Computational Neural and Behavioral Studies of Competition-Dependent Learning

描述(申请人提供)：这项研究的主要目标是了解神经学习规则：什么情况下会导致记忆的加强和减弱？我们的具体目标是评估学习依赖于竞争的假设。根据这一假设，每当神经表征竞争活跃时，获胜的表征被加强，失败的表征被削弱，学习受到胜利幅度的调节(即竞争越近，学习越多)。目前的研究建立在以前资助的工作的基础上，其中基于生物学的计算模型被用来探索依赖竞争的学习的神经机制。这些模拟表明，竞争依赖型学习可以通过利用神经抑制强度的振荡在大脑中实现；它们还证明了竞争依赖型学习可以解释人类记忆数据的详细模式。这项研究的第一个具体目标是完善我们的竞争依赖型学习的计算模型。具体地说，模拟将侧重于在受追捧的记忆和其他记忆之间存在激烈竞争的范式。在这种情况下，竞争依赖学习假说预测学习效果将高度不稳定--记忆激发的微小差异会影响竞争的结果，这反过来又会影响哪些记忆得到加强，哪些被削弱。计算模型将被扩展到包括更现实的抑制形式，更详细的生物物理海马体模型，以及一个认知控制系统，该系统允许该模型回忆起人们渴望的记忆，即使它比竞争对手弱。这些变化应该会提高我们在这些高波动性情况下预测行为遗忘效应和神经活动模式的能力。本研究的第二个具体目的是开发和利用新的实验方法来检验关于竞争依赖学习的假设。测试竞争依赖学习假说是困难的，因为(根据该模型)学习效果取决于竞争记忆的准确激发水平。为了解决这个问题，拟议的研究将使用高度敏感的模式分类器算法，应用于大脑成像数据，以跟踪逐个试验的记忆竞争程度。这种竞争记忆的神经读数可以用来测试模型关于竞争如何推动学习的预测。这项拟议的研究将检验该模型在知觉启动范式、短期记忆范式和长期配对-联想学习范式中对记忆减弱的预测。关于心理健康：这项研究将提高我们对触发记忆加强和减弱的环境的理解，并将提高我们(基于神经活动)检测何时会发生记忆加强或减弱的能力。这些发展将帮助我们设计出更好的方法来加强令人满意的记忆联系，也可以设计出削弱适应不良记忆的方法(例如，在治疗恐惧症或创伤后应激障碍时)。与公共健康相关：这项研究将提高我们对触发记忆加强和减弱的环境的理解，并将提高我们(基于神经活动)检测何时会发生记忆加强或减弱的能力。这些发展将帮助我们设计出更好的方法来加强令人满意的记忆联系，也可以设计出削弱适应不良记忆的方法(例如，在治疗恐惧症或创伤后应激障碍时)。

DESCRIPTION (provided by applicant): The overarching goal of this research is to understand neural learning rules: What are the circumstances that give rise to strengthening and weakening of memories? Our specific goal is to evaluate the hypothesis that learning is competition-dependent. According to this hypothesis, whenever neural representations compete to be active, the winning representations are strengthened, the losing representations are weakened, and learning is modulated by the margin of victory (i.e., closer competition yields more learning). The present research builds on previously funded work where biologically-based computational models were used to explore the neural mechanisms of competition- dependent learning. These simulations demonstrated that competition-dependent learning could be implemented in the brain by leveraging oscillations in the strength of neural inhibition; they also demonstrated that competition- dependent learning could account for detailed patterns of human memory data. The first specific aim of this research is to refine our computational model of competition-dependent learning. In particular, the simulations will focus on paradigms where there is close competition between the sought-after memory and other memories. In situations of this sort, the competition-dependent learning hypothesis predicts that learning effects will be highly volatile - small differences in memory excitation can affect the outcome of the competition, which (in turn) will affect which memories are strengthened and which are weakened. The computational model will be extended to include a more realistic form of inhibition, a more biophysically detailed hippocampal model, and a cognitive control system that allows the model to recall the sought-after memory even when it is weaker than competitors. These changes should improve our ability to predict behavioral forgetting effects and neural activity patterns in these high-volatility situations. The second specific aim of this research is to develop and utilize new experimental methods for testing hypotheses about competition-dependent learning. Testing the competition-dependent learning hypothesis is difficult because (according to the model) learning effects depend on the precise level of excitation of the competing memories. To address this problem, the proposed studies will use highly sensitive pattern classifier algorithms, applied to brain imaging data, to track the extent to which memories compete on a trial-by-trial basis. This neural readout of the competing memories can be used to test the model's predictions about how competition drives learning. The proposed studies will test the model's predictions about memory weakening in a perceptual priming paradigm, a short-term memory paradigm, and a long-term paired-associate learning paradigm. With regard to mental health: This research will improve our understanding of the circumstances that trigger strengthening and weakening of memories, and it will improve our ability to detect (based on neural activity) when strengthening or weakening will occur. These developments will help us to devise better methods for strengthening desirable memory associations and also methods for weakening maladaptive memories (e.g., when treating phobias or post-traumatic stress disorder). PUBLIC HEALTH RELEVANCE: This research will improve our understanding of the circumstances that trigger strengthening and weakening of memories, and it will improve our ability to detect (based on neural activity) when strengthening or weakening will occur. These developments will help us to devise better methods for strengthening desirable memory associations and also methods for weakening maladaptive memories (e.g., when treating phobias or post-traumatic stress disorder).