N × N to N pattern associative memory by using optoelectronic neurochips

A scheme of N × N to N pattern associations based on a fully connected neural network is proposed. The connection weight matrix of the neural network with N neurons is defined by the input pattern of size N × N through a linear transformation. The point attractor of the system state for this input pattern is then used as the associative output to identify the input pattern. This scheme can be implemented by the optoelectronic neurochips in a simple fashion. The learning algorithm, the simulation results, and the implementation are presented.