Reference : Spiking Neural Network Decoder for Brain-Machine Interfaces

	Document type :	Scientific congresses and symposiums : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Electrical & electronics engineering Engineering, computing & technology : Multidisciplinary, general & others
	To cite this reference:	http://hdl.handle.net/2268/103605

Title :	Spiking Neural Network Decoder for Brain-Machine Interfaces
Language :	English
Author, co-author :	Dethier, Julie [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systmod > >]
	Gilja, Vikash [Stanford University > Computer Science, Institute for Neuro-Innovation, and Translational Neuroscience > > >]
	Nuyujukian, Paul [Stanford University > Department of Bioengineering and MSTP > > >]
	Elassaad, Shauki A. [Stanford University > Bioengineering > > >]
	Shenoy, Krishna V. [Stanford University > Electrical Engineering and Bioengineering, and Neurosciences Program > > >]
	Boahen, Kwabena [Stanford university > Bioengineering > > >]
Publication date :	May-2011
Main document title :	Proceedings of the 5th International IEEE EMBS Conference on Neural Engineering
Peer reviewed :	Yes
On invitation :	No
Audience :	International
Event name :	5th International IEEE EMBS Conference on Neural Engineering
Event date :	April 27 - May 1, 2011
Event place (city) :	Cancun
Event country :	Mexico
Abstract :	[en] We used a spiking neural network (SNN) to decode neural data recorded from a 96-electrode array in premotor/motor cortex while a rhesus monkey performed a point-to-point reaching arm movement task. We mapped a Kalman-filter neural prosthetic decode algorithm developed to predict the arm’s velocity on to the SNN using the Neural Engineering Framework and simulated it using Nengo, a freely available software package. A 20,000-neuron network matched the standard decoder’s prediction to within 0.03% (normalized by maximum arm velocity). A 1,600-neuron version of this network was within 0.27%, and run in real-time on a 3GHz PC. These results demonstrate that a SNN can implement a statistical signal processing algorithm widely used as the decoder in high-performance neural prostheses (Kalman filter), and achieve similar results with just a few thousand neurons. Hardware SNN implementations—neuromorphic chips—may offer power savings, essential for realizing fully-implantable cortically controlled prostheses.
Permalink :	http://hdl.handle.net/2268/103605

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