Skip to main content
. Author manuscript; available in PMC: 2009 Jul 25.
Published in final edited form as: Neural Comput. 2008 Jul;20(7):1776–1795. doi: 10.1162/neco.2008.06-07-540

Figure 1.

Figure 1

Results for the case that the mean input is varying in time. (a) The KL divergence as a function of the amplitude of the mean input, a. The solid line, dotted line, and dashed lines represent the KL divergence of the m-IMI model, the TRRP model, and the inhomogeneous Poisson process, respectively. The mean and the standard error at each point were calculated with 10 repetitions. The KL divergence of the inhomogeneous Poisson process is much larger than that of the m-IMI model and the TRRP model. (b) The instantaneous firing rates of the LIF neuron for various values of a. Thin solid lines represent the instantaneous firing rates of the LIF model, and the thick gray lines are raw traces. The amplitude of the instantaneous firing rate is increasing as a is increasing. (c) The solid lines represent rescaled interval distributions of the m-IMI model for various values of a, which are obtained from the recovery function by equation 3.2. The gray dashed line is the interval distribution of the LIF model for a = 0. (d) Same as c for the TRRP model. The rescaled interval distributions in both c and d are departing from the interval distribution of the LIF model as a is increasing, but the LIF model shows less variation for the m-IMI model than for the TRRP model.