Abstract
Technological requirements for excellent parallel distributed processing capability of central nervous system has increased recently. This has led to the development of neurosimulators, e.g., neurocomputers and neurochips integrated from artificial neuron combinations. However, in developing the neurosimulators, there is no established theory as to how to establish each individual artificial neuron activity function. In the development of a theory, artificial neuron combinations were trialβproduced using analog MOS devices. The characteristics of the Analog MOS artificial neurons are as follows:
They consist of equivalent electronic circuits for synapses on the dendrites, cell bodies, and axons, assuming the electrical properties of each section of the bioneuron produces a unique processing pattern.
They exhibit the same electrical performance (absolute refractory period, temporal and spatial weightings of the post synaptic potential, and pulse generation at the axon base) as the bioneurons.
The synaptic equivalent circuits are connected with learning systems to enable plastic inputβoutput characteristics by changing pulse transmission efficiency according to the input pulse frequencies. In the final research stage, the possibility of fundamental operations, such as filtering and phase shifting in the pulse frequency domain using a neuroprocessor comprising several analog MOS artificial neurons with different pulse transmission efficiencies was investigated.