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.

翻译：摘要：脉冲神经网络（SNN）因其能效性与生物合理性近年来受到广泛关注。然而，由于缺乏共识性的最优学习算法，SNN的性能仍落后于传统人工神经网络（ANN）。当前性能最佳的SNN多基于ANN-SNN转换或通过替代梯度实现基于脉冲的反向传播学习。近期研究重点在于开发与测试不同学习策略，并配合手动定制架构与参数调优。神经进化（NE）已被证明可自动设计ANN架构并优化参数，但其在SNN领域的应用仍处于早期阶段。DENSER是一个基于遗传算法（GA）与结构化语法演化（SGE）原理的NE框架，专用于ANN的自动设计与参数化。本文提出SPENSER——基于DENSER的SNN生成NE框架，针对MNIST与Fashion-MNIST数据集进行图像分类任务。实验结果表明，SPENSER生成的网络性能具有竞争力，在测试集上的准确率分别达到99.42%和91.65%。