Spiking neural networks are shown to be suitable tools for the processing of spatio-temporal information. However, due to their intricately discontinuous and implicit nonlinear mechanisms, the formulation of efficient supervised learning algorithms f
Spiking neural P systems (SN P systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes, where neurons work in parallel in the sense that each neuron that can fire should f
The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific train encoded by precise firing times of spikes. The gradient-descent-based(GDB) learning methods are widely used and verified in the
Real time human action recognition is to recognize the human motion type based on skeleton movement in real time and is always a challenging task. In this paper, a novel method is proposed to accomplish the classification by using Spiking neural netw
