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Spiking neural networks have many different parameters that can be difficult to adjust manually to the current classification problem. The analysis and selection of coefficients\u2019 values in the network can be analyzed as an optimization problem. A practical method for automatic selection of them can decrease the time needed to develop such a model. In this paper, we propose the use of a heuristic approach to analyze and select coefficients with the idea of collaborative working. The proposed idea is based on parallel analyzing of different coefficients and choosing the best of them or average ones. This type of optimization problem allows the selection of all variables, which can significantly affect the convergence of the accuracy. Our proposal was tested using network simulators and popular databases to indicate the possibilities of the described approach. Five different heuristic algorithms were tested and the best results were reached by Cuckoo Search Algorithm, Grasshopper Optimization Algorithm, and Polar Bears Algorithm.<\/jats:p>","DOI":"10.1007\/s00521-021-06824-8","type":"journal-article","created":{"date-parts":[[2021,12,16]],"date-time":"2021-12-16T22:09:35Z","timestamp":1639692575000},"page":"13187-13200","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A heuristic approach to the hyperparameters in training spiking neural networks using spike-timing-dependent plasticity"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1972-5979","authenticated-orcid":false,"given":"Dawid","family":"Po\u0142ap","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcin","family":"Wo\u017aniak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Waldemar","family":"Ho\u0142ubowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robertas","family":"Dama\u0161evi\u010dius","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,12,16]]},"reference":[{"issue":"3","key":"6824_CR1","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1016\/j.neuron.2009.04.021","volume":"62","author":"BB Averbeck","year":"2009","unstructured":"Averbeck BB (2009) Poisson or not poisson: differences in spike train statistics between parietal cortical areas. 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