Encoding real values into polychronous spiking networks

Spiking neural networks show promising capability in handling the same kind of scaling up of problems as living brains, due to their more faithful similarity to biological neural networks. The big challenge of dealing with spiking neural networks is getting data into and out of them, which requires proper encoding and decoding methods. Presented in this paper is an adaptation of Izhikevich's model of a polychronous spiking network and an encoding scheme for real valued data. Data is chosen arbitrarily to cover the range of the encoding scheme in order to best demonstrate the network's response to different inputs. Preliminary results show that the network is able to recognize distinct input values and respond to them with unique spiking patterns.