1 / 7

Neural Network Modeling Jean Carlson, Ted Brookings

Neural Network Modeling Jean Carlson, Ted Brookings. 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

cornelia
Download Presentation

Neural Network Modeling Jean Carlson, Ted Brookings

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


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

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

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

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

More Related