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Progressively introducing quantified biological complexity into a hippocampal CA3 model

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3 Author(s)
William B. Levy ; University of Virginia, Department of Neurosurgery, P.O. Box 800420, Charlottesville, VA 22908, USA ; Kai S. Chang ; Andrew G. Howe

Quantifying the performance of a cognitive-behavioral model on a temporal paradigm requires mapping time onto the computational cycles of the simulation. We present a family of four minimal models of the hippocampus CA-3 simulated at different time resolutions. Behavioral results from the hippocampally-dependent trace classical conditioning paradigm show that rabbits can learn to properly anticipate US presentation for a specific range of trace interval time periods. Therefore, our hippocampal model should successfully anticipate US presentation for the same specific range of trace interval durations. Each model attempts to learn two different trace interval lengths. The results reinforce prior findings where we map time into the computational cycles of a minimal model. Further, our results support the the following idea : as the time resolution of a simulation increases, an increasing number of biological processes must be explicitly modeled to maintain behavioral performance for a temporal paradigm.

Published in:

2009 International Joint Conference on Neural Networks

Date of Conference:

14-19 June 2009