Poisson balanced spiking networks
Fig 3
Schematic of two neurons in a BSN with conditionally Poisson neurons.
The stimulus influences each neuron’s membrane potential vi via a set of input weights W⊤. The neurons reset themselves via instantaneous, fast synapses. Fast connections to other neurons propagate the effects of spikes with a synaptic time delay d. The desired linear dynamics are implemented via slow weights (through spike trains filtered by an exponential) also with a time delay d. Within each neuron, spiking is probabilistic with an instantaneous probability of firing λi(t) = f(vi(t)), where f(⋅) is a nonlinear function of voltage. Self-connections are only shown for neuron i.