神经回路中有效两阶段学习的规则和机制
Rules and mechanisms for efficient two-stage learning in neural circuits
摘要：
试错学习需要评估变量动作并强化成功的变量。在鸣禽中，发声探索是由LMAN引起的，LMAN是基底神经节回路的输出，也有助于对发声输出的校正偏差。这种偏见是逐步巩固RA，运动皮质类似物下游的LMAN。我们开发了一个新的模型，这样的两个阶段的学习。使用随机梯度下降，我们推导出“导师”电路中的活动（例如，LMAN）应该匹配“学生”电路中的可塑性机制（例如，RA）实现高效学习。我们进一步描述了一个强化学习框架，通过该框架，教师可以建立其教学信号。我们发现导师信号和可塑性机制之间的不匹配会损害学习。应用鸟鸣，我们的研究结果预测的时间结构的纠正偏差从LMAN在RA的可塑性规则。我们的框架可以预测性地应用于其他成对的大脑区域，显示两个阶段的学习。
英文摘要：
Trial-and-error learning requires evaluating variable actions and reinforcing successful variants. In songbirds, vocal exploration is induced by LMAN, the output of a basal ganglia-circuit that also contributes a corrective bias to the vocal output. This bias is gradually consolidated in RA, a motor cortex analogue downstream of LMAN. We develop a new model of such two-stage learning. Using stochastic gradient descent, we derive how the activity in 'tutor' circuits (e.g., LMAN) should match plasticity mechanisms in 'student' circuits (e.g., RA) to achieve efficient learning. We further describe a reinforcement learning framework through which the tutor can build its teaching signal. We show that mismatches between the tutor signal and the plasticity mechanism can impair learning. Applied to birdsong, our results predict the temporal structure of the corrective bias from LMAN given a plasticity rule in RA. Our framework can be applied predictively to other paired brain areas showing two-stage learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
