BPSpike II: A new backpropagation learning algorithm for spiking neural networks

Abstract

Using gradient descent, we propose a new backpropagation learning algorithm for spiking neural networks with multi-layers, multisynapses between neurons, and multi-spiking neurons. It adjusts synaptic weights, delays, and time constants, and neurons’ thresholds in output and hidden layers. It guarantees convergence to minimum error point, and unlike SpikeProp and its extensions, does not need a one-to-one correspondence between actual and desired spikes in advance. So, it is stably and widely applicable to practical problems.