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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-case16057895072464662021-08-03T07:16:31Z Noise Decomposition for Stochastic Hodgkin-Huxley Models Pu, Shusen Mathematics Neurosciences Cognitive Psychology Hodgkin-Huxley Models Langevin Models Efficient Stochastic Simulations Stochastic Shielding Variance of Interspike Interval Phase Response Curve Model Comparison Noise Decomposition Theorem In this thesis, we present a natural 14-dimensional Langevin model for the Hodgkin-Huxley (HH) conductance-based neuron model in which each directed edge in the ion channel state transition graph acts as an independent noise source, leading to a 14X28 noise coefficient matrix. We show that (i) the corresponding 14D mean-field ordinary differential equation system is consistent with the classical 4D representation of the HH system; (ii) the 14D representation leads to a noise coefficient matrix that can be obtained cheaply on each timestep, without requiring a matrix decomposition; (iii) sample trajectories of the 14D representation are pathwise equivalent to trajectories of several existing Langevin models, including one proposed by Fox and Lu in 1994; (iv) our 14D representation give the most accurate interspike-interval distribution, not only with respect to moments but under both the L1 and L∞ metric-space norms; and (v) the 14D representation gives an approximation to exact Markov chain simulations that are as fast and as efficient as all equivalent models. We combine the stochastic shielding (SS) approximation, introduced by Schmandt and Galán in 2012, with Langevin versions of the HH model to derive an analytic decomposition of the variance of the interspike intervals (ISI), based on the mean--return-time oscillator phase. We prove in theory, and demonstrate numerically, that in the limit of small noise, the variance of the ISI decomposes linearly into a sum of contributions from each directed edge. Unlike prior analyses, our results apply to current clamp rather than voltage clamp conditions. Under current clamp, a stochastic conductance-based model is an example of a piecewise-deterministic Markov process. Our theory is exact in the limit of small channel noise. Through numerical simulations we demonstrate its applicability over a range from small to moderate noise levels. We show numerically that the SS approximation has a high degree of accuracy even for larger, physiologically relevant noise levels. 2021-01-26 English text Case Western Reserve University School of Graduate Studies / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=case1605789507246466 http://rave.ohiolink.edu/etdc/view?acc_num=case1605789507246466 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection NDLTD
language English
sources NDLTD
topic Mathematics
Neurosciences
Cognitive Psychology
Hodgkin-Huxley Models
Langevin Models
Efficient Stochastic Simulations
Stochastic Shielding
Variance of Interspike Interval
Phase Response Curve
Model Comparison
Noise Decomposition Theorem
spellingShingle Mathematics
Neurosciences
Cognitive Psychology
Hodgkin-Huxley Models
Langevin Models
Efficient Stochastic Simulations
Stochastic Shielding
Variance of Interspike Interval
Phase Response Curve
Model Comparison
Noise Decomposition Theorem
Pu, Shusen
Noise Decomposition for Stochastic Hodgkin-Huxley Models
author Pu, Shusen
author_facet Pu, Shusen
author_sort Pu, Shusen
title Noise Decomposition for Stochastic Hodgkin-Huxley Models
title_short Noise Decomposition for Stochastic Hodgkin-Huxley Models
title_full Noise Decomposition for Stochastic Hodgkin-Huxley Models
title_fullStr Noise Decomposition for Stochastic Hodgkin-Huxley Models
title_full_unstemmed Noise Decomposition for Stochastic Hodgkin-Huxley Models
title_sort noise decomposition for stochastic hodgkin-huxley models
publisher Case Western Reserve University School of Graduate Studies / OhioLINK
publishDate 2021
url http://rave.ohiolink.edu/etdc/view?acc_num=case1605789507246466
work_keys_str_mv AT pushusen noisedecompositionforstochastichodgkinhuxleymodels
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