Nonlinear quantization on Hebbian-type associative memories

Hebbian-type associative memory is characterized by its simple architecture. However, the hardware implementation of Hebbian-type associative memories is normally complicated when there are a huge number of patterns stored. To simplify the interconnection values of a network, a nonlinear quantization strategy is presented. The strategy takes into account the property that the interconnection values are Gaussian distributed, and divides the interconnection weight values into a small number of unequal ranges accordingly. Interconnection weight values in each range contain information equally and each range is quantized to a value.