Frequency- and temporal-domain neural competition in analog integrate-and-fire neurochips

We present an inhibitory neural network implemented on analog CMOS chips, whose neurons compete with each other in the frequency and time domains. The circuit for each neuron was designed to produce sequences in time of identically shaped pulses, called spikes. The results of experiments and simulations revealed that the network more efficiently achieved the selective activation and inactivation of the neural circuits on the basis of spike timing than on the basis of firing rates. The results indicate that neural processing based on the spike timing of neural circuits provides a possible way to overcome the low-tolerance problems of analog devices in noisy environments.