期刊： NEURAL COMPUTATION, 2021; 33 (7)
Time series data sets often contain heterogeneous signals, composed of both continuously changing quantities and discretely occurring events. The coup......
期刊： NEURAL COMPUTATION, 2021; 33 (5)
Motor brain machine interfaces (BMIs) interpret neural activities from motor-related cortical areas in the brain into movement commands to control a p......
期刊： NEURAL COMPUTATION, 2021; 33 (9)
Learning new concepts rapidly from a few examples is an open issue in spike-based machine learning. This few-shot learning imposes substantial challen......
期刊： NEURAL COMPUTATION, 2021; 33 (8)
Deep learning is often criticized by two serious issues that rarely exist in natural nervous systems: overfitting and catastrophic forgetting. It can ......
期刊： NEURAL COMPUTATION, 2021; 33 (7)
This letter focuses on the problem of lifelong classification in the open world, the goal of which is to achieve an endless process of learning. Howev......
期刊： NEURAL COMPUTATION, 2021; 33 (4)
This work addresses the problem of network pruning and proposes a novel joint training method based on a multiobjective optimization model. Most of th......
期刊： NEURAL COMPUTATION, 2021; 33 (5)
Backdoor data poisoning attacks add mislabeled examples to the training set, with an embedded backdoor pattern, so that the classifier learns to class......
期刊： NEURAL COMPUTATION, 2021; 33 (2)
We propose a novel neural model with lateral interaction for learning tasks. The model consists of two functional fields: an elementary field to extra......
期刊： NEURAL COMPUTATION, 2020; 32 (1)
A spiking neural network (SNN) is a type of biological plausibility model that performs information processing based on spikes. Training a deep SNN ef......
期刊： NEURAL COMPUTATION, 2020; 32 (2)
Zero-shot learning (ZSL) aims to recognize unseen objects (test classes) given some other seen objects (training classes) by sharing information of at......
期刊： NEURAL COMPUTATION, 2020; 32 (12)
Spiking neural networks (SNNs) with the event-driven manner of transmitting spikes consume ultra-low power on neuromorphic chips. However, training de......
期刊： NEURAL COMPUTATION, 2020; 32 (12)
Pruning is an effective way to slim and speed up convolutional neural networks. Generally previous work directly pruned neural networks in the origina......
期刊： NEURAL COMPUTATION, 2020; 32 (5)
With the wide deployments of heterogeneous networks, huge amounts of data with characteristics of high volume, high variety, high velocity, and high v......
期刊： NEURAL COMPUTATION, 2020; 32 (2)
Neurons selective for faces exist in humans and monkeys. However, characteristics of face cell receptive fields are poorly understood. In this theoret......
期刊： NEURAL COMPUTATION, 2020; 32 (4)
Surface electromyography (sEMG) is an electrophysiological reflection of skeletal muscle contractile activity that can directly reflect neuromuscular ......
