Reference : Spiking Neural Network Decoder for Brain‐Machine Interfaces
Scientific conferences in universities or research centers : Scientific conference in universities or research centers
Engineering, computing & technology : Electrical & electronics engineering
Engineering, computing & technology : Multidisciplinary, general & others
Spiking Neural Network Decoder for Brain‐Machine Interfaces
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 [> >]
UGR meeting in Nancy
28 November 2011
[en] We used a spiking neural network (SNN) to decode neural data recorded from two 96-­electrode arrays in premotor and 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 tested it in brain-­‐machine interface (BMI) experiments with a rhesus monkey. 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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