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Nevjen, F.

Publications and source records attributed to Nevjen, F..

2 recordsLinked to original sources

Prefrontal cortex encodes behavior states decoupled from movement

Prefrontal cortex is often viewed as an extension of the motor system, but little is understood of how it relates to natural motor behavior. We therefore tracked the kinematics of freely moving rats performing minimally structured tasks and measured which aspects of behavior were read out in prefrontal neural populations. Naturalistic behaviors such as rearing or chasing a bait were each encoded by unique neural ensembles, but the behavioral representations were not anchored to posture or movement. Rather, the coding of kinematic features depended on their relevance to the animals current behavior or which task the animal performed. Behavior-specific ensembles often preceded and outlasted physical actions and, accordingly, prefrontal population activity evolved at slower timescales than in motor cortex. These findings argue that prefrontal coding of behavior is not locked to motor output, and may instead reflect motivations to perform certain actions rather than the actions themselves. HighlightsO_LIPrefrontal neural ensembles uniquely encode different naturalistic actions C_LIO_LIBehavioral tuning is not explained by movement kinematics C_LIO_LIPopulation activity in prefrontal cortex evolves slower than in M1 C_LIO_LISingle-cell coding of behavior varies across tasks yet ensemble coding is stable C_LI

neuroscience↗

Insights in neuronal tuning: Navigating the statistical challenges of autocorrelation and missing variables

Recent advances in neuroscience have improved our ability to investigate neural activity by making it possible to measure vast amounts of neurons and behavioral variables, and explore the underlying mechanisms that connect them. However, comprehensively understanding neuronal tuning poses challenges due to statistical issues such as temporal autocorrelation and missing variables, as neurons are likely driven in part by unknown factors. The field consequently needs a systematic approach to address these challenges. This study compares various methods for covariate selection using both simulated data and calcium data from the medial entorhinal cortex. We conclude that a combination of cross-validation and a cyclical shift permutation test yields higher test power than other evaluated methods while maintaining proper error rate control, albeit at a higher computational cost. This research sheds light on the quest for a systematic understanding of neuronal tuning and provides insight into covariate selection in the presence of statistical complexities.

neuroscience↗