Averbeck Bruno B, Lee Daeyeol
Department of Brain and Cognitive Sciences and Center for Visual Science, University of Rochester, Rochester, New York 14627, USA.
J Neurosci. 2003 Aug 20;23(20):7630-41. doi: 10.1523/JNEUROSCI.23-20-07630.2003.
We analyzed the variability of spike counts and the coding capacity of simultaneously recorded pairs of neurons in the macaque supplementary motor area (SMA). We analyzed the mean-variance functions for single neurons, as well as signal and noise correlations between pairs of neurons. All three statistics showed a strong dependence on the bin width chosen for analysis. Changes in the correlation structure of single neuron spike trains over different bin sizes affected the mean-variance function, and signal and noise correlations between pairs of neurons were much smaller at small bin widths, increasing monotonically with the width of the bin. Analyses in the frequency domain showed that the noise between pairs of neurons, on average, was most strongly correlated at low frequencies, which explained the increase in noise correlation with increasing bin width. The coding performance was analyzed to determine whether the temporal precision of spike arrival times and the interactions within and between neurons could improve the prediction of the upcoming movement. We found that in approximately 62% of neuron pairs, the arrival times of spikes at a resolution between 66 and 40 msec carried more information than spike counts in a 200 msec bin. In addition, in 19% of neuron pairs, inclusion of within (11%)- or between-neuron (8%) correlations in spike trains improved decoding accuracy. These results suggest that in some SMA neurons elements of the spatiotemporal pattern of activity may be relevant for neural coding.
我们分析了猕猴辅助运动区（SMA）中神经元放电计数的变异性以及同时记录的神经元对的编码能力。我们分析了单个神经元的均值 - 方差函数，以及神经元对之间的信号和噪声相关性。所有这三个统计量都强烈依赖于为分析所选的时间间隔宽度。不同时间间隔大小下单神经元放电序列的相关结构变化会影响均值 - 方差函数，并且在小时间间隔宽度时神经元对之间的信号和噪声相关性要小得多，随着时间间隔宽度的增加而单调增加。频域分析表明，平均而言，神经元对之间的噪声在低频时相关性最强，这解释了噪声相关性随时间间隔宽度增加而增加的现象。我们分析了编码性能，以确定放电到达时间的时间精度以及神经元内部和之间的相互作用是否可以改善对即将发生运动的预测。我们发现，在大约62%的神经元对中，分辨率在66至40毫秒之间的放电到达时间比200毫秒时间间隔内的放电计数携带更多信息。此外，在19%的神经元对中，在放电序列中纳入神经元内（11%）或神经元间（8%）的相关性可提高解码准确性。这些结果表明，在一些SMA神经元中，活动的时空模式元素可能与神经编码相关。
