Learning, Architectures and Large-Scale Systems Yang, Shuangming. creator author. aut http://id.loc.gov/vocabulary/relators/aut Chen, Badong. author. aut http://id.loc.gov/vocabulary/relators/aut SpringerLink (Online service) text sz 2024 1st ed. 2024. monographic eng

electronic

electronic resource

access XIX, 239 p. 131 illus., 109 illus. in color. online resource. This book provides a valuable resource on the design of neuromorphic intelligence, which serves as a computational foundation for building compact and low-power brain-inspired intelligent systems. The book introduces novel spiking neural network learning algorithms, including spike-based learning based on the multi-compartment model and spike-based learning with information theory. These offer important insights and academic value for readers to grasp the latest advances in neural-inspired learning. Additionally, the book presents insights and approaches to the design of scalable neuromorphic architectures, which are crucial foundations for achieving highly cognitive and energy-efficient computing systems. Furthermore, the book introduces representative large-scale neuromorphic systems and reviews several recently implemented large-scale digital neuromorphic systems by the authors, providing corresponding application scenarios. Introduction -- Introduction for Neuromorphic Intelligence -- Spike-based Learning with Multi-compartment Model -- Spike-based Learning with Information Theory -- Scalable Architectures on Neuromorphic Systems -- Large-scale Digital Neuromorphic Systems -- Conclusion. by Shuangming Yang, Badong Chen. Computational intelligence Artificial intelligence Neurosciences Computational Intelligence Artificial Intelligence Neuroscience Q342 9783031578731 https://doi.org/10.1007/978-3-031-57873-1 https://doi.org/10.1007/978-3-031-57873-1 240515 20251030112043.0 978-3-031-57873-1