[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.
