Modeling
- Experiments are complemented with computational models. It is very difficult to accurately predict the outcomes of experimental manipulations when working on a complex non-linear dynamic system like a spiking neuron with voltage-dependent conductances. Our goal is not to generate models for their own sake, although high quality computational representations of real neurons may be useful in several ways, but to use the models to provide a formal and quantitative test situation to evaluate hypotheses and determine where our understanding is weak. The comparison of these models to equivalent experiments allows us to make further predictions and develop new hypotheses; the failures of the models drive us to seek new information from experiments and lead to refinements of the models. The strength of this approach is that we can combine experimental and theoretical work in a single laboratory, which both shortens development time and allows the incorporation of many specific experimental measurements that may not necessarily be available from the literature. This approach results in fairly highly constrained models, closely tied to biophysics, and with known strengths and weaknesses with respect to the data set they represent.
- Models are generated in NEURON, MATLAB, or C.
- Models of ventral cochlear neurons and dorsal cochlear nucleus pyramidal cells have been translated into NEURON, and deposited with the Neuron Model Database.
- Ventral cochlear nucleus bushy and stellate cell models.
- Dorsal cochlear nucleus pyramidal cell models.