A Point-process Response Model for Spike Trains from Single Neurons in Neural Circuits under Optogenetic Stimulation

TL;DR

This paper introduces the PRO model, a statistical approach for analyzing high-frequency neuronal spike trains under optogenetic stimulation, providing interpretable insights into neural response mechanisms.

Contribution

The paper develops a generalized linear model for point processes that captures the relationship between light stimuli and neuronal spikes, with validation on real data and simulations.

Findings

PRO model achieves up to 93% prediction accuracy.

Neurons integrate inputs in a complex, interpretable manner.

Model helps compare neural circuit responses under different conditions.

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.