Enhancement into the dissipative stochastic mechanics based neuron model under input current pulses
It has been recently argued and experimentally shown that ion channel noise in neurons may have results that are profound the neuron's dynamical behavior. Most profoundly, ion channel noise was seen become able to cause spontaneous firing and resonance that is stochastic. An approach that is ph...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/19279/1/123.pdf http://umpir.ump.edu.my/id/eprint/19279/ https://wasrti.org/proceeedings/48.pdf |
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| Summary: | It has been recently argued and experimentally shown that ion channel noise in neurons may have results that are profound the neuron's dynamical behavior. Most profoundly, ion channel noise was seen become able to cause spontaneous firing and resonance that is stochastic. An approach that is physical the description of neuronal dynamics under the influence of ion channel noise has been recently proposed through the utilization of dissipative stochastic mechanics. It introduced a computational neuron model channel noise that is incorporating. The most feature that is distinctive of model could be the existence of so-called the renormalization terms therein. This model experimentally displays compatible noise- induced transitions among its dynamical states and gives the Rose-Hindmarsh model of the neuron in the limitation that is deterministic. The dissipative stochastic mechanics based neuron model will be studied when the input present to the neuron is an input pulse and noisy in this paper. Data of firing efficiency, latency, and jitter will undoubtedly be examined for various stimulus pulses. In particular, the role played by the existence of the renormalization term shall be focused on in the examination.
In addition, the investigation shows that the use of noise in the inputs can improve the spiking rates as well as the coherence that is spike, especially in the existence of the renormalization terms. |
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