Energy-efficient event pattern recognition in wireless sensor networks using multilayer spiking neural networks

Motivated by the energy-efficient computation of the brain, an energy-efficient wireless sensor network infrastructure is built for solving the pattern recognition task. In this work, spatiotemporal patterns originate from the wireless sensor nodes that use pulse-based networking schemes like pulse position-coded packet data unit and spiked time-division multiple access. Pulse-based networking schemes use spatiotemporal patterns to encode information rather than using traditional energy-expensive packets. These spatiotemporal patterns are learned through biologically plausible learning rule of Tempotron which is a neuronal binary classifier that uses a gradient-descent supervised learning rule to adjust the weights of synaptic inputs. A single Tempotron-trained spiking neuron is shown to read out information encoded in the spike timings of synaptic inputs. However, due to the simplicity of the single Tempotron classifier, its performance is inherently limited. In this work, we devise the multilayer spiking neuron training rules for event pattern classification in distributed wireless sensor networks. We also show that the proposed architecture improves classification accuracy by a considerable amount as compared to a single Tempotron model’s performance.