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.
