*8.1.4 Evolutionary spiking neural networks*

Population-based metaheuristics are evolutionary algorithms (EAs). Historically, observations of natural evolution in biological populations served as the inspiration for their design. The network architecture, model hyperparameters, and synaptic weights and delays can all be directly optimised using these approaches [274, 275]. The synaptic weights of SNNs are currently learned using evolutionary algorithms like differential evolution (DE), grammatical evolution (GE), harmony search algorithm (HSA), and particle swarm optimization (PSO). The synaptic weights of a spiking neuron, such as integrate and fire, Izhikevich, and spike response model (SRM) models, can be trained by employing algorithms like DE, GE, and HSA to carry out classification tasks, as demonstrated by Vazquez [276], López-Vázquez et al. [277], and Yusuf et al. [278]. In both linear and nonlinear classification problems, Vazquez and Garro [279] used the PSO algorithm to train the synaptic weights of a spiking neuron. They found that firing rates are matched by input patterns of the same class. For the purpose of training supervised feedforward SNNs, Pavlidis et al. [280] presented the parallel differential evolution method. Their method is tested only with exclusive OR, which does not accurately reflect its advantages. Evolutionary algorithms can be an alternative to exhaustive search. They take a lot of time, especially because the fitness function requires expensive computation [281].

The deployment of appropriate learning and training algorithms, which have a significant impact on application accuracy and execution cost, is a challenge in the development of SNNs. The way that information is encoded by spikes presents another difficulty. Choosing the right training and learning algorithms to use can have a significant impact on the accuracy and cost of an application. Information encoding using spikes presents another difficulty. Although neural coding significantly improves the performance of SNNs, there are still problems about the ideal encoding strategy and how to create a learning algorithm that complements the encoding technique. It has become extremely difficult to create a learning algorithm that can train hidden neurons in a linked SNN. As neuromorphic computing is still in its infancy, significant work needs to be done to develop algorithms and hardware that can simulate human intellect.
