Poisson balanced spiking networks
Fig 2
Balanced spiking network implementing an exact integrator.
The network consists of 400 neurons, divided into two populations with output weights of + 0.1 (red) and −0.1 (blue). (A) Simulation results under the condition that only one neuron is allowed to fire per discrete time bin. (B) Simulation results when all neurons whose membrane potential is above threshold in a single time bin are allowed to fire, leading to “ping-pong” behavior. Insets show that the read-out (yellow) is alternating between large over- and under-estimates of the target (in black). Insets show, in order from top to bottom: the voltage traces of neurons in both positively and negatively weighted populations for a small time window, the resulting spikes in each time bin, and the resulting read-out (yellow) and target (black). Since the weights and inputs are identical across populations, so are the voltage traces. Ping-ponging results, as all neurons within a population cross the threshold in the same time bin, spike, and cause the read-out to oscillate between over- and under-estimates of the target.