Poisson balanced spiking networks
Fig 4
(A) The conditional intensity for the exponential non-linearity (dashed lines) and the sigmoid non-linearity (solid lines). The conditional intensity of the sigmoidal non-linearity closely follows that of the exponential non-linearity for sub-threshold voltages, but levels off after threshold, keeping firing rates stable. (B) Family of nonlinearities with varying Fmax. Increasing Fmax raises the firing rate at which the nonlinearity saturates. (C) Family of nonlinearities with varying α. Increasing α increases the steepness of the nonlinearity, which approaches a hard-threshold function as α → ∞ (like the BSN). (D) Simulation of the original BSN implementing an exact integrator, showing membrane potential and spikes of a single example neuron. (E) Spikes and membrane potential of the same neuron in a local Poisson BSN implementation of the same system. High α simulations (yellow) replicate the behavior of the BSN integrator. Lowering α to 50 (blue) or 10 (red) results in a spread of spikes centered around the deterministic BSN spikes.