Excitation-Inhibition Balanced Spiking Neural Networks for Fast Information Processing

The balance of excitation and inhibition is a fundamental property of neural systems. The present study investigates an excitation and inhibition (E-I) balanced spiking neural network model for neuromorphic computing, in particular, to track rapid changes of external inputs. We analyze the working mechanism of an E-I balanced network and find that the network generates internal noises of a nearly optimal structure which enables neural population firing rates to track input changes almost instantly. Moreover, we extend the network model from homogenous connectivity to local connectivity, so that the network can remain balanced under spatially heterogeneous inputs and retain spatial information. Simulation results confirm that the model works well. This model may serve as a fast responding module for general neuromorphic computing systems.