Neural dynamics underlying rule-based decision-making

基于规则的决策的神经动力学

• 批准号: 10630933
• 负责人: Lee Phipps Lovejoy
• 金额: $ 19.76万
• 依托单位: NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-14 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10630933
• 关键词: Advanced Development; Animals; Behavior; Behavioral; Chronic; Classification; Cognition; Cognitive; Complex; Complex Mixtures; Decision Making; Dimensions; Exhibits; Failure; Functional disorder; Human; Impaired cognition; Implant; Implanted Electrodes; Individual; Intervention; Investigation; Joints; Learning; Mental disorders; Microelectrodes; Molecular; Monkeys; Neurocognitive Deficit; Neurons; Pattern; Performance; Population; Prefrontal Cortex; Primates; Process; Psychiatric therapeutic procedure; Psychotic Disorders; Role; Sampling; Schizophrenia; Short-Term Memory; Stimulus; Structure; Symptoms; Task Performances; Testing; Time; Translating; Work; cognitive function; cognitive reappraisal; dimensional analysis; emotion regulation; flexibility; high dimensionality; member; mental representation; neural; response; sensory input; theories; vector

Flexible cognition requires working memory (WM), the ability to form and manipulate mental representations. The contents of working memory include internally-generated factors required to determine which of many possible behavioral contingencies, or rules, should be applied under varying circumstances. Such an ability to flexibly invoke behavioral contingencies underlies many crucial functions, for example the cognitive regulation of emotion and context-dependent decision-making. In primates, the retention and manipulation of WM representations depends on the prefrontal cortex (PFC), and in humans, dysfunction of the PFC is associated with range of symptoms in psychiatric illness such as the neurocognitive deficits in schizophrenia. Neurons in PFC produce persistent spiking activity during behaviors that require WM, suggesting a mechanism by which mnemonic mental representations are maintained across time in the absence of a stimulus to drive activity. Although many PFC neurons respond most vigorously during WM of a specific stimulus feature to which they are specialized, a large proportion of PFC neurons actually exhibit mixed selectivity: heterogenous and time-varying responses to complex mixtures of remembered stimulus features. Nonlinear mixing is theorized to serve a pivotal role in flexible cognition by enabling high-dimensional representations from which simple linear readouts can extract many more task-related variables than if the neurons were highly specialized. How both the degree of nonlinearity and the dimensionality of representations are dynamically related to factors such as cognitive demand or learning remains larely unexplored. I propose to evaluate the proposition that nonlinear mixed-selectivity neurons give rise to distributed, high-dimensional representations suited to the higher cognitive functions for which they are invoked. This hypothesis asserts that substantial information exists in population-level structure which would be evident in the joint activity of a large number of neurons, and most apparent in an animal performing a cognitively demanding task for which successful completion necessitates formation of a high-dimensional representation. To test this assertion, I will use arrays of microelectrodes chronically implanted in the PFC of monkeys to record, in parallel, the activity of many single units while monkeys perform a delayed match to sample (DMS) task in which matches are based on conjunctions of features of the probe and sample stimuli. Because the matches are based on conjunctions, the decision rule can be made more or less complex and hence would require a representation of higher or lower dimension. I will examine how dimensionality of representations and the degree of nonlinear mixing is dynamically related to learning and task performance, and I will test the hypothesis that the complexity of the decision rule predicts the dimensionality of a neural representation during performance of the task. I will then examine whether or not the dimensionality of the representation is a constraint on successful performance of the task. Finally, I will investigate how the dimensionality of neural representations and the degree of nonlinear mixing evolves during learning of task rules.

灵活的认知需要工作记忆（WM），即形成和操纵心理表征的能力。 工作记忆的内容包括内部生成的因素，这些因素需要确定许多因素中的哪一个 可能的行为意外事件或规则应在不同的情况下适用。有这样的能力 灵活地调用行为偶然事件是许多关键功能的基础，例如认知调节 情绪和情境相关的决策。在灵长类动物中，WM 的保留和操纵 表征取决于前额叶皮层 (PFC)，在人类中，PFC 的功能障碍与之相关 具有一系列精神疾病症状，例如精神分裂症的神经认知缺陷。神经元在 PFC 在需要 WM 的行为期间产生持续的尖峰活动，这表明了一种机制 在没有刺激驱动活动的情况下，记忆心理表征会随着时间的推移而保持。 尽管许多 PFC 神经元在 WM 期间对特定刺激特征的反应最强烈，但它们 由于 PFC 神经元是专门化的，所以很大一部分 PFC 神经元实际上表现出混合选择性：对记忆刺激特征的复杂混合物做出异质且随时间变化的反应。非线性混合理论上可以服务 通过实现简单的线性读数的高维表示，在灵活认知中发挥关键作用 与高度专业化的神经元相比，可以提取更多与任务相关的变量。两者学位如何 非线性和表示的维度与认知等因素动态相关 需求或学习几乎未被探索。我建议评估这样一个命题：非线性混合选择性神经元产生适合更高认知能力的分布式、高维表示。 调用它们的函数。该假设断言，群体水平结构中存在大量信息，这在大量神经元的联合活动中很明显，并且在 动物执行一项认知要求较高的任务，成功完成该任务需要形成 高维表示。为了验证这一说法，我将使用长期植入的微电极阵列 在猴子的前额皮质中并行记录许多单个单元的活动，同时猴子执行延迟 样本匹配 (DMS) 任务，其中匹配基于探针和样本特征的结合 刺激。因为匹配是基于连词的，所以决策规则可以变得或多或少复杂 因此需要更高或更低维度的表示。我将研究维数如何 非线性混合的表示和程度与学习和任务表现动态相关，并且 我将测试以下假设：决策规则的复杂性可以预测神经网络的维数 执行任务期间的代表。然后我将检查是否有维数 代表性是成功执行任务的限制。最后，我将研究如何 神经表示的维度和非线性混合的程度在任务规则的学习过程中不断变化。