Training integrate-and-fire neurons with the Informax principle II

We develop neuron learning rules using the Informax principle together with the input-output relationship of the integrate-and-fire (IF) model with Poisson inputs. The learning rule is then tested with constant inputs, time-varying inputs and images. For constant inputs, it is found that, under the Informax principle, a network of IF models with initially all positive weights tends to disconnect some connections between neurons. For time-varying inputs and images, we perform signal separation tasks called independent component analysis. Numerical simulations indicate that some number of inhibitory inputs improves the performance of the system in both biological and engineering senses.