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21 October 2011
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A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm
Dethier, Julie  ; Nuyujukian, Paul; Eliasmith, Chris et al.
2011 • In Advances in Neural Information Processing Systems (NIPS) 24
Peer reviewed
 

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Keywords :
neural engineering; spiking neural network; brain-machine interfaces
Abstract :
[en] Motor prostheses aim to restore function to disabled patients. Despite compelling proof of concept systems, barriers to clinical translation remain. One challenge is to develop a low-power, fully-implantable system that dissipates only minimal power so as not to damage tissue. To this end, we implemented a Kalman-filter based decoder via a spiking neural network (SNN) and tested it in brain-machine interface (BMI) experiments with a rhesus monkey. The Kalman filter was trained to predict the arm’s velocity and mapped on to the SNN using the Neural Engineer- ing Framework (NEF). A 2,000-neuron embedded Matlab SNN implementation runs in real-time and its closed-loop performance is quite comparable to that of the standard Kalman filter. The success of this closed-loop decoder holds promise for hardware SNN implementations of statistical signal processing algorithms on neu- romorphic chips, which may offer power savings necessary to overcome a major obstacle to the successful clinical translation of neural motor prostheses.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Electrical & electronics engineering
Author, co-author :
Dethier, Julie ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systmod
Nuyujukian, Paul;  Stanford University > Department of Bioengineering and School of Medicine
Eliasmith, Chris;  University of Waterloo, Canada > Centre for Theoretical Neuroscience
Stewart, Terry;  University of Waterloo, Canada > Centre for Theoretical Neuroscience
Elassaad, Shauki A.;  Stanford University, > Department of Bioengineering
Shenoy, Krishna V.;  Stanford University > Department of Electrical Engineering, Department of Bioengineering, and Department of Neurobiology
Boahen, Kwabena;  Stanford University > Department of Bioengineering
Language :
English
Title :
A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm
Publication date :
December 2011
Event name :
Advances in Neural Information Processing Systems (NIPS) 24
Event place :
Granada, Spain
Event date :
December 12th - December 15th 2011
Audience :
International
Main work title :
Advances in Neural Information Processing Systems (NIPS) 24
Peer reviewed :
Peer reviewed

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