Abstract
We demonstrate a parameter-space search algorithm using a computational model of a single-compartment neuron with conductance-based Hodgkin–Huxley dynamics. To classify bursting (the desired behavior), we use a simple cost function whose inputs are derived from the frequency content of the neural output. Our method involves the repeated use of a stochastic gradient descent-type algorithm to locate parameter values that allow the neural model to produce bursting within a specified tolerance. We demonstrate good results, including those showing that the utility of our algorithm improves as the pre-defined allowable parameter ranges increase and that the initial approach to our method is computationally efficient.
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Reid, M.S., Brown, E.A. & DeWeerth, S.P. A parameter-space search algorithm tested on a Hodgkin–Huxley model. Biol Cybern 96, 625–634 (2007). https://doi.org/10.1007/s00422-007-0156-2
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DOI: https://doi.org/10.1007/s00422-007-0156-2