Reference : A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control A...
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Electrical & electronics engineering
Engineering, computing & technology : Multidisciplinary, general & others
http://hdl.handle.net/2268/100574
A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm
English
Dethier, Julie mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systmod > >]
Nuyujukian, Paul mailto [Stanford University > Department of Bioengineering and School of Medicine > > >]
Eliasmith, Chris mailto [University of Waterloo, Canada > Centre for Theoretical Neuroscience > > >]
Stewart, Terry mailto [University of Waterloo, Canada > Centre for Theoretical Neuroscience > > >]
Elassaad, Shauki A. [Stanford University, > Department of Bioengineering > > >]
Shenoy, Krishna V. mailto [Stanford University > Department of Electrical Engineering, Department of Bioengineering, and Department of Neurobiology > > >]
Boahen, Kwabena mailto [Stanford University > Department of Bioengineering > > >]
Dec-2011
Advances in Neural Information Processing Systems (NIPS) 24
Yes
No
International
Advances in Neural Information Processing Systems (NIPS) 24
December 12th - December 15th 2011
Granada
Spain
[en] neural engineering ; spiking neural network ; brain-machine interfaces
[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.
http://hdl.handle.net/2268/100574

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