A new technique to optimize single neuron models using experimental spike train data

We propose a new method for fitting model parameters to the neural spike train obtained from an experimental neuron. Due to the uncertainty associated with measuring the accurate voltage in a noisy environment, it is essential to develop methods that rely solely on the interspike intervals (ISI). Existing techniques do not provide a smooth and continuous cost function and optimal estimation of model parameters is difficult. In this paper we formulate a new cost function using the spike times of the neuron and determine the analytical gradient with respect to the model parameters. The optimal parameters are calculated using gradient based optimization techniques. We first use data generated by models to establish the accuracy of our technique. We also optimize the model to fit an experimental spike train of a biological neuron. We are able to find the optimal parameter set using a hybrid algorithm which is a combination of the gradient descent method and global optimization techniques.

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