Neural dynamics underlying rule-based decision-making

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决定许多因素中哪一个所需的内部产生的因素 可能的行为意外情况或规则应适用于不同的情况。这样一种能力 灵活地调用行为偶发事件是许多关键功能的基础，例如，认知调节 情感和上下文相关的决策。在灵长类动物中，WM的保持和操作 表征依赖于前额叶皮质(PFC)，而在人类中，PFC功能障碍与 有一系列精神疾病的症状，如精神分裂症的神经认知缺陷。神经元中的 在需要WM的行为中，PFC会产生持续的尖峰活动，这表明了一种机制，即 在没有刺激推动活动的情况下，记忆心理表征会随着时间的推移而保持不变。 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 尽管PFC神经元是专门化的，但实际上很大一部分PFC神经元表现出混合选择性：对记忆刺激特征的复杂混合做出异质和时变反应。非线性混合在理论上是服务于 通过实现高维表示在灵活认知中的关键作用，简单的线性读数 与神经元高度专门化相比，可以提取更多与任务相关的变量。如何既有学位又有 非线性度和表征的维度与认知等因素动态相关 需求或学习仍未得到开发。我建议评估这一命题，即非线性混合选择性神经元产生适合于高级认知的分布式、高维表征 为其调用它们的函数。这一假说断言，大量信息存在于种群水平的结构中，这在大量神经元的联合活动中将是显而易见的，在 一种执行认知要求高的任务的动物，要想成功完成该任务，就必须形成一种 高维表示。为了测试这一断言，我将使用长期植入的微电极阵列 在记录猴子的PFC时，并行记录多个单个单位的活动，而猴子则表现出延迟 匹配到样本(DMS)任务，其中匹配基于探头和样本的特征的结合 刺激物。因为匹配是基于合取的，所以可以使决策规则或多或少地变得复杂 因此将需要更高或更低维度的表示。我将研究维度是如何 表征和非线性混合程度与学习和任务绩效动态相关，并且 我将测试这样的假设，即决策规则的复杂性预测神经的维度 在任务执行过程中的代表性。然后我将检查是否有维度的 表征是任务成功完成的制约因素。最后，我将调查 在学习任务规则的过程中，神经表征的维度和非线性混合程度会发生变化。

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.