RI: Small: Generation and modulation of variability in trial and error learning

RI：小：试错学习中变异性的生成和调节

• 批准号: 1421245
• 负责人: Adrienne Fairhall
• 金额: $ 45.95万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1421245&HistoricalAwards=false
• 关键词: RI; Small; Generation; modulation; variability

Reinforcement learning is a powerful learning strategy in which random perturbations are made to an output and changes that lead to more successful outputs are reinforced. Animals learn to perform highly skilled tasks by trial and error, but how biological neural networks generate variability from trial to trial is not known. An ideal model for examining this relationship between variability and learning can be found in song learning by birds. Birds learn their songs by trial and error, and are able to adjust their songs into adulthood. In addition, the basic neural architecture of the learning circuit has been mapped out; some effects of behavioral context on variability are also known. How variability is generated, and how it is controlled, can thus be studied mechanistically.This project will study two candidate mechanisms for the generation and control of variability: chaotic dynamics in a cortex-like area, and dynamical modulation of synchrony in a basal ganglia circuit. While generic neural networks are chaotic, this project will explore how such dynamics are modified in a cortical network of biophysically plausible neurons with structured connectivity based on the known topography of the learning circuit. Furthermore, the birdsong cortical area receives inputs generated by basal ganglia. How might this input influence the variability of the output? It is proposed that the ability of activity in basal ganglia to affect cortex may depend on the synchrony of basal ganglia outputs. The studies will be constrained by experimental data from these candidate areas through collaborations with two laboratories. The work will contribute to a general understanding of variability in biological learning, and may suggest strategies for structuring noisy input during motor learning that can lead to efficient outcomes for robotics applications. Further, Dr Fairhall is developing materials for broad outreach programs that include a Massive Online Open Course to introduce concepts in computational neuroscience to a very diverse audience, and a workshop for middle school girls interested in mathematics applied to the life sciences.

强化学习是一种强大的学习策略，其中对输出进行随机扰动，并加强导致更成功输出的变化。动物通过试验和错误来学习执行高技能的任务，但生物神经网络如何在试验之间产生变异性尚不清楚。研究变异性和学习之间关系的理想模型可以在鸟类的鸣唱学习中找到。鸟类通过试验和错误来学习它们的歌曲，并且能够调整它们的歌曲到成年。此外，学习回路的基本神经结构已经被绘制出来;行为背景对可变性的一些影响也是已知的。因此，可以从机制上研究变异性是如何产生和控制的。本项目将研究变异性产生和控制的两种候选机制：类皮质区域的混沌动力学和基底神经节回路中同步性的动态调制。 虽然通用神经网络是混乱的，但该项目将探索如何在具有基于学习电路已知拓扑结构的结构化连接的生物物理学上合理的神经元的皮质网络中修改这种动态。此外，鸟鸣皮质区接收由基底神经节产生的输入。这种输入如何影响输出的可变性？基底神经节的活动影响皮层的能力可能取决于基底神经节输出的同步性。这些研究将通过与两个实验室的合作，受到来自这些候选领域的实验数据的限制。这项工作将有助于对生物学习中的变异性有一个全面的了解，并可能提出在运动学习过程中构建噪声输入的策略，从而为机器人应用带来有效的结果。此外，费尔霍尔博士正在为广泛的外展计划开发材料，其中包括一个大规模的在线开放课程，向各种各样的观众介绍计算神经科学的概念，并为对应用于生命科学的数学感兴趣的中学女生举办研讨会。