This paper investigates the passivity analysis problem for a class of discrete-time neural
networks subject to the mixed time-delays and randomly occurring quantization effects. Both …

Due to variation of neutron energy spectrum in the target sample during the activation
process and to peak overlapping caused by the Compton effect with gamma radiations …

Artificial neural network (ANN) represents one of artificial intelligence methods in the field of
modeling and uncertainty in different applications. The objective of the proposed work was …

With the advent of Internet of Things IoT, it is expected that the growth of Wireless Sensor
Networks WSN will increase exponentially as it provides a medium for IoT to sense the real …

Network communications have been widely studied in the last decades in different research
fields: artificial intelligence, computer science, biology, medicine and psychology among …

Experimental evidence has revealed the existence of characteristic spiking features in
different neural signals, eg, individual neural signatures identifying the emitter or functional …

Experimental evidence has revealed that different living neural systems can “sign” their
output signals with some specific neural signature. Although experimental and modeling …

Spiking Neural Networks constitute the most promising approach to develop realistic
Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information …

En este artículo se enfatiza la necesidad de una mayor transferencia de conocimiento de la
Neurociencia a la Inteligencia Artificial y de la Inteligencia Artificial a la Neurociencia. Para …

An artificial neural network (ANN) has been trained with real-sample spectra of radioactive
materials. Following the training stage ANN was applied to a subset of similar samples …