Spiking Neural Networks (SNNs) emerged as a promising solution in the field of Artificial Neural Networks (ANNs), attracting the attention of researchers due to their ability to mimic the human brain and process complex information with remarkable speed and accuracy. This research aimed to optimise the training process of Liquid State Machines (LSMs), a recurrent architecture of SNNs, by identifying the most effective weight range to be assigned in SNN to achieve the least difference between desired and actual output. The experimental results showed that by using spike metrics and a range of weights, the desired output and the actual output of spiking neurons could be effectively optimised, leading to improved performance of SNNs. The results were tested and confirmed using three different weight initialisation approaches, with the best results obtained using the Barabasi-Albert random graph method.

翻译：脉冲神经网络（SNNs）作为人工神经网络（ANNs）领域中的一种新兴解决方案，因其模拟人脑并以卓越速度和精度处理复杂信息的能力引起了研究人员的关注。本研究旨在通过识别脉冲神经网络中最有效的权重分配范围来优化液态状态机（LSMs，一种SNNs的循环架构）的训练过程，从而最小化期望输出与实际输出之间的差异。实验结果表明，通过使用脉冲度量指标和一系列权重值，能够有效优化脉冲神经元的期望输出与实际输出，进而提升SNNs的性能。该结果通过三种不同的权重初始化方法进行了测试与验证，其中采用巴拉巴西-阿尔伯特随机图方法取得了最优效果。