Department of Physiology, Centre for Applied Mathematics in Bioscience and Medicine, McGill University, Montreal, Quebec H3G 1Y6, Canada and
Department of Physiology, Centre for Applied Mathematics in Bioscience and Medicine, McGill University, Montreal, Quebec H3G 1Y6, Canada and.
J Neurosci. 2018 May 2;38(18):4399-4417. doi: 10.1523/JNEUROSCI.1182-17.2018. Epub 2018 Apr 6.
Spike-time correlations capture the short timescale covariance between the activity of neurons on a single trial. These correlations can significantly vary in magnitude and sign from trial to trial, and have been proposed to contribute to information encoding in visual cortex. While monkeys performed a motion-pulse detection task, we examined the behavioral impact of both the magnitude and sign of single-trial spike-time correlations between two nonoverlapping pools of middle temporal (MT) neurons. We applied three single-trial measures of spike-time correlation between our multiunit MT spike trains (Pearson's, absolute value of Pearson's, and mutual information), and examined the degree to which they predicted a subject's performance on a trial-by-trial basis. We found that on each trial, positive and negative spike-time correlations were almost equally likely, and, once the correlational sign was accounted for, all three measures were similarly predictive of behavior. Importantly, just before the behaviorally relevant motion pulse occurred, single-trial spike-time correlations were as predictive of the performance of the animal as single-trial firing rates. While firing rates were positively associated with behavioral outcomes, the presence of either strong positive or negative correlations had a detrimental effect on behavior. These correlations occurred on short timescales, and the strongest positive and negative correlations modulated behavioral performance by ∼9%, compared with trials with no correlations. We suggest a model where spike-time correlations are associated with a common noise source for the two MT pools, which in turn decreases the signal-to-noise ratio of the integrated signals that drive motion detection. Previous work has shown that spike-time correlations occurring on short timescales can affect the encoding of visual inputs. Although spike-time correlations significantly vary in both magnitude and sign across trials, their impact on trial-by-trial behavior is not fully understood. Using neural recordings from area MT (middle temporal) in monkeys performing a motion-detection task using a brief stimulus, we found that both positive and negative spike-time correlations predicted behavioral responses as well as firing rate on a trial-by-trial basis. We propose that strong positive and negative spike-time correlations decreased behavioral performance by reducing the signal-to-noise ratio of integrated MT neural signals.
尖峰时间相关捕获了在单个试验中单神经元活动之间的短时间尺度协方差。这些相关在幅度和符号上可以在试验间显著变化，并已被提出有助于视觉皮层中的信息编码。当猴子执行运动脉冲检测任务时，我们检查了两个不重叠的中间颞（MT）神经元池之间的单个试验尖峰时间相关的幅度和符号对行为的影响。我们应用了三种用于我们的多单位 MT 尖峰列车的单个试验尖峰时间相关测量（皮尔逊、皮尔逊绝对值和互信息），并检查了它们在逐次试验的基础上预测受试者表现的程度。我们发现，在每次试验中，正相关和负相关尖峰时间相关几乎同样可能，并且，一旦考虑了相关符号，所有三种测量都同样可以预测行为。重要的是，就在与行为相关的运动脉冲发生之前，单个试验尖峰时间相关与动物的表现一样具有预测性。虽然尖峰时间相关与行为结果呈正相关，但强正相关或负相关的存在对行为有不利影响。这些相关发生在短时间尺度上，最强的正相关和负相关通过与没有相关的试验相比，调制行为表现约 9%。我们提出了一个模型，其中尖峰时间相关与两个 MT 池的共同噪声源相关，这反过来又降低了驱动运动检测的积分信号的信噪比。以前的工作表明，短时间尺度上发生的尖峰时间相关可以影响视觉输入的编码。尽管尖峰时间相关在试验间在幅度和符号上都有很大变化，但它们对逐次试验行为的影响尚未完全了解。使用在猴子中记录的来自区域 MT（中间颞）的神经记录，使用短暂的刺激执行运动检测任务，我们发现正相关和负相关尖峰时间相关都可以预测行为反应以及逐次试验的尖峰时间相关。我们提出，强正相关和负相关尖峰时间相关通过降低集成 MT 神经信号的信噪比来降低行为表现。
