Frontiers

Spiking Neural Networks at Scale: Learning Algorithms, Architectures, and Real-World Applications

Spiking Neural Networks (SNNs) are a cutting-edge approach to artificial intelligence, designed to emulate the brain's architecture and functionality. Their ...
IEEE

Machine Learning Techniques for Stock Market Prediction: Neural Networks and Soft Computing Approaches

Abstract: This paper provides an updated overview of recent literature on stock market prediction using machine learning methods. Neural network-based models, particularly those focused on predicting ...
IEEE

Reconfiguring Gene Regulatory Neural Network Computing for Regulating Biofilm Formation

Abstract: The Gene Regulatory Network (GRN) in biological cells orchestrates essential functions for adaptation and survival in diverse environments, drawing on structural similarities with the ...
Frontiers

Efficient spiking convolutional neural networks accelerator with multi-structure compatibility

1 College of Electronic Science and Technology, National University of Defense Technology, Changsha, China 2 College of Electronics and Internet of Things, Chongqing Polytechnic University of ...
C&EN

A Multi-Stage Graph Neural Network–Physics-Informed Neural Network (GNN–PINN) Framework for Thermodynamic Property Prediction

Department of Chemistry and Chemical Engineering, Education and Research Center for Smart Energy and Materials, Inha University, Incheon 22212, Republic of Korea ...
eLife

Online reinforcement learning of state representation in recurrent network supported by the power of random feedback and biological constraints

Can a plastic RNN serve as a basis function for learning to estimate value. In previous work this was shown to be the case, with a similar architecture to that proposed here. The learning rule in ...