Poisson-Like Spiking in Circuits with Probabilistic Synapses
Figure 2
Approximate Fano factor constancy with probabilistic synapses.
(a) Scheme of a balanced recurrent network with excitatory and inhibitory neurons driven by non-Poisson-like inputs. Bottom: the network is embedded with probabilistic synaptic transmission. The scheme shows how a presynaptic spike train generates stochastic currents on several postsynaptic neurons. (b) Mean firing rate for excitatory (red) and inhibitory (green) populations for a network with probabilistic synapses and noiseless inputs (solid lines) and for a network without probabilistic noise and constant input noise (dashed lines) as a function of the mean input drive. (c–d) Spike count variance and Fano factor as a function of firing rate. Open circles correspond to mean values, and black dots correspond to individual neurons. Line and color codes are as in panel b. (e) Raster plots of 20 randomly selected excitatory and inhibitory neurons for the high firing rate network corresponding to the point marked in blue in panels b–d. Center: sample traces of excitatory and inhibitory current leading to the net input current (black), magnified on the right. Yellow line corresponds to zero net current, and blue trace shows the membrane potential of a randomly selected excitatory neuron. (f) Coefficient of variation of the ISIs, ,as a function of the mean ISI. (g) Distribution of ISIs for the selected neuron. (h) Auto-correlogram (ACG) of the spike train for that neuron.