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Aperiodic stochastic resonance in neural information processing with Gaussian colored noise
Yanmei Kang1, Ruonan Liu1, Xuerong Mao2
1School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, 710049 China.
Cognitive Neurodynamics
|May 27, 2021
Summary
This study reveals aperiodic stochastic resonance in neural systems using colored noise. Adjusting noise correlation offers a biologically plausible mechanism for neural signal processing.
Area of Science:
- Computational Neuroscience
- Nonlinear Dynamics
- Stochastic Processes
Background:
- Neural systems process information using complex dynamics, often influenced by noisy environments.
- Stochastic resonance (SR) is a phenomenon where noise enhances signal transmission in nonlinear systems.
- Aperiodic SR in neural systems with colored noise remains less understood.
Purpose of the Study:
- To explore aperiodic stochastic resonance (ASR) in neural systems subjected to colored noise.
- To theoretically predict and experimentally verify ASR in bistable and excitable neural models.
- To investigate the role of noise correlation time in inducing ASR.
Main Methods:
- Analysis of nonlinear dynamical systems driven by Gaussian colored noise.
- Application of global and local Lipschitz conditions to prove trajectory convergence.
- Utilizing the forbidden interval theorem for ASR prediction.
- Numerical simulations using two specific neuron models.
Main Results:
- Demonstrated convergence of stochastic trajectories to deterministic ones as noise intensity decreases.
- Predicted and verified ASR in bistable and excitable neural models.
- Disclosed ASR induced by noise correlation time.
Conclusions:
- ASR is a viable phenomenon in neural systems with colored noise.
- Noise correlation time is a significant factor in inducing ASR.
- Modulating noise correlation presents a biologically plausible strategy for neural signal processing.
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