Key Laboratory for Image Processing and Intelligent Control of Education Ministry of China, Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, China.
Neural Comput. 2010 Apr;22(4):1060-85. doi: 10.1162/neco.2009.10-08-885.
Recent investigation of cortical coding and computation indicates that temporal coding is probably a more biologically plausible scheme used by neurons than the rate coding used commonly in most published work. We propose and demonstrate in this letter that spiking neural networks (SNN), consisting of spiking neurons that propagate information by the timing of spikes, are a better alternative to the coding scheme based on spike frequency (histogram) alone. The SNN model analyzes cortical neural spike trains directly without losing temporal information for generating more reliable motor command for cortically controlled prosthetics. In this letter, we compared the temporal pattern classification result from the SNN approach with results generated from firing-rate-based approaches: conventional artificial neural networks, support vector machines, and linear regression. The results show that the SNN algorithm can achieve higher classification accuracy and identify the spiking activity related to movement control earlier than the other methods. Both are desirable characteristics for fast neural information processing and reliable control command pattern recognition for neuroprosthetic applications.
最近对皮质编码和计算的研究表明，与大多数已发表工作中常用的比率编码相比，时间编码可能是神经元更具生物学合理性的方案。我们在这封信中提出并证明，由通过尖峰定时来传播信息的尖峰神经元组成的尖峰神经网络 (SNN) 是一种优于仅基于尖峰频率 (直方图) 的编码方案的替代方案。SNN 模型直接分析皮质神经尖峰序列，而不会丢失时间信息，从而为皮质控制的假肢生成更可靠的运动命令。在这封信中，我们将 SNN 方法的时间模式分类结果与基于发放率的方法（传统人工神经网络、支持向量机和线性回归）生成的结果进行了比较。结果表明，SNN 算法可以实现更高的分类准确性，并比其他方法更早地识别与运动控制相关的尖峰活动。这两个特性都是神经假肢应用中快速神经信息处理和可靠控制命令模式识别所需要的。
