Effects of network topologies on stochastic resonance in feedforward neural network

Zhao, Jia; Qin, Yingmei; Che, Yanqiu; Ran, Huangyanqiu; Li, Jingwen

doi:10.1007/s11571-020-09576-8

Cited by 15 publications

(2 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the presence of noise interference [26,27], most neurons can be tamed in the form of external periodic currents to form regular discharge patterns and high neural activity regularity [28]. In the absence of periodic stimulation, a certain intensity of noise can also effectively induce nonlinear resonance [29][30][31]. In addition, similar coherent resonances and regular spatial patterns can be formed when noise is applied to a network of neurons [32,33].…”

Section: Introductionmentioning

confidence: 99%

Response mechanism of heat-sensitive neurons under combined noise stimulation

Wang,

Huang,

Zhu

et al. 2024

Preprint

View full text Add to dashboard Cite

Patients born with congenital analgesia have no ability to sense temperature, who generally have bleak chances of survival. However, the relevant pathological mechanism remains unclear. To explore how the body's heat-sensitive neurons change in response to external stimuli, based on a heat-sensitive neuron model developed from the piezoelectric neural circuit, we designed two different stimuli combined Gaussian white noise to each stimulus, triggering different firing modes (r1, r2, r3). The Hamilton energy corresponding to each emission mode is calculated to identify the contribution of the external stimulus, and further, a correlation function is defined to identify the influence of external stimuli on the firing mode selection of neurons. Simulation results revealed that the neuronal response to the external stimulus that induces the spike discharge is preferentially compared to the external stimulus that induces the r-clonic pattern. The changes in the contour of the periodic attractor confirmed that external stimuli inducing spike-and-wave and r-clonic patterns could be sensitively perceived. For external stimuli, the chaotic emission pattern caused by the Chua circuit stimulus, the contribution of spikes, r-clonic and even periodic stimuli can be detected because the contour of the chaotic attractor also changes greatly. Moreover, the neurons are more sensitive to external stimuli, which can cause a greater release of Hamilton energy and a higher level of regularity in neural activity. This study revealed the potential response mechanism of thermosensitive neurons under combined noise stimulation.

show abstract

Section: Introductionmentioning

confidence: 99%

Response mechanism of heat-sensitive neurons under combined noise stimulation

Wang,

Huang,

Zhu

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Stochastic resonance, initially proposed in exploring the periodicity of the continental ice volume in the quaternary era (Benzi et al 1981), is such an anti-intuitive phenomenon (Gammaitoni et al 1998;Nakamura and Tateno 2019;Xu et al 2020;Zhao et al 2020), where weak coherent signal can be amplified by noise through certain nonlinearity. In general, a suitable external weak signal is prerequisite for stochastic resonance.…”

Section: Introductionmentioning

confidence: 99%

Aperiodic stochastic resonance in neural information processing with Gaussian colored noise

2020

View full text Add to dashboard Cite

The aim of this paper is to explore the phenomenon of aperiodic stochastic resonance in neural systems with colored noise. For nonlinear dynamical systems driven by Gaussian colored noise, we prove that the stochastic sample trajectory can converge to the corresponding deterministic trajectory as noise intensity tends to zero in mean square, under global and local Lipschitz conditions, respectively. Then, following forbidden interval theorem we predict the phenomenon of aperiodic stochastic resonance in bistable and excitable neural systems. Two neuron models are further used to verify the theoretical prediction.Moreover, we disclose the phenomenon of aperiodic stochastic resonance induced by correlation time and this finding suggests that adjusting noise correlation might be a biologically more plausible mechanism in neural signal processing.

show abstract

Information propagation in recurrent neuronal populations with mixed excitatory–inhibitory synaptic connections

Sun

2021

Nonlinear Dyn

View full text Add to dashboard Cite

Effects of network topologies on stochastic resonance in feedforward neural network

Cited by 15 publications

References 52 publications

Response mechanism of heat-sensitive neurons under combined noise stimulation

Response mechanism of heat-sensitive neurons under combined noise stimulation

Aperiodic stochastic resonance in neural information processing with Gaussian colored noise

Information propagation in recurrent neuronal populations with mixed excitatory–inhibitory synaptic connections

Contact Info

Product

Resources

About