Biophysical vs reduced rate models for predicting retinal ganglion cell spike trains

Retinal ganglion cells (RGCs) respond to spatiotemporal patterns falling on photoreceptors by firing spike trains with an exquisitely precise temporal structure. Existing models of RGCs are reduced input-output models of light intensity or other features (eg contrast), but contain no biophysical parameters for a single RGC. These models, such as the stochastic integrate and fire (IF) and linear-nonlinear (LN) Poisson models are unable to account for the spike trains that are observed in actual data. The generalized linear model (GLM) is the most promising for reproducing the temporal structure of spike trains, even though it is not biophysically constrained. It would be an important result to generate a biophysical model of RGC spiking, and compare that model to one fit to the GLM to determine whether reduced models of that type are capable of capturing some spiking characteristics that full biophysical models contain. This paper analyzes some possible methods of comparison between these two types of models. It found that certain relations between elements of the two models reproduce actual retina data more faithfully.