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

项目摘要 我们的首要目标是了解存储的记忆如何随着经验而变化。亲- 提出的工作建立在先前的研究基础上，表明记忆激活和学习之间存在U形关系- 因此，强烈的激活导致突触加强，适度的激活导致突触减弱， 没有激活导致突触强度没有变化。目前的补助金集中在以下方面的影响： 这种代表性变化的U形关系：学习不仅仅是让记忆更强大， 较弱-它也可以减少记忆之间的神经重叠（分化）或增加神经重叠 （整合）。这些神经变化可能对记忆提取产生深远的影响：减少重叠可以 减少干扰，以防止泛化为代价。我们的具体目标是构建和测试一个COM- 代表性变化的推定模型，以及它是如何由竞争性神经动力学塑造的。当我- 在能够自组织内部表征的神经网络中，我们的理论使 关于分化和整合何时发生的清晰、新颖的预测：记忆A的分化和 当（i）B在处理A时被适度激活，导致A与B之间的连接减弱时，将发生B。 B和A，以及（ii）B稍后被重新激活，允许其获取不与A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，从而导致B之间的连接加强 和A.目标1将使用神经网络模拟来解决文献中令人烦恼的难题，并生成 新的经验预测。Aim 2将使用行为和fMRI实验来测试这些预测， 学习海马体中的新关联，特别强调测试模型的预测能力， 关于竞争动态如何与代表性变化相关的问题。AIM 3将测试模型的预测 关于皮层可塑性，使用一种新的素描任务，诱导竞争之间的代表性， 熟悉的物体。表征的变化将在行为方面进行评估， 使用视觉皮层的功能磁共振成像进行神经学习的识别变化; 腹侧流将用于测量草图特征的变化。摘要：拟议的 研究使用多种创新方法（功能磁共振成像模式分析，神经网络建模，自由形式的对象， 素描和计算机视觉）来解决经验何时导致神经再现的根本问题。 我们的研究表明，神经可塑性是一种分化或整合的过程，从而推进了我们对神经可塑性的基本理解。改善 我们对神经分化的理解可能会对治疗认知缺陷产生变革性的影响 在广泛的临床条件下，包括中风，阅读障碍和痴呆症。在所有这些条件下，认知- 潜在缺陷可能源于表征的不充分分离。这项研究可能会带来更好的方法， 重新区分这些表征，并通过这一点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.