总体而言，《IEEE Transactions on Neural Networks and Learning ...

Beijing, Feb. 06, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification ...

Neuronal network dynamics and electrophysiology encompass the study of how networks of neurons communicate, adapt and process information through electrical signals. This field integrates insights ...

2026年1月19日，广东省智能科学与技术研究院王超名研究组在国际知名学术期刊Nature Communications在线发表题为"Model-agnostic linear-memory online learning in spiking neural networks"的研究论文。该研究首次实现了模型无关、线性内存、全自动编译的脉冲神经网络（Spiking Neural Networks, ...

AI became powerful because of interacting mechanisms: neural networks, backpropagation and reinforcement learning, attention, ...

Uncovering the relationship between structure (connectivity) and function (neuronal activity) is a fundamental question across many areas of biology. However, investigating this directly in animal ...

A new publication from Opto-Electronic Technology; DOI   10.29026/oet.2025.250011, discusses integrated photonic synapses, neurons, memristors, and neural networks for photonic neuromorphic computing.

In order to uncover the relationship between structure and function, researchers used microfluidic devices to study neuronal networks. Uncovering the relationship between structure (connectivity) and ...

Researchers have mapped and catalogued more than 70,000 synaptic connections from about 2,000 rat neurons, using a silicon chip capable of recording small yet telltale synaptic signals from a large ...

Researchers have devised a way to make computer vision systems more efficient by building networks out of computer chips’ logic gates. Networks programmed directly into computer chip hardware can ...

Deep learning is increasingly used in financial modeling, but its lack of transparency raises risks. Using the well-known Heston option pricing model as a benchmark, researchers show that global ...