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）作为人工神经网络领域的一个有前景的解决方案出现，因其能够模拟人脑并以卓越的速度和准确性处理复杂信息而吸引了研究人员的关注。本研究旨在通过确定在SNN中分配的最有效权重范围，以优化液态状态机（LSMs，一种SNN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，通过使用脉冲度量标准和权重范围，可以有效地优化脉冲神经元的期望输出与实际输出，从而提升SNNs的性能。这些结果通过三种不同的权重初始化方法进行了测试和验证，其中使用Barabasi-Albert随机图方法获得了最佳结果。