1. SME Review - September 20, 2006 Neural Network Modeling Jean Carlson, Ted Brookings

2. Specific Project Objectives Determine the role of optimization, robustness, and trade-offs in the structure and behavior of neural networks. Investigate the role of “grid cells” in rat navigation Quantify potential information encoded by grid cells. Potential navigation strategies employing grid cells. The flow of information from dMEC cortical region to the hippocampus. Cause and governing of “place cell” formation in hippocampus.

3. We showed that the dMEC region of the rat cortex encodes position information in a manner analogous to a Residue Number System (RNS). Technical Advances This RNS encoding allows representation of position over vast scales with a small, biologically realistic number of neurons. Hafting et. al. Nature 436 (2005) dMEC neurons fire in a regular grid pattern

4. We developed a neural net simulation, and modeled the network connecting grid cells to the hippocampus of the rat Technical Advances

5. Hippocampus cells learn to integrate the various periodic signals from grid cells to identify the rats location. Technical Advances Multiple Inputs Cortex cell has broad firing pattern Hippocampus cell fires only when rat is in specific location

6. Highest Impact and Significance to the Army Gain understanding of the strategies employed by rats to navigate in novel, complicated environments Describe the roles of learning and network topology in processing of sense data in artificial neural networks.

7. Plan for Moving Forward Next Steps 1. Determine result of competition for influencing hippocampus between grid cells and more localized sensory input (e.g. vision, smell). 2. Investigate the role of network topology and synaptic learning rules in forming memorized sequences (i.e. navigational routes). 3. Model the effect of changes in the environment (e.g size, features) on hippocampal cell activity, and connect to existing experiment. Timeline 1. 6 months 2. 6 months 3. 12 months