在幼稚猕猴中由有效控制的网络动力学支持的习惯学习
Habit learning supported by efficiently controlled network dynamics in naive macaque monkeys
摘要：
灵长类动物在不确定和动态的环境中表现出明显的学习习惯的能力。与这些习惯相关的感知和行为会涉及分布式神经回路。然而，这些回路究竟如何支持习惯学习所必需的计算，仍然远未被理解。在这里，我们构建了一个正式的网络能量学理论，以解释大脑状态的变化如何产生序列行为的变化。我们行使的背景下，跨尾状核，前额叶皮层，和前额叶的视野的雌性猕猴从事60-180届会议的自由扫描任务，诱导运动习惯的多单元录音的理论。该理论依赖于记录通道之间的有效连接的确定，以及大脑状态被认为是这些通道的试验特定放电率的规定。然后，该理论预测了从一种状态转换到另一种状态需要多少能量，因为活动只能通过有效连接传播。与理论的预测相一致，我们观察到较小的能量需求之间的过渡更相似，更复杂的审判眼跳模式，并为会议的特点是较少的熵选择眼跳模式。使用虚拟损伤的方法，我们证明了弹性的最小控制能量和行为之间的关系，以显着中断推断的有效连接。我们对习惯学习研究的理论原则性方法为未来研究行为如何从分布式神经回路的活动模式变化中产生铺平了道路。
英文摘要：
Primates display a marked ability to learn habits in uncertain and dynamic environments. The associated perceptions and actions of such habits engage distributed neural circuits. Yet, precisely how such circuits support the computations necessary for habit learning remain far from understood. Here we construct a formal theory of network energetics to account for how changes in brain state produce changes in sequential behavior. We exercise the theory in the context of multi-unit recordings spanning the caudate nucleus, prefrontal cortex, and frontal eyefields of female macaque monkeys engaged in 60-180 sessions of a free scan task that induces motor habits. The theory relies on the determination of effective connectivity between recording channels, and on the stipulation that a brain state is taken to be the trial-specific firing rate across those channels. The theory then predicts how much energy will be required to transition from one state into another, given the constraint that activity can spread solely through effective connections. Consistent with the theory's predictions, we observed smaller energy requirements for transitions between more similar and more complex trial saccade patterns, and for sessions characterized by less entropic selection of saccade patterns. Using a virtual lesioning approach, we demonstrate the resilience of the observed relationships between minimum control energy and behavior to significant disruptions in the inferred effective connectivity. Our theoretically principled approach to the study of habit learning paves the way for future efforts examining how behavior arises from changing patterns of activity in distributed neural circuitry.
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