A compact correlation filter for on-chip learning in a spiking neural network

A Hebbian learning algorithm based on proportion sampling is presented that can be used to implement on-chip learning for a binary spiking neural network. A correlation filter estimates when statistical independence has been obtained between subsequent samples. Simulation shows that the correlation filter reduces falsely learned connections in environments were inputs are randomly activated an average of 83% of the total time. A correlation filter for 255 binary samples is implemented using 21 gates and a surface area of .0008cm(2) for a .5 mu fabrication process. Compared to traditional neural networks, the spiking neural network learned an odor in a single epoch resulting in only a 7% error, while classical learning algorithms required multiple epochs and typically resulted in 30% error.