Departments of Physiology and Ophthalmology and Visual Sciences, University of Wisconsin, Madison Medical School, Madison, Wisconsin 53706, USA.
J Neurosci. 2010 Feb 10;30(6):2340-55. doi: 10.1523/JNEUROSCI.1730-09.2010.
Brain regions involved in transforming sensory signals into movement commands are the likely sites where decisions are formed. Once formed, a decision must be read out from the activity of populations of neurons to produce a choice of action. How this occurs remains unresolved. We recorded from four superior colliculus neurons simultaneously while monkeys performed a target selection task. We implemented three models to gain insight into the computational principles underlying population coding of action selection. We compared the population vector average (PVA)/optimal linear estimator (OLE) and winner-takes-all (WTA) models and a Bayesian model, maximum a posteriori estimate (MAP), to determine which predicted choices most often. The probabilistic model predicted more trials correctly than both the WTA and the PVA. The MAP model predicted 81.88%, whereas WTA predicted 71.11% and PVA/OLE predicted the least number of trials at 55.71 and 69.47%. Recovering MAP estimates using simulated, nonuniform priors that correlated with monkeys' choice performance, improved the accuracy of the model by 2.88%. A dynamic analysis revealed that the MAP estimate evolved over time and the posterior probability of the saccade choice reached a maximum at the time of the saccade. MAP estimates also scaled with choice performance accuracy. Although there was overlap in the prediction abilities of all the models, we conclude that movement choice from populations of neurons may be best understood by considering frameworks based on probability.
大脑中负责将感觉信号转化为运动指令的区域，可能是决策形成的部位。一旦形成，决策就必须从神经元群体的活动中读出，以产生行动选择。这种情况如何发生仍未解决。当猴子执行目标选择任务时，我们同时记录了四个上丘神经元的活动。我们实施了三个模型，以深入了解动作选择的群体编码所依据的计算原则。我们比较了群体向量平均（PVA）/最优线性估计器（OLE）和胜者通吃（WTA）模型以及贝叶斯模型，最大后验估计（MAP），以确定哪个预测选择最准确。概率模型预测的正确试次比 WTA 和 PVA 都多。MAP 模型预测的准确率为 81.88%，而 WTA 预测的准确率为 71.11%，PVA/OLE 预测的准确率最低，分别为 55.71%和 69.47%。使用与猴子的选择表现相关的模拟非均匀先验恢复 MAP 估计，可将模型的准确性提高 2.88%。动态分析表明，MAP 估计值随时间演变，眼跳选择的后验概率在眼跳时达到最大值。MAP 估计值也与选择表现的准确性成正比。虽然所有模型的预测能力都有重叠，但我们得出结论，从神经元群体中进行运动选择可能最好通过考虑基于概率的框架来理解。
