Neural dynamics underlying rule-based decision-making

灵活认知需要工作记忆（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产生的因素，这些因素决定了 可能的行为偶然性或规则应适用于不同的情况。这种能力去 灵活地调用行为偶然性是许多关键功能的基础，例如， 情绪和情境依赖的决策。在灵长类动物中，WM的保留和操纵 表征依赖于前额叶皮层（PFC），在人类中，PFC的功能障碍与 与精神疾病的一系列症状，如精神分裂症的神经认知缺陷。神经元 PFC在需要WM的行为中产生持续的尖峰活动，这表明了一种机制， 记忆心理表征在没有刺激来驱动活动的情况下跨时间维持。 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， 虽然PFC神经元是专门的，但大部分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的异质性和时变反应。非线性混合理论上是为了 在灵活认知中的关键作用，通过使高维表示，简单的线性读出， 可以提取更多与任务相关的变量，而不是高度专业化的神经元。如何既度 的非线性和表征的维度是动态相关的因素，如认知 需求或学习仍然很少被探索。我建议评估的命题，非线性混合选择性神经元引起分布式，高维表示适合更高的认知 它们被调用的函数。这一假说断言，大量的信息存在于群体水平的结构中，这在大量神经元的联合活动中是显而易见的，并且在一个神经元中最明显。 完成一项要求认知的任务的动物，成功完成这项任务需要形成一个 高维表示。为了验证这一论断，我将使用长期植入的微电极阵列， 在猴子的PFC中，平行记录许多单个单位的活动，而猴子则进行延迟的 匹配到样品（DMS）任务，其中匹配基于探针和样品的特征的结合 刺激。因为匹配是基于合取词的，所以可以使决策规则变得或多或少复杂 因此需要更高或更低维度的表示。我将研究 表示和非线性混合的程度与学习和任务性能动态相关，并且 我将检验一个假设，即决策规则的复杂性预测了神经网络的维数。 在执行任务的过程中。然后，我将检查是否维度的 表示是对成功执行任务的约束。最后，我将研究如何 神经表征的维度和非线性混合的程度在任务规则的学习期间演变。

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.