Towards realtime stance classification by spiking neural network

Spiking neural networks are a popular area of current research in both artificial intelligence and neuroscience. Unlike second generation networks like the multilayer perceptron (MLP), they simulate rather than emulate neuronal interactions. Spiking networks have been shown to be theoretically more powerful than earlier generation networks, and have repeatedly been suggested as ideal for realtime problems due to their time-basis. Because of their sparse nature, real neural networks are also extremely power-efficient, a pressing concern in computing today. This raises the possibility of applying sparse spiking networks for power-saving. To investigate these ideas, we wish to apply a spiking network to realtime data classification. As a first step, we use a feedforward network with the SpikeProp algorithm to classify offline skeleton data derived from a depth camera. Classifier networks were successfully trained, but we found SpikeProp considerably more complex to apply than backpropagation. There is considerable potential for optimization and power efficiency, and we hope to compare the performance of our system with more established learning techniques in a realtime setting.