Neural Comput - Neural dynamics, bifurcations, and firing rates in a quadratic integrate-and-fire model with a recovery variable. I: Deterministic behavior.

Tópicos

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Resumo

We study the dynamics of a quadratic integrate-and-fire model of a single-compartment neuron with a slow recovery variable, as input current and parameters describing timescales, recovery variable, and postspike reset change. Analysis of a codimension 2 bifurcation reveals that the domain of attraction of a stable hyperpolarized rest state interacts subtly with reset parameters, which reposition the system state after spiking. We obtain explicit approximations of instantaneous firing rates for fixed values of the recovery variable, and use the averaging theorem to obtain asymptotic firing rates as a function of current and reset parameters. Along with the different phase-plane geometries, these computations provide explicit tools for the interpretation of different spiking patterns and guide parameter selection in modeling different cortical cell types.

Resumo Limpo

studi dynam quadrat integrateandfir model singlecompart neuron slow recoveri variabl input current paramet describ timescal recoveri variabl postspik reset chang analysi codimens bifurc reveal domain attract stabl hyperpolar rest state interact subt reset paramet reposit system state spike obtain explicit approxim instantan fire rate fix valu recoveri variabl use averag theorem obtain asymptot fire rate function current reset paramet along differ phaseplan geometri comput provid explicit tool interpret differ spike pattern guid paramet select model differ cortic cell type

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