confidence: 99%

Propagation of spiking regularity and double coherence resonance in feedforward networks

Men

Wang

Qin

et al. 2012

Chaos: An Interdisciplinary Journal of Nonlinear Science

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show abstract

“…neuronal dynamics [37], only that here this is additionally amplified by the scale-free interaction structure. Indeed, as Fig.…”

mentioning

confidence: 78%

“…The relevance of ion channel noise can also be studied by means of computational models. It is known, for example, that the regularity of spontaneous spike trains can be reduced or enhanced by blocking or poisoning some fraction of sodium or potassium ion channels [34,35], and also that channel blocking can enhance the collective spiking regularity of bi-directionally coupled [36] and small-world neuronal networks [37]. Recently, the development, propagation or robustness of spiral waves observed in the cortex of brain has been addressed via ion channel poisoning [38][39][40].…”

mentioning

confidence: 99%

Can scale-freeness offset delayed signal detection in neuronal networks?

Uzun¹,

Özer²,

Perc³

2014

EPL

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-First spike latency following stimulus onset is of significant physiological relevance. Neurons transmit information about their inputs by transforming them into spike trains, and the timing of these spike trains is in turn crucial for effectively encoding that information. Random processes and uncertainty that underly neuronal dynamics have been shown to prolong the time towards the first response in a phenomenon dubbed noise-delayed decay. Here we study whether Hodgkin-Huxley neurons with a tunable intensity of intrinsic noise might have shorter response times to external stimuli just above threshold if placed on a scale-free network. We show that the heterogeneity of the interaction network may indeed eradicate slow responsiveness, but only if the coupling between individual neurons is sufficiently strong. Increasing the average degree also favors a fast response, but it is less effective than increasing the coupling strength. We also show that noise-delayed decay can be offset further by adjusting the frequency of the external signal, as well as by blocking a fraction of voltage-gated sodium or potassium ion channels. For certain conditions, we observe a double peak in the response time depending on the intensity of intrinsic noise, indicating competition between local and global effects on the neuronal dynamics.

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“…Since a regular network may be biologically questionable due to the brain's sophisticated anatomical structure, random network topologies have been proposed to bridge the gap between artificial and brain networks. In this context, the small-world network topology, proposed by Watts and Storgatz [5], is one of the best models to reflect the functional connectivity and anatomic structure of the brain [6][7][8][9][10][11][12][13]. Moreover, many recent investigations showed that real-life networks such as the World Wide Web, protein interaction networks, email networks, social networks, and metabolic networks, exhibit the small-world property [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Impact of small-world topology on the performance of a feed-forward artificial neural network based on 2 different real-life problems

Erkaymaz¹,

Özer²,

Yumuşak³

2014

Turk J Elec Eng & Comp Sci

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Abstract:Since feed-forward artificial neural networks (FFANNs) are the most widely used models to solve real-life problems, many studies have focused on improving their learning performances by changing the network architecture and learning algorithms. On the other hand, recently, small-world network topology has been shown to meet the characteristics of real-life problems. Therefore, in this study, instead of focusing on the performance of the conventional FFANNs, we investigated how real-life problems can be solved by a FFANN with small-world topology. Therefore, we considered 2 real-life problems: estimating the thermal performance of solar air collectors and predicting the modulus of rupture values of oriented strand boards. We used the FFANN with small-world topology to solve both problems and compared the results with those of a conventional FFANN with zero rewiring. In addition, we investigated whether there was statistically significant difference between the regular FFANN and small-world FFANN model. Our results show that there exists an optimal rewiring number within the small-world topology that warrants the best performance for both problems.

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Controlling the spontaneous spiking regularity via channel blocking on Newman-Watts networks of Hodgkin-Huxley neurons

Cited by 118 publications

References 44 publications

Propagation of spiking regularity and double coherence resonance in feedforward networks

Propagation of spiking regularity and double coherence resonance in feedforward networks

Can scale-freeness offset delayed signal detection in neuronal networks?

Impact of small-world topology on the performance of a feed-forward artificial neural network based on 2 different real-life problems

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