A point‐process response model for spike trains from single neurons in neural circuits under optogenetic stimulation. (27th September 2015)

• Record Type: Journal Article
• Authors: Luo, X.; Gee, S.; Sohal, V.; Small, D.
• Is Part Of: Statistics in medicine. Volume 35:Number 3(2016)
• Page Start: 455
• Page End: 474
• Publication Date: 2015-09-27
• Subjects: generalized linear models -- neuronal circuits -- optogenetics -- point processes -- prediction -- response functions; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.6742
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : Optogenetics is a new tool to study neuronal circuits that have been genetically modified to allow stimulation by flashes of light. We study recordings from single neurons within neural circuits under optogenetic stimulation. The data from these experiments present a statistical challenge of modeling a high‐frequency point process (neuronal spikes) while the input is another high‐frequency point process (light flashes). We further develop a generalized linear model approach to model the relationships between two point processes, employing additive point‐process response functions. The resulting model, point‐process responses for optogenetics (PRO), provides explicit nonlinear transformations to link the input point process with the output one. Such response functions may provide important and interpretable scientific insights into the properties of the biophysical process that governs neural spiking in response to optogenetic stimulation. We validate and compare the PRO model using a real dataset and simulations, and our model yields a superior area‐under‐the‐curve value as high as 93% for predicting every future spike. For our experiment on the recurrent layer V circuit in the prefrontal cortex, the PRO model provides evidence that neurons integrate their inputs in a sophisticated manner. Another use of the model is that it enables understanding how neural circuits are altered under various disease conditions and/or experimental conditions by comparing the PRO parameters. Copyright © 2015 John Wiley & Sons, Ltd.