Synaptic learning in VLSI-based artificial nerve cells

A VLSI method for analog synaptic learning in an electronic neuronal model is presented. This method reduces the size and complexity involved in implementing adaptive neuronally based controllers for robotic motion. It also provides for a continuous range of synaptic weights at both excitatory and inhibitory inputs while anticipating the need to interface to a pulse-driven system. The system is described, and test results indicate that it is able to alter the synaptic coupling on an inhibitory or an excitory input over a wide range.<>