Supervised learning in multilayer spiking neural network - Shrestha, Sumit Bam (2017)
Spiking Neural Networks (SNNs) are an exciting prospect in the field of Artificial Neural Networks (ANNs). We try to replicate the massive interconnection of neurons, the computational units, evident in brain to perform useful task in ANNs, albeit with highly abstracted model of neurons. Mostly the artificial neurons are realized in the form of non-linear activation function which process numeric inputs and output. SNNs are less abstract than these systems with non-linear activation function in the sense that they make use of mathematical model of neurons, termed spiking neurons, which process inputs in the form of spikes and emits spike as output. This is exactly the way in which natural neurons exchange information. Since spikes are events in time, there is an extra dimension of time along with amplitude in SNNs which makes them suited to temporal processes.