https://web.stanford.edu/group/brainsinsilicon/documents/SNNforBMI_NIPS11.pdf A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm Abstract: 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 euromorphic chips, which may offer power savings necessary to overcome a major obstacle to the successful clinical translation of neural motor prostheses. https://web.stanford.edu/group/brainsinsilicon/documents/SNNforBMI_NIPS11.pdf Creating the future w/Kwabena Boahen and his team at Stanford University Join in @ https://www.minds.com/groups/profile/525170186303578112/activity