A New Supervised Learning Algorithm Based On Genetic Inheritance For Spiking Neural Networks

Neurons in an Artificial neural networks(ANNs)are characterized by a single,static,continuous valued activation.Yet biological neurons use discrete spikes to compute and transmit information,and the spike times,in addition to the spike rates,matter.Spiking neural networks(SNNs)are thus more biologically realistic than ANNs,and are arguably the only viable option if one wants to understand how the brain computes at the neuronal description level.The spikes of biological neurons are sparse in time and space,and event-driven.Combined with bio-plausible local learning rules,this makes it easier to build low-power,neuromorphic hardware for SNNs.Spiking neurons' transfer function is usually non-differentiable,which prevents using back-propagation.In recent years,spiking neural network learning algorithms are also being continuously explored.Unsupervised learning in SNNs often involves spike-timing-dependent-plasticity(STDP)as part of the learning mechanism.The most common form of biological STDP has a very intuitive interpretation.If a presynaptic neuron fires briefly before the postsynaptic neuron,the weight connecting them is strengthened.If the presynaptic neuron fires briefly after the postsynaptic neuron,then the causal relationship between the temporal events is spurious and the weight is weakened.Most existing training supervised algorithms are based on gradient descent with inherent defects,such as local optimum and over-fitting.Therefore,finding a learning algorithm with a global optimal solution has become a hot topic in current research.In this paper,we investigate the performance of the Genetic Algorithm Involving Mechanism of Simulated Annealing,as a supervised training algorithm for SNNs.The key idea is to adopt global search,which effectively avoid local optima and over-fitting.At the same time,we conducted a detailed analysis of the network training process and network parameters,and found the optimal combination of parameters.According to the experiment results,this approach has higher accuracy than other learning algorithms on well-known classification problems.