A Minimal-Model Approach to Analyze Neuronal Circuit Dynamics from multifocal ERG (mERG)

The multifocal electroretinogram (mERG) is a valuable non-invasive tool used by clinicians for studying both the local physiology of the normal human retina, as well as retinal function affected by disease. However, the neural basis of the major components of the mERG is not well understood. Computational modeling of neural circuits has lead to important insights into the structure and workings of nervous systems. A better understanding of the different neural factors that contribute to the retina function and progression of the pathology can help in developing an effective clinical intervention. In this paper, we propose a minimal computational model as an analysis tool for inferring the relationship between mERG data of the human retina and its underlying neural activity and interpreting the data in terms of the specific neural features that contribute to it. In this preliminary study, the rigor of this analysis technique was assessed by fitting the model with a genetic algorithm (GA) to collected mERG data from ten healthy patients.