Computational studies of decision-making and planning in the primate brain

大脑是如何做出行动决定的？一种假设认为，当我们看世界时，我们大脑的一部分根据当前可能的行动来处理感官信息。这些动作的神经表征然后相互竞争，直到选择一个来指导公开的表现。换句话说，行为是世界呈现给我们的行动需求和机会之间的一场持续的战斗。 在我的实验室里，我通过两种方法来研究这个广泛的假设。一方面，我利用多电极记录，功能成像和行为分析进行实验。另一方面，我开发了计算模型，描述了神经系统实现决策和规划的机制。这些模型通过数学方程来表达，这些数学方程被实现为模拟广泛的神经生理和行为现象的计算机程序。使用这些模型，我们可以尝试在机械水平上理解大脑的运作，甚至预测它在各种新情况下的行为。 在这个NSERC申请中，我请求资金支持我的计算建模研究计划。最近，我开发了一个数学模型，解释了灵长类动物决策和规划的各种神经和行为结果。该模型为计算神经科学的长期研究计划提供了起点。它将涉及一系列项目，包括：探索学习不同代表性参考框架之间感官信息转换的机制（例如从视觉坐标到运动坐标）;通过强化学习扩展模型以适应;并使模型具有发现任意刺激-反应关联规则的能力。希望这些计算项目将发展成为一个长期的研究计划，将导致对自愿行为背后的大脑机制的新见解。

How does the brain make decisions about actions? One hypothesis suggests that when we look at the world, part of our brain processes the sensory information in terms of the actions which are currently possible. Neural representations of these actions then compete against each other until one is chosen to guide overt performance. In other words, behavior is a constant battle between the demands and opportunities for action which the world presents to us.      In my laboratory, I investigate this broad hypothesis through two approaches. On one hand, I conduct experiments utilizing multi-electrode recording, functional imaging, and behavioral analysis. On the other, I develop computational models which describe the mechanisms by which the nervous system implements decision-making and planning. These models are expressed through mathematical equations which are implemented as computer programs that simulate a wide range of neurophysiological and behavioral phenomena. Using these models, we can try to understand brain operation at a mechanistic level, and even to predict how it should behave in various novel situations.      In this NSERC application, I am requesting funds to support my computational modeling research program. Recently, I have developed a mathematical model which explains a variety of neural and behavioral results on primate decision-making and planning. This model provides the starting point for a long-term research program in computational neuroscience. It will involve a series of projects, including: exploring the mechanisms for learning the transformation of sensory information between different representational reference frames (e.g. from visual coordinates to motor coordinates); extending the model to adapt through reinforcement learning; and giving the model the capacity to discover arbitrary stimulus-response association rules. It is hoped that these computational projects will evolve into a long-term research program that will lead to new insights into the brain mechanisms underlying voluntary behavior.