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In this paper the electronic circuit implementation of a fuzzy neuron model with a fuzzy Gupta integrator is presented.This neuron model simulates the performance and the fuzzy response of a fast-spiking biological neuron. The fuzzyneuron response is analyzed for two classical (non-fuzzy) input signals, the results are spike trains with relative andabsolute refractory period and an axonal delay. A comparison between the response of the proposed fuzzy neuronmodel and the intracellular registers of biological fast-spiking cortical interneurons is made, as well as the transientspresented at the beginning of each spike train. Also the results obtained from the electronic circuit of the fuzzy neuronmodel with the Matlab™ simulation of the mathematical model are compared.

Data driven fuzzy neural networks have some disadvantages, such as high dimensions and complex learning process. Also, the obtained models are difficult to interpret. In this paper, we propose a novel simple fuzzy system, which uses fuzzy adaptive neurons. This novel model takes the advantages of the interpretability of the fuzzy system and good approximation ability of the neural networks. We propose a simple learning algorithm for the novel fuzzy system. The stability analysis is given. We successfully apply this fuzzy model for the earthquake modeling. Comparisons with the popular fuzzy neural model are proposed.

This paper introduces a new spike activation function (SPKAF) or spike membership function for fuzzy adaptive neurons (FAN), developed for decoding spatiotemporal information with spikes, optimizing digital signal processing. A solution with the adaptive network-based fuzzy inference system (ANFIS) method is proposed and compared with that of the FAN-SPKAF model, obtaining very precise simulation results. Stability analysis of systems models is presented. An application to voice recognition using solfeggio syllables in Spanish is performed experimentally, comparing the methods of FAN-step activation function (STEPAF)-SPKAF, Augmented Spiking Neuron Model, and Augmented FAN-STEPAF-SPKAF, achieving very good results.