A New Fuzzy Spiking Neural Network Based on Neuronal Contribution Degree

This article presents a novel network, contribution-degree-based spiking neural network (CDSNN), which combines ideas of spiking neural network (SNN) and fuzzy set theory. In this framework, two types of information, interval and instantaneous information conveyed by the membrane potential are described by two concepts such as area under membrane potential (AUM) and firing strength. Given that the neuron with large AUM or strong firing strength would enhance the frequency of action potentials of its postsynaptic neurons, the connection between the neuron and its postsynaptic neurons should be strengthened. Combined with an idea of membership function, three contribution degrees ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{\mu}_E$</tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{\mu}_S$</tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{\mu}_{ES}$</tex-math></inline-formula> ) are defined to quantify the ability of a neuron to provide information for postsynaptic neurons. According to these three degrees, the corresponding SpikeProp learning algorithms, referred to as SPE, SPS, and SPES, are developed. Experimental results obtained on ten benchmark datasets, one high-dimensional feature dataset, one big dataset, and one time series dataset with some commonly used algorithms, networks and CDSNN demonstrate that CDSNN can achieve improved performance in terms of accuracy, generalization, precision, recall and F-measure. The article demonstrates that the mechanism by which interval-instantaneous information is simultaneously learned in a SNN is feasible.