The hippocampus in mammals is a brain area important for learning and memory that is also implicated in several major neurological and psychiatric disorders including epilepsy, autism, schizophrenia and Alzheimer's disease. Prior research revealed that learning and memory-related information processing in the hippocampus critically depends on clock-like, rhythmic electrical signals elicited by an adjacent brain structure called the septum. However, how the oscillatory neuronal activity emerges from the closely interconnected neuronal networks of the septum and the hippocampus is not understood. The current project will employ novel, uniquely high-throughput experimental approaches in mice integrated with innovative, biologically realistic, large-scale supercomputer-based theoretical modeling techniques to identify key mechanisms underlying rhythmic electrical signal generation in septo-hippocampal circuits. The project represents a potentially major milestone in the development of biologically based realistic computer models of the brain, as it would allow the generation and testing of hypotheses concerning network mechanisms of behaviorally relevant oscillatory neuronal activity with unparalleled biological realism, precision and speed. In addition, the experimental knowledge base and the large-scale computational brain models will be placed in open-source databases (including ModelDB for network models at http://senselab.med.yale.edu/modeldb/, and Neuromorpho for biological structural data at http://neuromorpho.org/) that are freely accessible for the unrestricted use by the scientific community. This project involves extensive postdoctoral training in neuroinformatics.
This award is co-funded by NSF's Office of the Director, International Science and Engineering, and a companion project is being funded by the French National Research Agency (ANR).
