Abstract
The third type of neural network called spiking is developed due to a more accurate representation of neuronal activity in living organisms. 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’ 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.
Highlights
Spiking neural networks are representatives of the third type of neural network, where, unlike its predecessor, it models the operation of information flow in the human body in much more detail
We propose the use of a heuristic approach to analyze and select coefficients with the idea of collaborative working
We propose using heuristic algorithms for searching for the best coefficients for spiking neural networks
Summary
Spiking neural networks are representatives of the third type of neural network, where, unlike its predecessor, it models the operation of information flow in the human body in much more detail. The automatic method can help in decreasing the time to develop the best neural structure Based on these observations, we noticed that the selection of parameters can be reduced to an optimization problem. We noticed that the selection of parameters can be reduced to an optimization problem It is important because of the huge development of heuristic techniques inspired by nature. We propose using heuristic algorithms for searching for the best coefficients for spiking neural networks. The proposed approach is based on the modification of the heuristic operation idea and by proposing a fitness function that can be used for hyperparameters problems with the use of the federated learning method. – Evaluation of the proposal by five different heuristic algorithms inspired by nature for selecting the best one
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