Dynamics and stochastic resonance in a thermosensitive neuron

Xu, Ying; Guo, Yeye; Ren, Guo-Dong; Ma, Jun

doi:10.1016/j.amc.2020.125427

Cited by 76 publications

(29 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, one should choose positively strongest correlated highest possible strength of noises in predation and harvesting together as control with Michaelis-Menten type harvesting to reduce average population density rapidly and effectively (Fig. 3,7,18).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

The Role of Harvesting in Population Control in Presence of Multiple Stochastic Noise Sources

Mandal¹,

Jana²,

Poria

2021

Preprint

View full text Add to dashboard Cite

In this paper, we compare the role of constant and Michaelis-Menten type harvesting in single species population control in presence of stochastic noises sources. Steady state probability distributions and stationary potentials of the population for the two types of harvesting are determined by Fokker-Planck equations. Stochastic bifurcation analysis and mean first passage times have been computed. Noise induced critical transitions are observed depending on the strength of the noises. The extinction possibility of population in stochastic control with Michaelis-Menten type harvesting is higher than the constant rate of harvesting. One of the findings is the transition of the population from bistable to tristable for weak noise and Michaelis-Menten type harvesting. Another finding is noise enhanced stability phenomenon for negatively correlated noises. In case of population control, constant rate of harvesting is better in deterministic case whereas Michaelis-Menten type harvesting is better in stochastic case. The stochastic control is more efficient than deterministic control as average population size in stochastic case is lower than the deterministic case. The results obtained in this study can throw light on toxic phytoplankton blooms and its control in marine ecosystem. Moreover, the study can be useful to explain wild prey population outbreak and its control in deep forest.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Results of stability analysis of various deterministic population models have been reported by many authors [1,2]. The study of models with noise are becoming increasingly important in various branches of science [3][4][5][6][7][8]. In real world ecosystem various environmental noises have significant impact on the stability of the system.…”

Section: Introductionmentioning

confidence: 98%

The Role of Harvesting in Population Control in Presence of Multiple Stochastic Noise Sources

Mandal¹,

Jana²,

Poria

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…There are many available biophysical models of neuronal membrane potential dynamics (Morris and Lecar, 1981 ; Hodgkin and Huxley, 1990 ; Xu et al, 2020a , b ; Zhang et al, 2020 ). For our purpose we chose the Izhikevich model as it is quite functional and computationally effective for network simulations (Izhikevich, 2003 ):…”

Section: Methodsmentioning

confidence: 99%

Modeling Working Memory in a Spiking Neuron Network Accompanied by Astrocytes

Gordleeva

Tsybina

Krivonosov

et al. 2021

Front. Cell. Neurosci.

View full text Add to dashboard Cite

We propose a novel biologically plausible computational model of working memory (WM) implemented by a spiking neuron network (SNN) interacting with a network of astrocytes. The SNN is modeled by synaptically coupled Izhikevich neurons with a non-specific architecture connection topology. Astrocytes generating calcium signals are connected by local gap junction diffusive couplings and interact with neurons via chemicals diffused in the extracellular space. Calcium elevations occur in response to the increased concentration of the neurotransmitter released by spiking neurons when a group of them fire coherently. In turn, gliotransmitters are released by activated astrocytes modulating the strength of the synaptic connections in the corresponding neuronal group. Input information is encoded as two-dimensional patterns of short applied current pulses stimulating neurons. The output is taken from frequencies of transient discharges of corresponding neurons. We show how a set of information patterns with quite significant overlapping areas can be uploaded into the neuron-astrocyte network and stored for several seconds. Information retrieval is organized by the application of a cue pattern representing one from the memory set distorted by noise. We found that successful retrieval with the level of the correlation between the recalled pattern and ideal pattern exceeding 90% is possible for the multi-item WM task. Having analyzed the dynamical mechanism of WM formation, we discovered that astrocytes operating at a time scale of a dozen of seconds can successfully store traces of neuronal activations corresponding to information patterns. In the retrieval stage, the astrocytic network selectively modulates synaptic connections in the SNN leading to successful recall. Information and dynamical characteristics of the proposed WM model agrees with classical concepts and other WM models.

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

Dynamics and stochastic resonance in a thermosensitive neuron

Cited by 76 publications

References 64 publications

The Role of Harvesting in Population Control in Presence of Multiple Stochastic Noise Sources

The Role of Harvesting in Population Control in Presence of Multiple Stochastic Noise Sources

Modeling Working Memory in a Spiking Neuron Network Accompanied by Astrocytes

Aperiodic stochastic resonance in neural information processing with Gaussian colored noise

Contact Info

Product

Resources

About