Neuromorphic computing based on spiking neural networks has the potential to significantly
improve on-line learning capabilities and energy efficiency of artificial intelligence, specially …

This paper introduces a new spike activation function (SPKAF) or spike membership function
for fuzzy adaptive neurons (FAN), developed for decoding spatiotemporal information with …

Computers nowadays have different components for data storage and data processing,
making data transfer between these units a bottleneck for computing speed. Therefore, so …

Neural modelling tools are increasingly employed to describe, explain, and predict the
human brain's behavior. Among them, spiking neural networks (SNNs) make possible the …

We propose and demonstrate a plastic photonic synapse to achieve all-optical synaptic
plasticity based on the potentiated and depressed dynamics in commercially available …

The recent “multi-neuronal spike sequence detector”(MNSD) architecture integrates the
weight-and delay-adjustment methods by combining heterosynaptic plasticity with the …

Simple Summary A computational model of primates' early visual processing, showing
orientation selectivity, is presented. The system importantly integrates two key elements:(1) a …

The concept of “surface modeling” generally describes the process of representing a
physical or artificial surface by a geometric model, namely a mathematical expression …

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 …

The identification of nonlinear systems is a complex task. This article presents a method
comparison between the new Fuzzy Adaptive Neurons (FAN), Radial Basis Function …