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    Optimal Inputs for Phase Models of Spiking Neurons

    Source: Journal of Computational and Nonlinear Dynamics:;2006:;volume( 001 ):;issue: 004::page 358
    Author:
    Jeff Moehlis
    ,
    Eric Shea-Brown
    ,
    Herschel Rabitz
    DOI: 10.1115/1.2338654
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Variational methods are used to determine the optimal currents that elicit spikes in various phase reductions of neural oscillator models. We show that, for a given reduced neuron model and target spike time, there is a unique current that minimizes a square-integral measure of its amplitude. For intrinsically oscillatory models, we further demonstrate that the form and scaling of this current is determined by the model’s phase response curve. These results reflect the role of intrinsic neural dynamics in determining the time course of synaptic inputs to which a neuron is optimally tuned to respond, and are illustrated using phase reductions of neural models valid near typical bifurcations to periodic firing, as well as the Hodgkin-Huxley equations.
    keyword(s): Fire , Bifurcation , Current , Equations , Firing (materials) , Dynamics (Mechanics) , Phase space AND Trajectories (Physics) ,
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      Optimal Inputs for Phase Models of Spiking Neurons

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    https://yetl.yabesh.ir/yetl1/handle/yetl/133262
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    contributor authorJeff Moehlis
    contributor authorEric Shea-Brown
    contributor authorHerschel Rabitz
    date accessioned2017-05-09T00:19:05Z
    date available2017-05-09T00:19:05Z
    date copyrightOctober, 2006
    date issued2006
    identifier issn1555-1415
    identifier otherJCNDDM-25552#358_1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl/handle/yetl/133262
    description abstractVariational methods are used to determine the optimal currents that elicit spikes in various phase reductions of neural oscillator models. We show that, for a given reduced neuron model and target spike time, there is a unique current that minimizes a square-integral measure of its amplitude. For intrinsically oscillatory models, we further demonstrate that the form and scaling of this current is determined by the model’s phase response curve. These results reflect the role of intrinsic neural dynamics in determining the time course of synaptic inputs to which a neuron is optimally tuned to respond, and are illustrated using phase reductions of neural models valid near typical bifurcations to periodic firing, as well as the Hodgkin-Huxley equations.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleOptimal Inputs for Phase Models of Spiking Neurons
    typeJournal Paper
    journal volume1
    journal issue4
    journal titleJournal of Computational and Nonlinear Dynamics
    identifier doi10.1115/1.2338654
    journal fristpage358
    journal lastpage367
    identifier eissn1555-1423
    keywordsFire
    keywordsBifurcation
    keywordsCurrent
    keywordsEquations
    keywordsFiring (materials)
    keywordsDynamics (Mechanics)
    keywordsPhase space AND Trajectories (Physics)
    treeJournal of Computational and Nonlinear Dynamics:;2006:;volume( 001 ):;issue: 004
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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