IEEE transactions on neural networks and learning systems
Aug 31, 2022
This work proposes a decision tree (DT)-based method for initializing a dendritic neuron model (DNM). Neural networks become larger and larger, thus consuming more and more computing resources. This calls for a strong need to prune neurons that do no...
IEEE transactions on neural networks and learning systems
Aug 31, 2022
The cerebellum plays a vital role in motor learning and control with supervised learning capability, while neuromorphic engineering devises diverse approaches to high-performance computation inspired by biological neural systems. This article present...
IEEE transactions on neural networks and learning systems
Aug 31, 2022
Perturbation has a positive effect, as it contributes to the stability of neural systems through adaptation and robustness. For example, deep reinforcement learning generally engages in exploratory behavior by injecting noise into the action space an...
Computational intelligence and neuroscience
Aug 29, 2022
In this paper, a comprehensive quantitative and biological neural network optimization model of sports industry structure is thoroughly studied and analyzed using knowledge graphs. To address the problems of poor performance interpretability deficien...
IEEE transactions on cybernetics
Aug 18, 2022
A large number of experiments have proved that the ring structure is a common phenomenon in neural networks. Nevertheless, a few works have been devoted to studying the neurodynamics of networks with only one ring. Little is known about the dynamics ...
Neural networks : the official journal of the International Neural Network Society
Aug 10, 2022
Backpropagation (BP) algorithm is one of the most basic learning algorithms in deep learning. Although BP has been widely used, it still suffers from the problem of easily falling into the local minima due to its gradient dynamics. Inspired by the fa...
Journal of computational neuroscience
Aug 10, 2022
An important function of the brain is to predict which stimulus is likely to occur based on the perceived cues. The present research studied the branching behavior of a computational network model of populations of excitatory and inhibitory neurons, ...
Proceedings of the National Academy of Sciences of the United States of America
Aug 10, 2022
We propose that coding and decoding in the brain are achieved through digital computation using three principles: relative ordinal coding of inputs, random connections between neurons, and belief voting. Due to randomization and despite the coarsenes...
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Aug 4, 2022
Owing to programmable nonlinear dynamics, magnetic domain wall (DW)-based devices can be configured to function as spintronic neurons, promising to execute sophisticated tasks as a human brain. Developing energy-efficient, CMOS compatible, reliable, ...
IEEE transactions on pattern analysis and machine intelligence
Aug 4, 2022
Deep neural networks are trained so as to achieve a kind of the maximum overall accuracy through a learning process using given training data. Therefore, it is difficult to fix them to improve the accuracies of specific problematic classes or classes...