Population-level neural correlates of flexible avoidance learning in medial prefrontal cortex

The medial prefrontal cortex (mPFC) has been proposed to link sensory inputs and behavioral outputs to mediate the execution of learned behaviors. However, how such a link is implemented has remained unclear. To measure prefrontal neural correlates of sensory stimuli and learned behaviors, we performed population calcium imaging during a novel tone-signaled active avoidance paradigm in mice. We developed a novel analysis approach based on dimensionality reduction and decoding that allowed us to identify and isolate population activity patterns related the tone stimulus, learned avoidance actions and general motion. While tone-related activity was not informative about behavior, avoidance-related activity was predictive of upcoming avoidance actions. Moreover, avoidance-related activity distinguished between two different learned avoidance actions, consistent with a model in which mPFC contributes to the selection between different goal-directed actions. Overall, our results suggest that mPFC circuit dynamics transform sensory inputs into specific behavioral outputs through distributed population-level computations

Residual dynamics resolves recurrent contributions to neural computation

Relating neural activity to behavior requires an understanding of how neural computations arise from the coordinated dynamics of distributed, recurrently connected neural populations. However, inferring the nature of recurrent dynamics from partial recordings of a neural circuit presents considerable challenges. Here we show that some of these challenges can be overcome by a fine-grained analysis of the dynamics of neural residuals-that is, trial-by-trial variability around the mean neural population trajectory for a given task condition. Residual dynamics in macaque prefrontal cortex (PFC) in a saccade-based perceptual decision-making task reveals recurrent dynamics that is time dependent, but consistently stable, and suggests that pronounced rotational structure in PFC trajectories during saccades is driven by inputs from upstream areas. The properties of residual dynamics restrict the possible contributions of PFC to decision-making and saccade generation and suggest a path toward fully characterizing distributed neural computations with large-scale neural recordings and targeted causal perturbations

Population-level neural correlates of flexible avoidance learning in medial prefrontal cortex

The medial prefrontal cortex (mPFC) has been proposed to link sensory inputs and behavioral outputs to mediate the execution of learned behaviors. However, how such a link is implemented has remained unclear. To measure prefrontal neural correlates of sensory stimuli and learned behaviors, we performed population calcium imaging during a novel tone-signaled active avoidance paradigm in mice. We developed a novel analysis approach based on dimensionality reduction and decoding that allowed us to identify and isolate population activity patterns related the tone stimulus, learned avoidance actions and general motion. While tone-related activity was not informative about behavior, avoidance-related activity was predictive of upcoming avoidance actions. Moreover, avoidance-related activity distinguished between two different learned avoidance actions, consistent with a model in which mPFC contributes to the selection between different goal-directed actions. Overall, our results suggest that mPFC circuit dynamics transform sensory inputs into specific behavioral outputs through distributed population-level computations