Songnian Zhao, Xiaoyun Xiong, Guozheng Yao, Zhi Fu
LAPC, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China.
Neural Comput. 2003 Oct;15(10):2399-418. doi: 10.1162/089976603322362419.
Based on synchronized responses of neuronal populations in the visual cortex to external stimuli, we proposed a computational model consisting primarily of a neuronal phase-locked loop (NPLL) and multiscaled operator. The former reveals the function of synchronous oscillations in the visual cortex. Regardless of which of these patterns of the spike trains may be an average firing-rate code, a spike-timing code, or a rate-time code, the NPLL can decode original visual information from neuronal spike trains modulated with patterns of external stimuli, because a voltage-controlled oscillator (VCO), which is included in the NPLL, can precisely track neuronal spike trains and instantaneous variations, that is, VCO can make a copy of an external stimulus pattern. The latter, however, describes multi-scaled properties of visual information processing, but not merely edge and contour detection. In this study, in which we combined NPLL with a multiscaled operator and maximum likelihood estimation, we proved that the model, as a neurodecoder, implements optimum algorithm decoding visual information from neuronal spike trains at the system level. At the same time, the model also obtains increasingly important supports, which come from a series of experimental results of neurobiology on stimulus-specific neuronal oscillations or synchronized responses of the neuronal population in the visual cortex. In addition, the problem of how to describe visual acuity and multiresolution of vision by wavelet transform is also discussed. The results indicate that the model provides a deeper understanding of the role of synchronized responses in decoding visual information.
基于视觉皮层中神经元群体对外部刺激的同步反应，我们提出了一个主要由神经元锁相环（NPLL）和多尺度算子组成的计算模型。前者揭示了视觉皮层中同步振荡的功能。无论这些脉冲序列模式是平均发放率编码、脉冲时间编码还是发放率-时间编码，NPLL都可以从受外部刺激模式调制的神经元脉冲序列中解码出原始视觉信息，因为NPLL中包含的压控振荡器（VCO）可以精确跟踪神经元脉冲序列及其瞬时变化，也就是说，VCO可以复制外部刺激模式。然而，后者描述了视觉信息处理的多尺度特性，而不仅仅是边缘和轮廓检测。在本研究中，我们将NPLL与多尺度算子和最大似然估计相结合，证明了该模型作为一种神经解码器，在系统层面实现了从神经元脉冲序列中解码视觉信息的最优算法。同时，该模型也获得了越来越重要的支持，这些支持来自神经生物学关于视觉皮层中刺激特异性神经元振荡或神经元群体同步反应的一系列实验结果。此外，还讨论了如何通过小波变换描述视敏度和视觉多分辨率的问题。结果表明，该模型为深入理解同步反应在解码视觉信息中的作用提供了帮助。
