Reference : A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control A...
Scientific congresses and symposiums : Poster
Engineering, computing & technology : Multidisciplinary, general & others
Engineering, computing & technology : Electrical & electronics engineering
A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm
Dethier, Julie mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Nuyujukian, Paul [ > > ]
Elassaad, Shauki A. [ > > ]
Shenoy, Krishna V. [ > > ]
Boahen, Kwabena [ > > ]
IAP DYSCO Study day
29th of November 2011
IAP DYSCO network
[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 Engineering 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 neuromorphic chips, which may offer power savings necessary to overcome a major obstacle to the successful clinical translation of neural motor prostheses.

There is no file associated with this reference.

Bookmark and Share SFX Query

All documents in ORBi are protected by a user license.