2020 31st Irish Signals and Systems Conference (ISSC)最新文献_第7页

Genetic Algorithmic Parameter Optimisation of a Recurrent Spiking Neural Network Model 循环脉冲神经网络模型的遗传算法参数优化

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-03-30 DOI: 10.1109/ISSC49989.2020.9180185

Ifeatu Ezenwe, Alok Joshi, KongFatt Wong-Lin

{"title":"Genetic Algorithmic Parameter Optimisation of a Recurrent Spiking Neural Network Model","authors":"Ifeatu Ezenwe, Alok Joshi, KongFatt Wong-Lin","doi":"10.1109/ISSC49989.2020.9180185","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180185","url":null,"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.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124486578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}