SPENSER: Towards a NeuroEvolutionary Approach for Convolutional Spiking
  Neural Networks
Henrique Branquinho Nuno Lourenço Ernesto Costa
Spiking Neural Networks (SNNs) have attracted recent interest due to their
energy efficiency and biological plausibility. However, the performance of SNNs
still lags behind traditional Artificial Neural Networks (ANNs), as there is no
consensus on the best learning algorithm for SNNs. Best-performing SNNs are
based on ANN to SNN conversion or learning with spike-based backpropagation
through surrogate gradients. The focus of recent research has been on
developing and testing different learning strategies, with hand-tailored
architectures and parameter tuning. Neuroevolution (NE), has proven successful
as a way to automatically design ANNs and tune parameters, but its applications
to SNNs are still at an early stage. DENSER is a NE framework for the automatic
design and parametrization of ANNs, based on the principles of Genetic
Algorithms (GA) and Structured Grammatical Evolution (SGE). In this paper, we
propose SPENSER, a NE framework for SNN generation based on DENSER, for image
classification on the MNIST and Fashion-MNIST datasets. SPENSER generates
competitive performing networks with a test accuracy of 99.42% and 91.65%
respectively.