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 - ULg > 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.

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