Dethier Julie, Gilja Vikash, Nuyujukian Paul, Elassaad Shauki A, Shenoy Krishna V, Boahen Kwabena
Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
Department of Computer Science and Stanford Institute for Neuro-Innovation and Translational Neuroscience, Stanford University, Stanford, CA 94305, USA.
Int IEEE EMBS Conf Neural Eng. 2011. doi: 10.1109/NER.2011.5910570.
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 , 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.
我们使用了一个脉冲神经网络(SNN)来解码从恒河猴运动前区/运动皮层的96电极阵列记录的神经数据,此时恒河猴正在执行点对点的手臂伸展运动任务。我们使用神经工程框架将一种为预测手臂速度而开发的卡尔曼滤波器神经假体解码算法映射到SNN上,并使用一个免费的软件包对其进行模拟。一个20,000个神经元的网络将标准解码器的预测匹配到了0.03%以内(以最大手臂速度进行归一化)。这个网络的一个1,600个神经元版本的误差在0.27%以内,并且可以在一台3GHz的个人电脑上实时运行。这些结果表明,一个SNN可以实现一种广泛用作高性能神经假体解码器的统计信号处理算法(卡尔曼滤波器),并且仅用几千个神经元就能取得类似的结果。硬件SNN实现——神经形态芯片——可能会节省功耗,这对于实现完全可植入的皮层控制假体至关重要。