SPENSER: Towards a NeuroEvolutionary Approach for Convolutional Spiking Neural Networks
This work addresses the challenge of optimizing SNNs for image classification, offering an automated approach that could benefit researchers in neuromorphic computing, though it is incremental as it builds on existing neuroevolution methods.
The authors tackled the performance gap between Spiking Neural Networks (SNNs) and traditional Artificial Neural Networks by proposing SPENSER, a neuroevolutionary framework that automatically designs SNNs, achieving test accuracies of 99.42% on MNIST and 91.65% on Fashion-MNIST.
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.