One of the most intriguing aspects of adaptive behavior involves the inference of regularities and rules in ever-changing environments. Rules are often deduced through evidence-based learning which relies on the prefrontal cortex (PFC). This is a highly dynamic process, evolving trial by trial and therefore may not be adequately captured by averaging single-unit responses over numerous repetitions. Here, we employed advanced statistical techniques to visualize the trajectories of ensembles of simultaneously recorded medial PFC neurons on a trial-by-trial basis as rats deduced a novel rule in a set-shifting task. Neural populations formed clearly distinct and lasting representations of familiar and novel rules by entering unique network states. During rule acquisition, the recorded ensembles often exhibited abrupt transitions, rather than evolving continuously, in tight temporal relation to behavioral performance shifts. These results support the idea that rule learning is an evidence-based decision process, perhaps accompanied by moments of sudden insight.
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