Medial prefrontal cortex neural populations have been shown to encode the learning of avoidance behaviors

Over the course of their lives, animals form associations between sensory stimuli and anticipated threats or rewards. These associations can, in turn, shape animals’ behaviors, prompting them to engage in avoidance behaviors (e.g., avoiding specific stimuli and situations) or, conversely, to interact with their environment in various ways.

Previous neuroscience studies have shown that this process of acquiring behavioral patterns through experience is supported by various brain regions. One such region is the medial prefrontal cortex (mPFC), a large segment of the frontal part of the brain known to contribute to decision-making, attention, learning, and memory consolidation.

Researchers from the University of Zurich and ETH Zurich recently conducted a study on how the mPFC contributes to the learning of behavioral strategies over time, focusing specifically on the processes by which it links sensory information to an animal’s behavior. Their results, published in Neuroscience of Naturesuggest that the mPFC transforms sensory inputs into behavioral outputs by performing a series of computations at the neuronal population level.

“The medial prefrontal cortex (mPFC) has been suggested to link sensory inputs and behavioral outputs to mediate the execution of learned behaviors,” Benjamin Ehret, Roman Boehringer, and colleagues wrote in their paper. “However, how such a link is implemented remains unclear. To measure the prefrontal neural correlates of sensory stimuli and learned behaviors, we performed population calcium imaging during a novel cued active avoidance paradigm in mice.”

During their experiments, which lasted 11 days, Ehret, Boehringer and their colleagues recorded the activity of neurons in the brains of mice while the animals were engaged in a fear conditioning task. The mice were placed in a chamber for training sessions consisting of 50 trials each.

After hearing a beep, the mice received a mild but unpleasant electric shock to their paws. The half of the chamber they were in, however, was marked off as a “safe zone,” meaning that if they were in that zone, the mice would not receive any electric shock after the beep.

After repeated trials, the mice engaged in avoidance behaviors and learned to move quickly to the safe zone after hearing the sound signal. To examine the activity of neurons as the mice learned to avoid the shock by escaping to the safe zone, the researchers used calcium imaging and fluorescence microscopy techniques.

“We developed an analysis approach based on dimensionality reduction and decoding that allowed us to identify interpretable task-related population activity patterns,” Ehret, Boehringer, and colleagues wrote.

“Although much of the tone-evoked activity was not informative about behavioral execution, we identified a pattern of activity that was predictive of tone-induced avoidance actions and that did not occur for spontaneous actions with similar movement kinematics. Furthermore, this avoidance-specific activity differed between distinct avoidance actions learned in two consecutive tasks.”

By analyzing the recordings collected during the experimental trials, the researchers observed a pattern of activity in mPFC neurons related to the execution of avoidance behaviors after hearing the sound and after prior fear conditioning training. Overall, the team’s observations suggest that the mPFC transforms sensory inputs into specific behavioral outputs through a series of distributed computations at the neuronal population level.

“These results highlight the complex interplay between sensory processing and behavioral execution, and further work is needed to understand the temporal dynamics of sensory information flow across the network of brain areas involved,” Ehret, Boehringer and colleagues wrote.

The results of this recent study could soon pave the way for a better understanding of the mPFC and its contribution to learning goal-directed behavior. Future studies could examine the activity patterns identified by the ETH Zurich team in more detail using other experimental methods and learning paradigms.

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