SNIB: Improving Spike-Based Machine Learning Using Nonlinear Information Bottleneck
Author:
Affiliation:
1. School of Electrical and Information Engineering, Tianjin University, Tianjin, China
2. Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, China
Funder
National Natural Science Foundation of China
Young Elite Scientists Sponsorship Program by CAST
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Control and Systems Engineering,Software
Link
http://xplorestaging.ieee.org/ielx7/6221021/10321818/10235316.pdf?arnumber=10235316
Reference50 articles.
1. Towards Intelligent Intracortical BMI (i$^2$BMI): Low-Power Neuromorphic Decoders That Outperform Kalman Filters
2. A Comparison of Low-Complexity Real-Time Feature Extraction for Neuromorphic Speech Recognition
3. Spiking Neural Networks and online learning: An overview and perspectives
4. An Ultra Low-Power Memristive Neuromorphic Circuit for Internet of Things Smart Sensors
5. Self-supervised learning of event-based optical flow with spiking neural networks;hagenaars;Proc Adv Neural Inf Process Syst,2021
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