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Spiking neural network decoder for brain-machine interfaces

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6 Author(s)
Julie Dethier ; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA ; Vikash Gilja ; Paul Nuyujukian ; Shauki A. Elassaad
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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.

Published in:

Neural Engineering (NER), 2011 5th International IEEE/EMBS Conference on

Date of Conference:

April 27 2011-May 1 2011