Computational intelligence and neuroscience
Apr 13, 2022
Cardiovascular disease is one of the most serious diseases that threaten human health in the world today. Therefore, establishing a high-quality disease prediction model is of great significance for the prevention and treatment of cardiovascular dise...
IEEE transactions on neural networks and learning systems
Feb 28, 2022
Deep neural networks are vulnerable to adversarial attacks. More importantly, some adversarial examples crafted against an ensemble of source models transfer to other target models and, thus, pose a security threat to black-box applications (when att...
Computational intelligence and neuroscience
Dec 21, 2021
This paper analyzes and collates the research on traditional homeschooling attention mechanism and homeschooling attention mechanism based on two-way short- and long-term memory network intelligent computing IoT model and finds the superiority of two...
Computational intelligence and neuroscience
Nov 24, 2021
As a new brain-inspired computational model of artificial neural networks, spiking neural networks transmit and process information via precisely timed spike trains. Constructing efficient learning methods is a significant research field in spiking n...
BACKGROUND: Reminiscence is the act of thinking or talking about personal experiences that occurred in the past. It is a central task of old age that is essential for healthy aging, and it serves multiple functions, such as decision-making and intros...
As the number of known proteins has expanded, how to accurately identify DNA binding proteins has become a significant biological challenge. At present, various computational methods have been proposed to recognize DNA-binding proteins from only amin...
What is the physiological basis of long-term memory? The prevailing view in Neuroscience attributes changes in synaptic efficacy to memory acquisition, implying that stable memories correspond to stable connectivity patterns. However, an increasing b...
IEEE transactions on neural networks and learning systems
May 3, 2019
Learning long-term dependences (LTDs) with recurrent neural networks (RNNs) is challenging due to their limited internal memories. In this paper, we propose a new external memory architecture for RNNs called an external addressable long-term and work...
International journal of neural systems
Nov 14, 2018
In the framework of open-ended learning cognitive architectures for robots, this paper deals with the design of a Long-Term Memory (LTM) structure that can accommodate the progressive acquisition of experience-based decision capabilities, or what dif...
Neural networks : the official journal of the International Neural Network Society
Sep 8, 2017
In this paper, we propose a fractional-order neural network system with time-varying delays and reaction-diffusion terms. We first develop a new Mittag-Leffler synchronization strategy for the controlled nodes via impulsive controllers. Using the fra...
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