Distributional value coding and reinforcement learning in the brain

大脑中的分布值编码和强化学习

• 批准号: 10539251
• 负责人: Adam Stanley Lowet
• 金额: $ 3.45万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10539251
• 关键词: Affect; Algorithms; Anatomy; Animals; Anxiety; Architecture; Behavior; Behavioral; Bernoulli Distribution; Bipolar Disorder; Brain; Brain region; Calcium; Cells; Code; Collaborations; Color; Complex; Corpus striatum structure; Dopamine; Dopamine D2 Receptor; Environment; Event; Future; Gambling; Grain; Heterogeneity; Image; Individual; Laboratories; Lead; Learning; Logic; Machine Learning; Measures; Mental Depression; Mental disorders; Modification; Molecular; Mus; Nature; Neurons; Odors; Outcome; Pattern; Performance; Population; Population Theory; Predictive Value; Probability; Psychological reinforcement; RL13; Resolution; Rewards; Role; Scheme; Shapes; Silicon; Stimulus; Structure; System; Tail; Testing; Ventral Striatum; Ventral Tegmental Area; Water; Work; addiction; burden of illness; cell type; classical conditioning; density; design; dopamine system; dopaminergic neuron; drug of abuse; improved; in vivo; in vivo two-photon imaging; maladaptive behavior; neural circuit; neuromechanism; optimism; receptor; relating to nervous system; theories; two photon microscopy; two-photon

ABSTRACT Making predictions about future rewards in the environment, and taking actions to obtain those rewards, is critical for survival. When these predictions are overly optimistic — for example, in the case of gambling addiction — or overly pessimistic — as in anxiety and depression — maladaptive behavior can result and present a significant disease burden. A fundamental challenge for making reward predictions is that the world is inherently stochastic, and events on the tails of a distribution need not reflect the average. Therefore, it may be useful to predict not only the mean, but also the complete probability distribution of upcoming rewards. Indeed, recent advances in machine learning have demonstrated that making this shift from the average reward to the complete reward distribution can dramatically improve performance in complex task domains. Despite its apparent complexity, such “distributional reinforcement learning” can be achieved computationally with a remarkably simple and biologically plausible learning rule. A recent study found that the structure of dopamine neuron activity may be consistent with distributional reinforcement learning, but it is unknown whether additional neuronal circuity is involved — most notably the ventral striatum (VS) and orbitofrontal cortex (OFC), both of which receive dopamine input and are thought to represent anticipated reward, also called “value”. Here, we propose to investigate whether value coding in these downstream regions is consistent with distributional reinforcement learning. In particular, we will record from these brain regions while mice perform classical conditioning with odors and water rewards. In the first task, we will hold the mean reward constant while changing the reward variance or higher- order moments, and ask whether neurons in the VS and OFC represent information over and above the mean, consistent with distributional reinforcement learning. In principle, this should enable us to decode the complete reward distribution purely from neural activity. In the second task, we will present mice with a panel of odors predicting the same reward amount with differing probabilities. The simplicity of these Bernoulli distributions will allow us to compare longstanding theories of population coding in the brain — that is, how probability distributions can be instantiated in neural activity to guide behavior. In addition to high-density silicon probe recordings, we will perform two-photon calcium imaging in these tasks to assess whether genetically and molecularly distinct subpopulations of neurons in the striatum contribute differentially to distributional reinforcement learning. Finally, we will combine these recordings with simultaneous imaging of dopamine dynamics in the striatum to ask how dopamine affects striatal activity in vivo. Together, these studies will help clarify dopamine’s role in learning distributions of reward, as well as its dysregulation in addiction, anxiety, depression, and bipolar disorder.

抽象的 对环境中的未来奖励做出预测，并采取行动获得这些奖励是至关重要的 生存。当这些预测过于乐观时（例如，在赌博成瘾的情况下）或 过于悲观（如焦虑和抑郁中）可能会导致不良适应性行为 伯恩疾病。做出奖励预测的一个基本挑战是，世界本质上是随机的， 分布尾部的事件不必反映平均值。因此，预测不预测可能很有用 只有平均值，但同时也是即将到来的奖励的完整概率分布。确实，最近的进步 机器学习已经表明，从平均奖励转变为完全奖励 分配可以显着改善复杂任务域中的性能。尽管显而易见 可以通过非常简单且非常简单和 生物学上合理的学习规则。最近的一项研究发现，多巴胺神经元活性的结构可能是 与分布强化学习一致，但是尚不清楚其他神经元电路是否为 涉及 - 最著名的是腹侧纹状体（VS）和眶额皮质（OFC），两者都接受多巴胺 输入并被认为代表预期的奖励，也称为“价值”。在这里，我们建议调查 这些下游区域中的价值编码是否与分布强化学习一致。在 特别是，我们将从这些大脑区域记录，而小鼠则用气味和水进行经典调节 奖励。在第一个任务中，我们将保持平均奖励常数，同时更改奖励差异或更高 订购时刻，并询问VS和OFC中的神经元是否代表信息以外的信息， 与分布强化学习一致。原则上，这应该使我们能够解码完整 纯粹来自神经活动的奖励分布。在第二个任务中，我们将向老鼠展示一个气味面板 通过不同的可能性预测相同的奖励金额。这些伯努利分布的简单性将 允许我们比较大脑中人口编码的长期理论，即概率分布方式 可以在神经活动中实例化以指导行为。除了高密度的硅探针记录外，我们 将在这些任务中执行两光子钙成像，以评估遗传和分子上是否不同 纹状体中神经元的亚群对分布强化学习的贡献不同。最后， 我们将将这些录音与纹状体中多巴胺动态的简单成像结合起来，以询问如何 多巴胺会影响体内纹状体活性。这些研究将共同​​阐明多巴胺在学习中的作用 奖励分布及其成瘾，焦虑，抑郁和躁郁症的失调。