Nature and precision of temporal coding in visual cortex: A metric-space analysis
Nature and precision of temporal coding in visual cortex: A metric-space analysis
复制标题
DOI：
10.1152/jn.1996.76.2.1310
复制
发表时间：
1996-08-01
影响因子：
2.5
通讯作者：
Purpura, KP
中科院分区：
文献类型：
--
作者：
Victor, JD;Purpura, KP
1. 我们在两只经过训练能执行注视任务的清醒猴子的V1、V2和V3的旁中心凹表征区域内的25个位点，记录了对纹理和光栅图案的瞬时呈现所产生的单单位和多单位活动。在光栅实验中，刺激在方向、空间频率或两者上有所不同。在纹理实验中，刺激在对比度、方格大小、纹理类型或这些属性的组合上有所不同。
2. 为了检验时间编码的性质和精度，我们根据两类度量标准比较了每组刺激所引发的个体反应。一类度量标准，D - 尖峰，对绝对尖峰时间（刺激开始后）敏感。第二类度量标准，D - 间隔，对尖峰间隔模式敏感。在每一类中，度量标准取决于一个参数q，它表示时间编码的精度。当q = 0时，这两种度量标准都归结为“尖峰计数”度量标准（D - 计数），它对脉冲数量敏感，但对其在时间上的位置不敏感。
3. 这些度量标准中的每一个（q值范围从0到512/秒）都被用于计算每个数据集中所有尖峰序列对之间的距离。这些成对距离中所体现的刺激特异性聚类程度通过一种信息度量来量化。通过将相同程序应用于将反应随机分配给输入刺激的合成数据集来估计随机聚类。
4. 在352个数据集中，170个通过尖峰计数（q = 0）度量标准显示出调谐的证据，294个通过尖峰时间度量标准显示出调谐的证据，272个通过尖峰间隔度量标准显示出对所研究的刺激属性（对比度、方格大小、方向、空间频率或纹理类型）调谐的证据。在整个数据集中，不归因于随机聚类的信息对于尖峰计数度量标准平均为0.042比特，对于最优尖峰时间度量标准为0.171比特，对于最优尖峰间隔度量标准为0.107比特。
5. 最优成本q的倒数可作为时间编码的时间精度的一种度量。在V1和V2中，对于两种度量标准，对比度的时间精度最高（约10 - 30毫秒），纹理类型的时间精度最低（约100毫秒）。q对刺激属性的这种系统性依赖为在一个尖峰序列中同时表征多个刺激属性提供了一种可能的机制。
6. 我们的发现与尖峰序列的泊松模型不一致。在其中放电率由与观察到的刺激后时间直方图（PSTH）匹配的时间相关泊松过程控制的合成数据集，高估了由D - 计数以及在q值较低时由D - 尖峰[q]和D - 间隔[q]所诱导的聚类。由一种改进的泊松过程构建的合成数据集，它不仅保留了PSTH，还保留了尖峰计数统计信息，解释了由D - 计数所诱导的聚类，但低估了由D - 尖峰[q]和D - 间 隔[q]所诱导的聚类。
1. We recorded single-unit and multi-unit activity in response to transient presentation of texture and grating patterns at 25 sites within the parafoveal representation of V1, V2, and V3 of two awake monkeys trained to perform a fixation task. In grating experiments, stimuli varied in orientation, spatial frequency, or both. In texture experiments, stimuli varied in contrast, check size, texture type, or pairs of these attributes.2. To examine the nature and precision of temporal coding, we compared individual responses elicited by each set of stimuli in terms of two families of metrics. One family of metrics, D-spike, was sensitive to the absolute spike time (following stimulus onset). The second family of metrics, D-interval, was sensitive to the pattern of interspike intervals. In each family, the metrics depend on a parameter q, which expresses the precision of temporal coding. For q = 0, both metrics collapse into the ''spike count'' metric (D-count), which is sensitive to the number of impulses but insensitive to their position in time.3. Each of these metrics, with values of q ranging from 0 to 512/s, was used to calculate the distance between all pairs of spike trains within each dataset. The extent of stimulus-specific clustering manifest in these pairwise distances was quantified by an information measure. Chance clustering was estimated by applying the same procedure to synthetic data sets in which responses were assigned randomly to the input stimuli.4. Of the 352 data sets, 170 showed evidence of tuning via the spike count (q = 0) metric, 294 showed evidence of tuning via the spike time metric, 272 showed evidence of tuning via the spike interval metric to the stimulus attribute (contrast, check size, orientation, spatial frequency, or texture type) under study. Across the entire dataset, the information not attributable to chance clustering averaged 0.042 bits for the spike count metric, 0.171 bits for the optimal spike time metric, and 0.107 bits for the optimal spike interval metric.5. The reciprocal of the optimal cost q serves as a measure of the temporal precision of temporal coding. In V1 and V2, with both metrics, temporal precision was highest for contrast (ca. 10-30 ms) and lowest for texture type (ca. 100 ms). This systematic dependence of q on stimulus attribute provides a possible mechanism for the simultaneous representation of multiple stimulus attributes in one spike train.6. Our findings are inconsistent with Poisson models of spike trains. Synthetic data sets in which firing rate was governed by a time-dependent Poisson process matched to the observed poststimulus time histogram (PSTH) overestimated clustering induced by D-count and, for low values of q, D-spiker[q] and D-interval[q]. Syn thetic data sets constructed from a modified Poisson process, which preserved not only the PSTH but also spike count statistics accounted for the clustering induced by D-count but underestimated the clustering induced by D-spiker[q] and D-interval[q].