Spiking neural network approaches PCA with metaheuristics

Abstract

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.

Automotive Vulnerability Analysis for Deep Learning Blockchain Consensus Algorithm

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Approaching Optimal Nonlinear Dimensionality Reduction by a Spiking Neural Network

“…On the other hand, training spiking systems based on metaheuristics could be divided into two branches, firing rate and spike-time delay. The works in [6][7][8] belong to the first category meanwhile those cited in [9][10][11] to the second one. The cuckoo search algorithm in [6], which uses a random walk strategy, achieves the spiking training phase on six complex databases.…”

“…They concluded that the ABC algorithm improves the quality of the results compared with earlier metaheuristic methods. The work in [8] trains with the ABC algorithm a one-layer spiking neural network to reduce the dimension of complex datasets into 3D spaces in a linear way. In improving the training of spiking systems, similar researchers are found in [9,10] a decade apart.…”

“…The works [6][7][8][9][10][11][12] cited in the Introduction section about training spiking systems through using metaheuristics focus on either improving the backpropagation type algorithm or demonstrating the performance of evolutionary algorithms do not get concerned with using another objective function from artificial intelligence theory, but the fundamental MSE estimator. For completeness, we mention alternative and recent strategies to create spiking systems that include transforming continuous deep neural systems into spiking systems [32], using particular training formulations which compare superb with the SpikeProp algorithm but associated with particular loss function [33], retaking the biological STDP rule for convolutional deep networks [34], tuning convolutional-type networks with a biologically plausible algorithm [35], to name a few.…”