SPENSER：迈向卷积脉冲神经网络的神经进化方法
SPENSER: Towards a NeuroEvolutionary Approach for Convolutional Spiking Neural Networks
AI总结：
本文提出SPENSER，一个基于DENSER的神经进化框架，用于自动生成卷积脉冲神经网络，在MNIST和Fashion-MNIST数据集上分别达到99.42%和91.65%的测试准确率。
摘要：
脉冲神经网络（SNNs）因其能效和生物合理性而备受关注。然而，SNNs的性能仍落后于传统人工神经网络（ANNs），因为目前对SNNs的最佳学习算法尚无共识。表现最好的SNNs基于ANN到SNN的转换或通过代理梯度进行基于脉冲的反向传播学习。近期研究的重点是开发和测试不同的学习策略，并进行手工定制的架构和参数调优。神经进化（NE）已被证明是自动设计ANNs和调优参数的成功方法，但其在SNNs中的应用仍处于早期阶段。DENSER是一个基于遗传算法（GA）和结构化语法进化（SGE）原理，用于自动设计和参数化ANNs的NE框架。本文提出SPENSER，一个基于DENSER的SNN生成NE框架，用于在MNIST和Fashion-MNIST数据集上进行图像分类。SPENSER生成的网络性能具有竞争力，测试准确率分别达到99.42%和91.65%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
