Robert H. Lee - US grants

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.

DESCRIPTION (provided by applicant): Motoneurons appear to have the ability to strongly amplify their own inputs based on neuromodulatory input from the brainstem. This amplification appears to preferentially effect fluctuating, rather than steady, synaptic input. In some input situations this effect results in what we refer to as "direct" mode firing. The objective of this project is to examine this amplification and to understand the role that a "fast" persistent inward current (PIC) plays in generating this amplification. The specific aims for this project are: 1) to quantify "fast" dendritic amplification of synaptic inputs; 2) to characterize direct mode firing; 3) to evaluate possible mechanisms for direct mode firing. In regards to the health relatedness of this project, this amplification may explain the profound weakness that occurs in cases of descending brainstem input disruption due to injury or disease. Overall, motoneuron research has been in a state of flux for several years as the effect of neuromodulators on motoneuron behavior has become increasingly apparent. The significance of this project is that it represents the culmination of this revolutionary period of change, bringing together much of the recent data along with the results anticipated from the work proposed here. In order to form this new view of motoneuron input processing based on steady inputs as well as input dynamics and neuromodulatory influences, the intended experimental approach utilizes a novel means of separating ionic currents based on their kinetics of activation. This approach permits instantaneous switching between measuring motoneuron firing and measuring ionic currents in intracellular in vivo experiments. Furthermore, voltage-clamp recording of currents generated by dynamic synaptic events can be examined for amplification and re-injected during current-clamp to determine their effect on the timing of individual spikes and firing rate in general.