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Analysis of Chaotic Resonance in Izhikevich Neuron Model.

Nobukawa S, Nishimura H, Yamanishi T, Liu JQ - PLoS ONE (2015)

Bottom Line: We found the existence of two distinctive states, a chaotic state involving primarily turbulent movement and an intermittent chaotic state.Through computer simulations, we confirmed that both chaotic states in CR can sensitively respond to weak signals.Moreover, we found that the intermittent chaotic state exhibited a prompter response than the chaotic state with primarily turbulent movement.

View Article: PubMed Central - PubMed

Affiliation: Department of Management Information Science, Fukui University of Technology, Fukui, Japan.

ABSTRACT
In stochastic resonance (SR), the presence of noise helps a nonlinear system amplify a weak (sub-threshold) signal. Chaotic resonance (CR) is a phenomenon similar to SR but without stochastic noise, which has been observed in neural systems. However, no study to date has investigated and compared the characteristics and performance of the signal responses of a spiking neural system in some chaotic states in CR. In this paper, we focus on the Izhikevich neuron model, which can reproduce major spike patterns that have been experimentally observed. We examine and classify the chaotic characteristics of this model by using Lyapunov exponents with a saltation matrix and Poincaré section methods in order to address the measurement challenge posed by the state-dependent jump in the resetting process. We found the existence of two distinctive states, a chaotic state involving primarily turbulent movement and an intermittent chaotic state. In order to assess the signal responses of CR in these classified states, we introduced an extended Izhikevich neuron model by considering weak periodic signals, and defined the cycle histogram of neuron spikes as well as the corresponding mutual correlation and information. Through computer simulations, we confirmed that both chaotic states in CR can sensitively respond to weak signals. Moreover, we found that the intermittent chaotic state exhibited a prompter response than the chaotic state with primarily turbulent movement.

No MeSH data available.


Related in: MedlinePlus

Dependence of Lyapunov exponents λj (j = 1, 2) on the input DC current I (a = 0.2, b = 2, c = −56, d = −16).
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pone.0138919.g003: Dependence of Lyapunov exponents λj (j = 1, 2) on the input DC current I (a = 0.2, b = 2, c = −56, d = −16).

Mentions: We investigated the behavior of the system in detail by enlarging the parameter region on I and d, including the values I = −99 and d = −16 used in Fig 2. Fig 3 shows the dependence of λj on I, obtained under the condition that the values of the other parameters were fixed to those shown in Fig 2. Chaotic behavior was observed (λ1 > 0) within a certain range on either side of I = −99 (−104.5 ≲ I ≲ −94.5). Furthermore, the system came to rest (non-firing) (λ1 < 0, λ2 < 0) for I≲ −104.5, whereas periodic firing (λ1 ≈ 0, λ2 < 0) was observed at I ≳ −94.5.


Analysis of Chaotic Resonance in Izhikevich Neuron Model.

Nobukawa S, Nishimura H, Yamanishi T, Liu JQ - PLoS ONE (2015)

Dependence of Lyapunov exponents λj (j = 1, 2) on the input DC current I (a = 0.2, b = 2, c = −56, d = −16).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4589341&req=5

pone.0138919.g003: Dependence of Lyapunov exponents λj (j = 1, 2) on the input DC current I (a = 0.2, b = 2, c = −56, d = −16).
Mentions: We investigated the behavior of the system in detail by enlarging the parameter region on I and d, including the values I = −99 and d = −16 used in Fig 2. Fig 3 shows the dependence of λj on I, obtained under the condition that the values of the other parameters were fixed to those shown in Fig 2. Chaotic behavior was observed (λ1 > 0) within a certain range on either side of I = −99 (−104.5 ≲ I ≲ −94.5). Furthermore, the system came to rest (non-firing) (λ1 < 0, λ2 < 0) for I≲ −104.5, whereas periodic firing (λ1 ≈ 0, λ2 < 0) was observed at I ≳ −94.5.

Bottom Line: We found the existence of two distinctive states, a chaotic state involving primarily turbulent movement and an intermittent chaotic state.Through computer simulations, we confirmed that both chaotic states in CR can sensitively respond to weak signals.Moreover, we found that the intermittent chaotic state exhibited a prompter response than the chaotic state with primarily turbulent movement.

View Article: PubMed Central - PubMed

Affiliation: Department of Management Information Science, Fukui University of Technology, Fukui, Japan.

ABSTRACT
In stochastic resonance (SR), the presence of noise helps a nonlinear system amplify a weak (sub-threshold) signal. Chaotic resonance (CR) is a phenomenon similar to SR but without stochastic noise, which has been observed in neural systems. However, no study to date has investigated and compared the characteristics and performance of the signal responses of a spiking neural system in some chaotic states in CR. In this paper, we focus on the Izhikevich neuron model, which can reproduce major spike patterns that have been experimentally observed. We examine and classify the chaotic characteristics of this model by using Lyapunov exponents with a saltation matrix and Poincaré section methods in order to address the measurement challenge posed by the state-dependent jump in the resetting process. We found the existence of two distinctive states, a chaotic state involving primarily turbulent movement and an intermittent chaotic state. In order to assess the signal responses of CR in these classified states, we introduced an extended Izhikevich neuron model by considering weak periodic signals, and defined the cycle histogram of neuron spikes as well as the corresponding mutual correlation and information. Through computer simulations, we confirmed that both chaotic states in CR can sensitively respond to weak signals. Moreover, we found that the intermittent chaotic state exhibited a prompter response than the chaotic state with primarily turbulent movement.

No MeSH data available.


Related in: MedlinePlus