Princeton Neuroscience Institute, Princeton University, Green Hall, Princeton, NJ 08540, USA.
J Neurosci Methods. 2011 Sep 30;201(1):251-61. doi: 10.1016/j.jneumeth.2011.06.028. Epub 2011 Jul 18.
A central goal of neuroscience is to understand how neural dynamics bring about the dynamics of behavior. However, neural and behavioral measures are noisy, requiring averaging over trials and subjects. Unfortunately, averaging can obscure the very dynamics that we are interested in, masking abrupt changes and artificially creating gradual processes. We develop a hidden semi-Markov model for precisely characterizing dynamic processes and their alteration due to experimental manipulations. This method takes advantage of multiple trials and subjects without compromising the information available in individual events within a trial. We apply our model to studying the effects of motivation on response rates, analyzing data from hungry and sated rats trained to press a lever to obtain food rewards on a free-operant schedule. Our method can accurately account for punctate changes in the rate of responding and for sequential dependencies between responses. It is ideal for inferring the statistics of underlying response rates and the probability of switching from one response rate to another. Using the model, we show that hungry rats have more distinct behavioral states that are characterized by high rates of responding and they spend more time in these high-press-rate states. Moreover, hungry rats spend less time in, and have fewer distinct states that are characterized by a lack of responding (Waiting/Eating states). These results demonstrate the utility of our analysis method, and provide a precise quantification of the effects of motivation on response rates.
神经科学的一个核心目标是了解神经动力学如何产生行为的动力学。然而，神经和行为测量是有噪声的，需要在试验和主体上进行平均。不幸的是，平均化可能会掩盖我们感兴趣的动态，掩盖突然的变化，并人为地产生渐进的过程。我们开发了一种隐藏的半马尔可夫模型，用于精确描述动态过程及其由于实验操作而发生的变化。这种方法利用了多个试验和主体，而不会损害试验中单个事件的可用信息。我们将我们的模型应用于研究动机对反应率的影响，分析了饥饿和饱腹的大鼠的数据，这些大鼠接受了训练，以便在自由操作程序中按压杠杆以获得食物奖励。我们的方法可以准确地解释反应率的点状变化，以及反应之间的顺序依赖关系。它非常适合推断潜在反应率的统计数据以及从一种反应率转换到另一种反应率的概率。使用该模型，我们表明饥饿的大鼠具有更多独特的行为状态，其特征是反应率高，并且它们在这些高按压率状态下花费更多的时间。此外，饥饿的大鼠花费更少的时间，并且具有较少独特的状态，其特征是缺乏反应（等待/进食状态）。这些结果证明了我们的分析方法的实用性，并提供了对动机对反应率的影响的精确量化。
