Training Spiking Neural Models Using Artificial Bee Colony

Vázquez, Roberto A.; Garro, Beatriz A.

doi:10.1155/2015/947098

Cited by 27 publications

(16 citation statements)

References 64 publications

(81 reference statements)

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“…We chose six experimental evolutionary algorithms, DE [13], CS [14], PSO [12], HS [19], ABC [18], and CRO [21], to compare with the proposed algorithm to evaluate its performance.…”

Section: ) Experimental Conditionsmentioning

confidence: 99%

“…The authors argued that the results obtained with the spiking neuron model trained with the cuckoo search algorithm were good. Vazquez et al proposed a learning strategy based on artificial bee colonies to train a spiking neuron aiming to solve pattern recognition problems [18]. Saleh et al proposed a hybrid harmony search algorithm with evolving SNNs for classification problems [19].…”

Section: Introductionmentioning

confidence: 99%

“…To solve the above problems, this paper mainly explores a learning algorithm with high learning efficiency and high biogenesis, in line with the rules of biological cognition, a wide application range, and the ability to control the output mode of neurons. Therefore, based on previous work [13], [14], [18], [19], [21], this paper aims to study the learning algorithm based on the evolutionary membrane algorithm to further improve the generalization performance of SNNs. The evolutionary membrane algorithm is a heuristic algorithm influenced by the internal function and structure of the cell and is an evolutionary algorithm (EA) [22], [23].…”

Section: Introductionmentioning

confidence: 99%

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Spike Neural Network Learning Algorithm Based on an Evolutionary Membrane Algorithm

et al. 2021

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As one of the important artificial intelligence fields, brain-like computing attempts to give machines a higher intelligence level by studying and simulating the cognitive principles of the human brain. Compared with the traditional neural network, the spiking neural network (SNN) has better biogenesis and stronger computing power. In this paper, an SNN learning model based on an evolutionary membrane algorithm is proposed to solve the problem of supervised classification. The proposed algorithm uses the P system's object, reaction rules, and membrane structure to solve these problems. Specifically, the proposed algorithm can automatically adjust the learning parameters of the network by adjusting the synaptic weight in the learning stage of the spiking neural model according to different application data, providing a better solution model for balance exploration and exploitation. In the simulation experiment, effectiveness verification research is carried out. The simulation results show that compared with other experimental algorithms, the proposed algorithm has a competitive advantage in solving twelve supervised classification benchmark problems through learning curves and quantified classification results.INDEX TERMS evolutionary membrane algorithm, P systems, spiking neural network, supervised classification.

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“…We chose six experimental evolutionary algorithms, DE [13], CS [14], PSO [12], HS [19], ABC [18], and CRO [21], to compare with the proposed algorithm to evaluate its performance.…”

Section: ) Experimental Conditionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Spike Neural Network Learning Algorithm Based on an Evolutionary Membrane Algorithm

et al. 2021

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“…Therefore, in multi-layer networks, the input values to the inputs of the first layer, allow the signals to propagate through the network, and read the output values where output of the th node can be described by the function in Eq. 4.1 below [25], [28].…”

Section: Rnn Training Using Abc Algorithmmentioning

confidence: 99%

Recurrent Neural Network Training using ABC Algorithm For Traffic Volume Prediction

Bosire

2019

IJCAI

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This study evaluates the use of the Artificial Bee Colony (ABC) algorithm to optimize the Recurrent Neural Network (RNN) that is used to analyze traffic volume. Related studies have shown that Deep Neural Networks are superseding the Shallow Neural Networks especially in terms of performance. Here we show that using the ABC algorithm in training the Recurrent Neural Network yields better results, compared to several other algorithms that are based on statistical or heuristic techniques that were preferred in earlier studies. The ABC algorithm is an example of swarm intelligence algorithms which are inspired by nature. Therefore, this study evaluates the performance of the RNN trained using the ABC algorithm for the purpose of forecasting. The performance metric used in this study is the Mean Squared Error (MSE) and ultimately, the outcome of the study may be generalized and extended to suit other domains.Povzetek: Ocena uspešnosti algoritma umetne kolonije čebelje pri optimizaciji ponavljajoče se nevronske mreže.

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“…They can combine diversification and intensification in their search for finding optimal solutions, jointly with an objective function. A representative case is given in [5], where the classic ABC algorithm takes the part of one supervised training process. On the other hand, advanced optimisation for designing SNNs based on evolutionary metaheuristics has also been proposed [6].…”

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confidence: 99%

Spiking neural network approaches PCA with metaheuristics

et al. 2020

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This Letter presents meaningful results that demonstrate the reduction of dimensionality by spiking neural networks (SNNs) on benchmarking data. This experimental scheme includes metaheuristics, namely, the artificial bee colony algorithm (ABC algorithm) for finding optimal conductance values in the SNNs. Therefore, the objective function in the used ABC algorithm leads the SNNs to compute the principal component analysis (PCA), efficiently. The eigendecomposition of the information drawn by the SNNs in the training phase is the base of the formulated objective function. In these experiments, the Izhikevich model represents the spiking neurons, which have biological plausibility with parameters for reproducing a uniform firing rate. The visualisation of clusters in the 3D PCA space, whose sample values are compared with the PCA function in Matlab, is also shown; this comparison demonstrates an acceptable error in the MSE sense.

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Training Spiking Neural Models Using Artificial Bee Colony

Cited by 27 publications

References 64 publications

Spike Neural Network Learning Algorithm Based on an Evolutionary Membrane Algorithm

Spike Neural Network Learning Algorithm Based on an Evolutionary Membrane Algorithm

Recurrent Neural Network Training using ABC Algorithm For Traffic Volume Prediction

Spiking neural network approaches PCA with metaheuristics

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