In the past years, there has been a paradigm shift in the field of cognitive neuroscience as a number of behavioral studies demonstrated that animals and humans can take into account statistical uncertainties of task, reward, and their own behavior, in order to achieve optimal task performance. These results have been interpreted in terms of statistical inference in probabilistic models. However, such an interpretation raises the question of how cortical networks represent and make use of the probability distributions necessary to carry out such computations. Recently, we have proposed that neural activity patterns correspond to samples from the posterior distribution over interpretations of the sensory input, a hypothesis that is consistent with several experimental observations (e.g. trial-to-trial variability). Last year, using this framework, we verified experimentally that the distribution of spontaneous activity in such probabilistic representations adapts over development to match that of evoked activity averaged over stimuli, based on recordings from V1 of awake ferrets. In the present study, we define and test two novel predictions of this framework. First, we predict that the match between evoked and spontaneous activity should be specific to the distribution of neural activity evoked by natural stimuli, and not to that evoked by artificial stimulus ensembles. We expect this match to hold for instantaneous neural activity, and for temporal transitions between activity pattern. Second, if this hypothesis captures the general computational strategy in the sensory cortex, it should be valid across sensory modalities. To test these predictions, we analyzed single unit data (N=32 over 6 recordings) recorded simultaneously from multiple electrodes in the primary auditory cortex (A1) of awake ferrets in three stimulus conditions: a natural condition consisting in a stream of continuous speech, a white noise (0-20 kHz) condition, and a spontaneous activity condition where the animal was listening in silence. Speech was chosen since its spectrotemporal characteristics are similar to those of natural sounds. We analyzed the neural data, which was discretized in 25 ms bins, binarized, and the distribution of instantaneous, joint activity, and the transition probability from one activity pattern to the next was estimated in the three conditions. We measured dissimilarity between the silence and stimulus condition distributions using Kullback-Leibler divergence. The robustness of our results was estimated using a bootstrapping technique.

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