Neural Comput - The dynamics of integrate-and-fire: mean versus variance modulations and dependence on baseline parameters.

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Resumo

The leaky integrate-and-fire (LIF) is the simplest neuron model that captures the essential properties of neuronal signaling. Yet common intuitions are inadequate to explain basic properties of LIF responses to sinusoidal modulations of the input. Here we examine responses to low and moderate frequency modulations of both the mean and variance of the input current and quantify how these responses depend on baseline parameters. Across parameters, responses to modulations in the mean current are low pass, approaching zero in the limit of high frequencies. For very low baseline firing rates, the response cutoff frequency matches that expected from membrane integration. However, the cutoff shows a rapid, supralinear increase with firing rate, with a steeper increase in the case of lower noise. For modulations of the input variance, the gain at high frequency remains finite. Here, we show that the low-frequency responses depend strongly on baseline parameters and derive an analytic condition specifying the parameters at which responses switch from being dominated by low versus high frequencies. Additionally, we show that the resonant responses for variance modulations have properties not expected for common oscillatory resonances: they peak at frequencies higher than the baseline firing rate and persist when oscillatory spiking is disrupted by high noise. Finally, the responses to mean and variance modulations are shown to have a complementary dependence on baseline parameters at higher frequencies, resulting in responses to modulations of Poisson input rates that are independent of baseline input statistics.

Resumo Limpo

leaki integrateandfir lif simplest neuron model captur essenti properti neuron signal yet common intuit inadequ explain basic properti lif respons sinusoid modul input examin respons low moder frequenc modul mean varianc input current quantifi respons depend baselin paramet across paramet respons modul mean current low pass approach zero limit high frequenc low baselin fire rate respons cutoff frequenc match expect membran integr howev cutoff show rapid supralinear increas fire rate steeper increas case lower nois modul input varianc gain high frequenc remain finit show lowfrequ respons depend strong baselin paramet deriv analyt condit specifi paramet respons switch domin low versus high frequenc addit show reson respons varianc modul properti expect common oscillatori reson peak frequenc higher baselin fire rate persist oscillatori spike disrupt high nois final respons mean varianc modul shown complementari depend baselin paramet higher frequenc result respons modul poisson input rate independ baselin input statist

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