A hardware neuron model as a component of the neural network

In the nervous system there are two kinds of information processing: intra-cellular analog operation and digital transmission between cells. These properties are considered to be indispensable in the construction of a large-scale parallel information processing system having reasonable redundancy and which incorporates a number of spreading spatial and temporal patterns. As a simplified model having these two processing capabilities we propose a hardware neuron model using a voltage-controlled oscillator IC. The model characteristics are easily controlled and it can supply a number of models as components of the hardware neural network. The fundamental characteristics for neural operation between multichannel asynchronous inputs are investigated with the model. It is shown that the mean impulse frequencies of the asynchronous inputs are linearly added or subtracted at the model despite the non-linear property characterized by threshold in the output pulse generation. The characteristics of a backward lateral inhibition connection are also investigated for input pulse sequences. It has an input excitation frequency threshold and shows variable gain or hysteresis characteristics, depending on whether the weighting coefficients of the inhibition inputs are large or small. The model can be connected directly to a microprocessor and can process spatial analog information without an A-D converter. These characteristics suggest that the model can be used as functional elements in the pre-processing network of an information processing system for spatial patterns.