Low-pass filtering of information in the leaky integrate-and-fire neuron driven by white noise

Abstract

The question of how noisy spiking neurons respond to external time-dependent stimuli is a central topic in computational neuroscience. An important aspect of the neural information transmission is, whether neurons encode preferentially information about slow or about fast components of the stimulus (signal). A convenient way to quantify this is the spectral coherence function, that in some experimental data shows a global maximum at low frequencies (low-pass information filter), in some other cases has a maximum at higher frequencies (band-pass or high-pass information filter); information-filtering defined in this way is related but not identical to the usual filtering of spectral power. Here I demonstrate numerically that the leaky integrate-and-fire neuron driven by white noise (a stimulus without temporal correlations) acts as a low-pass information filter irrespective of the dynamical regime (fluctuation-driven and irregular or mean-driven and regular firing).