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Pattern recognition of Hodgkin-Huxley equations by auto-regressive Laguerre Volterra network

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A Structure of ASLVN for modeling H-H equations, where the input x(n) is the randomly injected current and the output y*(n) is the membrane potential. B The predictions results, z(1) represents the exogenous output, z(2) represents the autoregressive output and z(x) represents the cross term output.
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Figure 1: A Structure of ASLVN for modeling H-H equations, where the input x(n) is the randomly injected current and the output y*(n) is the membrane potential. B The predictions results, z(1) represents the exogenous output, z(2) represents the autoregressive output and z(x) represents the cross term output.

Mentions: A nonparametric, data-driven nonlinear auto-regressive Volterra (NARV) [1] model has been successfully applied for capturing the dynamics in the generation of action potentials, which is classically modeled by Hodgkin-Huxley (H-H) equations. However, the compactness still need to be improved for further interpretations. Therefore, we propose a novel Auto-regressive Sparse Laguerre Volterra Network (ASLVN) model (shown in Figure 1A), which is developed from traditional Laguerre Volterra Network (LVN) and principal dynamic mode (PDM) framework [2].


Pattern recognition of Hodgkin-Huxley equations by auto-regressive Laguerre Volterra network
A Structure of ASLVN for modeling H-H equations, where the input x(n) is the randomly injected current and the output y*(n) is the membrane potential. B The predictions results, z(1) represents the exogenous output, z(2) represents the autoregressive output and z(x) represents the cross term output.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4697513&req=5

Figure 1: A Structure of ASLVN for modeling H-H equations, where the input x(n) is the randomly injected current and the output y*(n) is the membrane potential. B The predictions results, z(1) represents the exogenous output, z(2) represents the autoregressive output and z(x) represents the cross term output.
Mentions: A nonparametric, data-driven nonlinear auto-regressive Volterra (NARV) [1] model has been successfully applied for capturing the dynamics in the generation of action potentials, which is classically modeled by Hodgkin-Huxley (H-H) equations. However, the compactness still need to be improved for further interpretations. Therefore, we propose a novel Auto-regressive Sparse Laguerre Volterra Network (ASLVN) model (shown in Figure 1A), which is developed from traditional Laguerre Volterra Network (LVN) and principal dynamic mode (PDM) framework [2].

View Article: PubMed Central - HTML

No MeSH data available.


Related in: MedlinePlus