Computational, Neural, and Behavioral Studies of Competition-Dependent Learning

项目总结 我们的首要目标是了解储存的记忆如何随着经验的变化而变化。支持- 提出的研究建立在先前的研究基础上，该研究表明记忆激活和学习之间存在U型关系。 ING，强激活导致突触加强，适度激活导致突触减弱- ING，NO的激活不会导致突触强度的改变。目前的赠款侧重于以下方面的影响 这种代表变化的U型关系：学习不仅仅是让记忆变得更强大或 较弱-它还可以减少记忆(分化)之间的神经重叠或增加神经重叠 (整合)。这些神经变化可以对记忆提取产生深远的影响：重叠减少可以 减少干扰，但代价是防止泛化。我们的具体目标是构建和测试一个COM- 表象变化的假设模型以及竞争性神经动力学如何塑造它。当我- 在能够自组织内部表示的神经网络中实现，我们的理论使 关于何时发生分化和整合的清晰、新颖的预测：记忆A和记忆的分化 B将在以下情况下发生：(I)在处理A时B被适度激活，导致 B和A，以及(Ii)B稍后被重新激活，允许它获得与A不重叠的新特征； 相反，如果B在A期间被强烈激活，则会发生整合，从而加强B之间的连接 和A.Aim 1将使用神经网络模拟来解决文献中令人烦恼的谜题并生成 新奇的经验预测。Aim 2将使用行为和功能磁共振实验来测试这些预测 学习海马体中的新联系，特别强调测试模型的预测- 关于竞争动态如何与代表性变化相关的观点。目标3将测试模型的预测 关于皮质可塑性，使用了一种新的素描任务，该任务在不同的表示之间引发了竞争 熟悉的物体。表征变化将根据草图和对象如何在行为上进行评估 视觉皮层功能磁共振成像在学习和神经学上的识别变化；一个深度神经网络模型 腹侧流将被用来测量草图特征的变化。总结：建议的 研究使用多种创新方法(fMRI模式分析、神经网络建模、自由形状对象 草图和计算机视觉)，以解决经验何时导致神经表示的根本问题 句子来区分或整合，从而促进我们对神经可塑性的基本理解。改进 我们对神经分化的理解可能会对治疗认知缺陷产生革命性的影响 在广泛的临床条件下，包括中风、阅读障碍和痴呆症。在所有这些情况下，干邑- 表象分离不充分，可能会产生动因赤字。这项研究可能会带来更好的方法 重新区分这些表征，并通过此来改善相关的认知缺陷。

PROJECT SUMMARY Our overarching goal is to understanding how stored memories change as a function of experience. The pro- posed work builds on prior research showing a U-shaped relationship between memory activation and learn- ing, whereby strong activation leads to synaptic strengthening, moderate activation leads to synaptic weaken- ing, and no activation leads to no change in synaptic strength. The present grant focuses on the implications of this U-shaped relationship for representational change: Learning is not just about making memories stronger or weaker—it can also decrease neural overlap between memories (differentiation) or increase neural overlap (integration). These neural changes can have profound effects on memory retrieval: Decreased overlap can reduce interference, at the cost of preventing generalization. Our specific goal is to construct and test a com- putational model of representational change and how it is shaped by competitive neural dynamics. When im- plemented in neural networks that are capable of self-organizing internal representations, our theory makes clear, novel predictions about when differentiation and integration will occur: Differentiation of memories A and B will occur when (i) B is moderately activated while processing A, causing weakening of connections between B and A, and (ii) B is reactivated later, allowing it to acquire new features that do not overlap with A; by con- trast, integration will occur if B is strongly activated during A, causing strengthening of connections between B and A. Aim 1 will use neural network simulations to address vexing puzzles in the literature and to generate novel empirical predictions. Aim 2 will test these predictions using behavioral and fMRI experiments focused on learning of new associations in the hippocampus, with a particular emphasis on testing the model's predic- tions about how competitive dynamics relate to representational change. Aim 3 will test the model's predictions regarding cortical plasticity, using a novel sketching task that induces competition between representations of familiar objects. Representational change will be assessed behaviorally in terms of how sketches and object recognition change over learning and neurally using fMRI of visual cortex; a deep neural network model of the ventral stream will be used to measure changes in the features of sketches. In summary: The proposed studies use multiple innovative approaches (fMRI pattern analysis, neural network modeling, free-form object sketching, and computer vision) to address the fundamental question of when experience causes neural repre- sentations to differentiate or integrate, thereby advancing our basic understanding of neuroplasticity. Improving our understanding of neural differentiation could have transformative implications for treating cognitive deficits in a wide range of clinical conditions, including stroke, dyslexia, and dementia. In all of these conditions, cogni- tive deficits can arise from insufficient separation of representations. This research may lead to better ways of re-differentiating these representations and—through this—ameliorating the associated cognitive deficits.