Genetic Algorithmic Parameter Optimisation of a Recurrent Spiking Neural Network Model

Ifeatu Ezenwe, Alok Joshi, KongFatt Wong-Lin
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引用次数: 1

Abstract

Neural networks are complex algorithms that loosely model the behaviour of the human brain. They play a significant role in computational neuroscience and artificial intelligence. The next generation of neural network models is based on the spike timing activity of neurons: spiking neural networks (SNNs). However, model parameters in SNNs are difficult to search and optimise. Previous studies using genetic algorithm (GA) optimisation of SNNs were focused mainly on simple, feedforward, or oscillatory networks, but not much work has been done on optimising cortex-like recurrent SNNs. In this work, we investigated the use of GAs to search for optimal parameters in recurrent SNNs to reach targeted neuronal population firing rates, e.g. as in experimental observations. We considered a cortical column based SNN comprising 1000 Izhikevich spiking neurons for computational efficiency and biologically realism. The model parameters explored were the neuronal biased input currents. First, we found for this particular SNN, the optimal parameter values for targeted population averaged firing activities, and the convergence of algorithm by ~100 generations. We then showed that the GA optimal population size was within ~16-20 while the crossover rate that returned the best fitness value was ~0.95. Overall, we have successfully demonstrated the feasibility of implementing GA to optimize model parameters in a recurrent cortical based SNN.
循环脉冲神经网络模型的遗传算法参数优化
神经网络是一种复杂的算法,可以松散地模拟人类大脑的行为。它们在计算神经科学和人工智能中发挥着重要作用。下一代神经网络模型是基于神经元的尖峰定时活动:尖峰神经网络(SNNs)。然而,snn中的模型参数很难搜索和优化。以往使用遗传算法(GA)优化snn的研究主要集中在简单、前馈或振荡网络上,但在优化类皮质循环snn方面做得不多。在这项工作中,我们研究了使用GAs在循环snn中搜索最优参数以达到目标神经元群放电率,例如在实验观察中。我们考虑了一个基于皮质柱的SNN,包括1000个Izhikevich尖峰神经元,以提高计算效率和生物学真实性。研究的模型参数为神经元偏置输入电流。首先,我们发现对于这个特定的SNN,目标群体平均射击活动的最优参数值,以及算法的收敛约100代。结果表明,遗传最优种群大小在~16 ~ 20之间,获得最佳适应度值的交叉率为~0.95。总的来说,我们已经成功地证明了在基于循环皮层的SNN中实现遗传算法优化模型参数的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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