Deep Learning in Spiking Neural Networks for Brain-Inspired Artificial Intelligence-Reference-Cited by-同舟云学术

Deep Learning in Spiking Neural Networks for Brain-Inspired Artificial Intelligence

Published:2018-09-13 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 19th International Conference on Computer Systems and Technologies
language:
Short-container-title:

Author:

Kasabov Nikola¹

Affiliation:

1. Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3274005.3274006

Reference15 articles.

1. Kasabov N. Neural networks and genetic algorithms. Avtomatika i Informatika 8/9:51--60 (1990) (in Bulgarian) Kasabov N. Neural networks and genetic algorithms. Avtomatika i Informatika 8/9:51--60 (1990) (in Bulgarian)

2. Kasabov N. Incorporating neural networks into production systems and a practical approach towards the realisation of fuzzy expert systems. Computer Science and Informatics 21(2): 26--34 (1991) Kasabov N. Incorporating neural networks into production systems and a practical approach towards the realisation of fuzzy expert systems. Computer Science and Informatics 21(2): 26--34 (1991)

3. Kasabov N. and Shishkov S. A connectionist production system with partial match and its use for approximate reasoning. Connection Science 5(3/4): 275--305 (1993) Kasabov N. and Shishkov S. A connectionist production system with partial match and its use for approximate reasoning. Connection Science 5(3/4): 275--305 (1993)

4. Evolving spiking neural network—a survey

5. NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Spatio-Temporal Pattern Representation from AI Inspired Brain Model in Spiking Neural Network;Biointerface Research in Applied Chemistry;2021-11-19

2. Global research on artificial intelligence-enhanced human electroencephalogram analysis;Neural Computing and Applications;2021-01-07