Learning spatio-temporal statistics from the environment in recurrent networks

项目摘要摘要 学习新的任务和暴露在新的环境中会导致大脑回路动态的变化，如 在最近的各种实验中观察到的。将环境统计数据嵌入大脑的能力 回路对于动物在不断变化的环境中茁壮成长和生存是必不可少的。然而， 实现和学习电路动力学的机制没有被很好地理解，并提出 重大的理论挑战。最近在理论和实验实验室中的工作突出了 电路动力学的重要性。然而，在大多数理论模型中，网络连接要么不是塑料的，要么 遵守生物学上不可信的学习规则。在这里，我们将发展一种理论，说明大脑回路如何学习它们的 从环境的统计数据中得出动态。我们将通过一系列实验来支撑这项工作，以便 使其在生物学上逼真，并在范围上受到限制。在目标1中，我们将尝试了解网络如何学习 刺激奖励时空统计。这一目标将建立在电路级实验的基础上，这些实验表明 神经元动力学的变化是由于刺激之后的延迟奖赏，以及通过细胞实验 阐明了强化学习的机制。这是一个我们更了解的问题，也是 本质上比在无人监督的情况下学习环境统计数据更简单。在《目标2》中我们将 专注于大脑皮层回路学习时空的顺序而不是时间的实验 序列。在这样的网络中，学习序列的时序由固有的网络动态决定； 使这个问题比学习序列的顺序和定时更简单。在目标3中，我们开发了 可以学习时空序列的顺序和定时的网络和学习规则。这 努力将以目标2的结果为基础，其中以无人监督的方式了解事件的顺序，并 目标1，其中使用强化学习来学习事件的时间。

Project Summary Abstract Learning new tasks and exposure to new environments lead to changes in the dynamics of brain circuits, as observed in various recent experiments. The ability to embed the statistics of the environment within brain circuits is essential for animals ability to thrive and survive in changing environments. However, the mechanisms by which circuits dynamics are implemented and learned are not well understood, and pose significant theoretical challenges. Recent work in both theoretical and experimental labs has highlighted the importance of circuit dynamics. Yet in most theoretical models the network connectivity is either not plastic, or obeys biologically implausible learning rules. Here we will develop a theory of how brain circuits can learn their dynamics from the statistics of the environment. We will anchor this work in a set of experiments, in order to make it biologically realistic and limited in scope. In aim 1 we will try to understand how networks can learn stimulus-reward spatiotemporal statistics. This aim will be based on circuit level experiments that show how neuronal dynamics change due to a stimulus followed by a delayed reward, and by cellular experiments that shed light on the mechanisms of reinforcement learning. This is a problem we know more about, and it is also inherently simpler than learning the statistics of the environment in an unsupervised manner. In aim 2 we will concentrate on experiments on which cortical circuits learn the order, but not the timing, of a spatiotemporal sequence. In such networks the timing of the learned sequence are determined by intrinsic network dynamics; making this problem simpler than learning both the order and the timing of a sequence. In aim 3 we develop networks and learning rules that can learn both the order and the timing of a spatiotemporal sequence. This effort will build on results in aim 2 in which the order of events is learned in an unsupervised manner, and of aim 1 in which the timing of events is learned using reinforcement learning.