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A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm
Julie Dethier · Paul Nuyujukian · Chris Eliasmith · Terrence C Stewart · Shauki A Elasaad · Krishna V Shenoy · Kwabena A Boahen

Mon Dec 12 10:00 AM -- 02:59 PM (PST) @

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 neuromorphic chips, which may offer power savings necessary to overcome a major obstacle to the successful clinical translation of neural motor prostheses.

Author Information

Julie Dethier (University of Liege)
Paul Nuyujukian (Stanford University)
Chris Eliasmith (U of Waterloo)
Terrence C Stewart (University of Waterloo)
Shauki A Elasaad
Krishna V Shenoy (Stanford University)
Kwabena A Boahen (Stanford University)

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