Temporal coding in recurrent spiking neural networks with synaptic delay-weight plasticity

As a brain-inspired artificial neural network computational model, a recurrent spiking neural network is composed of biologically plausible spiking neurons, which has taken on increasing importance in this study mainly include complex network structure and implicit nonlinear mechanism. This paper presents a learning algorithm with synaptic delay-weight plasticity for recurrent spiking neural network, which may enable real-time communication of complex spatiotemporal spike trains simulating organisms. First, by copying the hidden layer between the input and output layers, a context layer can be created. A total error for spatiotemporal pattern is then introduced, along with the learning rule for synaptic weights with synaptic delay plasticity. Moreover, by using synaptic delays of spike train from postsynaptic neuron, the proposed algorithm defines the learning rules for synaptic weights. In addition, the performance of the learning algorithm has been successfully tested to achieve high-precision learning with limited iterative resources. Finally, the main factors have been evaluated qualitatively and quantitatively, such as different lengths and frequencies for desired output spike trains.