Sensory cortical population dynamics uniquely track behavior across learning and extinction.
Neural responses in many cortical regions encode information relevant to behavior: information that necessarily changes as that behavior changes with learning. Although such responses are reasonably theorized to be related to behavior causation, the true nature of that relationship cannot be clarified by simple learning studies, which show primarily that responses change with experience. Neural activity that truly tracks behavior (as opposed to simply changing with experience) will not only change with learning but also change back when that learning is extinguished. Here, we directly probed for this pattern, recording the activity of ensembles of gustatory cortical single neurons as rats that normally consumed sucrose avidly were trained first to reject it (i.e., conditioned taste aversion learning) and then to enjoy it again (i.e., extinction), all within 49 h. Both learning and extinction altered cortical responses, consistent with the suggestion (based on indirect evidence) that extinction is a novel form of learning. But despite the fact that, as expected, postextinction single-neuron responses did not resemble "naive responses," ensemble response dynamics changed with learning and reverted with extinction: both the speed of stimulus processing and the relationships among ensemble responses to the different stimuli tracked behavioral relevance. These data suggest that population coding is linked to behavior with a fidelity that single-neuron coding is not.
Psychology Department and Volen Center for Complex Systems, Brandeis University, Waltham, Massachusetts 02453.
Anan
A
Moran
Donald B
DB
Katz
D000818:Animals; D001522:Behavior, Animal / Q000502:physiology*; D002540:Cerebral Cortex / Q000502:physiology*; D004594:Electrophysiology; D005108:Extinction, Psychological / Q000502:physiology*; D005260:Female; D007858:Learning / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D051381:Rats
condition taste aversion; dynamics; hidden Markov models; learning; neural network