Design study of efficient digital order-based STDP neuron implementations for extracting temporal features

Spiking neural networks are naturally asynchronous and use pulses to carry information. In this paper, we consider implementing such networks on a digital chip. We used an event-based simulator and we started from a previously established simulation, which emulates an analog spiking neural network, that can extract complex and overlapping, temporally correlated features. We modified this simulation to allow an easier integration in an embedded digital implementation. We first show that a four bits synaptic weight resolution is enough to achieve the best performance, although the network remains functional down to a 2 bits weight resolution. Then we show that a linear leak could be implemented to simplify the neurons leakage calculation. Finally, we demonstrate that an order-based STDP with a fixed number of potentiated synapses as low as 200 is efficient for features extraction. A simulation including these modifications, which lighten and increase the efficiency of digital spiking neural network implementation shows that the learning behavior is not affected, with a recognition rate of 98% in a cars trajectories detection application.