An orderly single-trial organization of population dynamics in premotor cortex predicts behavioral variability

• Published in: Nature communications Vol. 10; no. 1; p. 216
• Main Authors: Wei, Ziqiang, Inagaki, Hidehiko, Li, Nuo, Svoboda, Karel, Druckmann, Shaul
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 15.01.2019; Nature Publishing Group; Nature Portfolio
• Subjects: 631/378/116/2393; 631/378/2632/2635; 631/378/2649/1409; 631/378/3920; Airborne particulates; Animals; Biofuels; Biomass; Decision Making - physiology; Ethanol; Fuels; Gasoline; Humanities and Social Sciences; Mice; Models, Neurological; Motor Cortex - physiology; multidisciplinary; Nanoparticles; Neurons - physiology; Particulate matter; Science; Science (multidisciplinary); Ultrafines; Urban transportation
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-018-08141-6

Animals are not simple input-output machines. Their responses to even very similar stimuli are variable. A key, long-standing question in neuroscience is to understand the neural correlates of such behavioral variability. To reveal these correlates, behavior and neural population activity must be related to one another on single trials. Such analysis is challenging due to the dynamical nature of brain function (e.g., in decision making), heterogeneity across neurons and limited sampling of the relevant neural population. By analyzing population recordings from mouse frontal cortex in perceptual decision-making tasks, we show that an analysis approach tailored to the coarse grain features of the dynamics is able to reveal previously unrecognized structure in the organization of population activity. This structure is similar on error and correct trials, suggesting dynamics that may be constrained by the underlying circuitry, is able to predict multiple aspects of behavioral variability and reveals long time-scale modulation of population activity. To explain the neural correlates of behavior and its variability, one must analyze single-trial population dynamics. Here, the authors develop a statistical method that extracts low-dimensional dynamics that explain behavior better than high-dimensional neural activity revealing unexpected structure.