Efficient current-based optimization techniques for parameter estimation in multi-compartment neuronal models

Estimation of the maximal ion channel conductances in Hodgkin-Huxley models from patch clamp data is a difficult optimization problem. Automating this process promises high-throughput computational modeling of use to both experimenters (for rapid feedback on their experimental preparations) and modelers (for investigating the details of neuronal function). Hitherto, attempts to do this have focused on stochastic searches such as genetic algorithms and simulated annealing [1-3]. Such methods give robust estimates of model parameters but converge slowly or need to sample a large population of test cases in parallel, and therefore require substantial computing resources. Meanwhile, deterministic searches (e.g. the simplex search and conjugate gradient descent) are far more computationally efficient but are hampered by the complex fitting landscape of the optimization problem. As such, there is no general neuronal parameter-fitting algorithm that is both computationally efficient and robust.