Spiking Neural Network Decoder for Brain-Machine Interfaces.
Spiking Neural Network Decoder for Brain-Machine Interfaces.
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DOI：
10.1109/ner.2011.5910570
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Boahen K
中科院分区：
文献类型：
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作者：
Dethier J;Gilja V;Nuyujukian P;Elassaad SA;Shenoy KV;Boahen K
当猕猴执行点对点到达手臂运动任务时，我们使用脉冲神经网络（SNN）来解码由96个电极阵列记录的运动前/运动皮层的神经数据。我们使用神经工程框架将卡尔曼滤波神经假肢解码算法映射到SNN上，该算法用于预测手臂的速度，并使用免费软件包Nengo进行模拟。一个20,000个神经元的网络与标准解码器的预测相匹配，误差在0.03%以内（按最大臂速归一化）。该网络的1600个神经元版本在0.27%以内，并在3GHz PC上实时运行。这些结果表明，SNN可以实现广泛用于高性能神经假体（卡尔曼滤波器）解码器的统计信号处理算法，并且仅用几千个神经元就可以获得类似的结果。硬件SNN实现——神经形态芯片——可能会节省电力，这对于实现完全可植入的皮质控制假肢至关重要。
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.