Neural information processing : 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16-21, 2016, Proceedings
Author(s)
Bibliographic Information
Neural information processing : 23rd International Conference, ICONIP 2016, Kyoto, Japan, October 16-21, 2016, Proceedings
（Lecture notes in computer science, 9948 . LNCS sublibrary ; SL 1 . Theoretical computer science and general issues）
Springer, c2016
- pt. 2
- Other Title
-
ICONIP 2016
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Note
Other editors: Seiichi Ozawa, Kenji Doya, Kazushi Ikeda, Minho Lee, Derong Liu
Includes bibliographical references and index
Description and Table of Contents
Description
The four volume set LNCS 9947, LNCS 9948, LNCS 9949, and LNCS 9950 constitues the proceedings of the 23rd International Conference on Neural Information Processing, ICONIP 2016, held in Kyoto, Japan, in October 2016. The 296 full papers presented were carefully reviewed and selected from 431 submissions. The 4 volumes are organized in topical sections on deep and reinforcement learning; big data analysis; neural data analysis; robotics and control; bio-inspired/energy efficient information processing; whole brain architecture; neurodynamics; bioinformatics; biomedical engineering; data mining and cybersecurity workshop; machine learning; neuromorphic hardware; sensory perception; pattern recognition; social networks; brain-machine interface; computer vision; time series analysis; data-driven approach for extracting latent features; topological and graph based clustering methods; computational intelligence; data mining; deep neural networks; computational and cognitive neurosciences; theory and algorithms.
Table of Contents
Deep and reinforcement learning.- Big data analysis.- Neural data analysis.-Robotics and control.- Bio-inspired/energy efficient information processing.-Whole brain architecture.- Neurodynamics.- Bioinformatics.- Biomedical engineering.- Data mining and cybersecurity workshop.- Machine learning.-Neuromorphic hardware.- Sensory perception.- Pattern recognition.- Social networks.- Brain-machine interface.- Computer vision.- Time series analysis.-Data-driven approach for extracting latent features.- Topological and graph based clustering methods.- Computational intelligence.- Data mining.- Deep neural networks.- Computational and cognitive neurosciences.- Theory and algorithms.
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