EAGER: Collaborative Research: Non-Local Cortical Computation and Enhanced Learning with Astrocytes

大脑由两种主要细胞类型组成：神经元和神经胶质细胞。神经胶质细胞传统上被认为是大脑的支持细胞。然而，过去十年的许多工作已经证明，神经胶质细胞也可能参与复杂的神经过程，从而构成高级认知功能的一个组成部分，例如工作记忆、学习和睡眠。其他工作表明，人类星形胶质细胞比啮齿动物大脑中的星形胶质细胞更大，结构更复杂。为了支持这一概念，将人类神经胶质细胞移植到小鼠体内，产生了学习速度更快、在记忆测试中表现更好的小鼠。 然而，用于理解学习和记忆所涉及过程的现有计算建模技术不包括神经胶质细胞。拟议研究的目的是：1）开发包含神经胶质细胞的计算建模技术。 2）使用这些新颖的计算建模技术来预测神经胶质细胞在学习和记忆中的作用。 3) 使用膜片钳和 Ca2 成像相结合来测试预测。 4）利用收集到的数据不断完善计算建模技术。该提案的更广泛影响将是促进对学习和记忆的未被充分重视但必不可少的基础的科学理解。 除了神经元之外，在建模方法中还包括神经胶质细胞，除了可以提高学习障碍或其他使人衰弱的神经系统疾病个体的生物集成假体的性能之外，还有望提高自适应学习技术的计算能力和处理能力。

The brain is composed of two major cell types: Neurons and glial cells. Glial cells are traditionally regarded as the brain's supportive cells. However, many lines of work over the past decade have documented that glial cells may also participate in complex neural processes and thereby comprise an integral element of higher cognitive function, such as working memory, learning, and sleep. Other lines of work have shown that human astrocytes are larger and structurally more complex than astrocytes in the rodent brain. In support of this concept, transplantation of human glial cells into mice resulted in generation of mice that were faster learners and performed better on memory tests. However, existing computational modeling techniques employed for understanding the processes involved in learning and memory do not include glial cells. The aim of the proposed research is to: 1) Develop computational modeling techniques that incorporate glial cells. 2) Use these novel computational modeling techniques to make predictions regarding the role of glial cells in learning and memory. 3) Test the predictions using a combination of patch clamping and Ca2+ imaging. 4) Use the data collected to continuously refine the computational modeling techniques. The broader impact of this proposal will be to further the scientific understanding of underappreciated, yet essential substrates of learning and memory. Including glial cells in addition to neurons in modeling approaches additionally carries the hope of increasing computational power and processing capabilities of adaptive learning technology, in addition to improving the performance of bio-integrated prostheses for individuals with impaired learning or other debilitating neurological disorders.