Advancing neural modeling methods and technology

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DESCRIPTION (provided by applicant): Today it is possible to generate very complex computer models of both single neurons and neural circuits. This has allowed researchers to examine the dynamics of neurons in ways not before possible. These models have been instrumental in advancing our understanding of the human nervous system at all levels. However, managing the ever-increasing model complexity has been problematic as it can easily outstrip our ability to meaningfully comprehend all of the intricacies the model represents. In effect, the advancements in computer technology have out-paced the advancements in the methods by which models are developed and analyzed. Our long-term goal is the development of the methods, technology, and infrastructure necessary to automate the neural model generation process. The objective of this research project is to focus on model characterization and simulation technology. The rationale for this project is that a rigorous model characterization process tailored to exploit the ever-increasing power of computing hardware enables detailed comparisons of experimental data and model alternatives, manageable forward progression of model complexity, and dramatically shorter model development times than are now possible. The project will pursue the following three aims: 1. Characterize neural models, where the objective is to exploit the changing dimensionality from model input to model output to improve the process of both parameter estimation and parameter sensitivity analysis. 2. Quantify Model and Simulator Robustness, where the objective is to examine the effect of model variation on model output. 3. Advance mainstream simulation platform technology, where the objective is to develop optimal hardware platforms for simulating neuron models. It is our expectation that the resulting set of analytical tools and simulation platform recommendations will be applicable to all types of neural models, will increase our understanding of these models, and decrease the time necessary to construct the models themselves.