Prediction of single neural firings for Hodgkin-Huxley neuron by fitting generalized linear model

At the single neuron level, neural information processing involves the transformation of input stimulation into an output spike train. Here a generalized linear model (GLM) is used to reconstruct the mapping from stimulation to firing trains of single neuron for Hudgkin-Huxley (H-H) model. Firstly, H-H model is stimulated by the white noise to generate the input-output data samples used to construct GLM. Then, the parameters of GLM are estimated according to the maximum likelihood of the spike time serial of spike trains extracted from action potential of H-H. After that, the input-output mapping of spike trains evoked by white noise for H-H is successfully reconstructed. Through comparing the inter spike interval (ISI) and Pearson´s correlation coefficient, it also proves that the established GLM provides a good reproduction and prediction of the firing information for H-H. These studies provide us a new insight into coding processes and information tansfer of single neural.