NDLI: Indirect training of a spiking neural network for flight control via spike-timing-dependent synaptic plasticity

Content Provider	IEEE Xplore Digital Library
Author	Foderaro, G. Henriquez, C. Ferrari, S.
Copyright Year	2010
Description	Author affiliation: Laboratory for Intelligent Systems and Control (LISC), Department of Mechanical Engineering, Duke University, Durham, NC 27708-0005, USA (Foderaro, G.; Ferrari, S.) \|\| Department of Biomedical Engineering, Duke University, Durham, NC 27708-0005, USA (Henriquez, C.)
Abstract	Recently, spiking neural networks (SNNs) have been shown capable of approximating the dynamics of biological neuronal networks, and of being trainable by biologically-plausible learning mechanisms, such as spike-timing-dependent synaptic plasticity. Numerical simulations also support the possibility that they may possess universal function approximation abilities. However the effectiveness of training algorithms to date is far inferior to those of other artificial neural networks. Moreover, they rely on directly manipulating the SNN weights, which may not be feasible in a number of their potential applications. This paper presents a novel indirect training approach to modulate spike-timing-dependent plasticity (STDP) in an action SNN that serves as a flight controller without directly manipulating its weights. A critic SNN is directly trained with a reward-based Hebbian approach to send spike trains to the action SNN, which in turn controls the aircraft and learns via STDP. The approach is demonstrated by training the action SNN to act as a flight controller for stability augmentation. Its performance and dynamics are analyzed before and after training through numerical simulations and Poincaré maps.
Starting Page	911
Ending Page	917
File Size	476235
Page Count	7
File Format	PDF
ISBN	9781424477456
ISSN	07431546
e-ISBN	9781424477463
DOI	10.1109/CDC.2010.5717260
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurons Training Artificial neural networks Aircraft Biological neural networks Biological system modeling
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Modeling and Simulation

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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