| Riassunto: | Classifying neurons into different cell classes is both an idea that has existed since the origins of neuroscience, and one that is essential to understanding the complex interactions of the brain. While there has been a substantial effort to categorize neurons morphologically, molecularly and physiologically in in vitro studies, there is a gap in experiments performed on awake and behaving animals. Using data collected from macaque monkeys performing a working memory task, and employing an unsupervised Gaussian mixture model (GMM) clustering algorithm, a number of different cell classes and their defining features were distinguished in area 7A, the lateral intraparietal area (LIP), the dorsolateral and ventrolateral prefrontal cortex (PFC) and the extrastriate visual area (V4). While the number of cell classes found across areas differed, there were several classes across areas that appeared to be correlates. Classes in each area also showed functional differences in information encoding during predictable trials and distributional differences in depth. This signifies both the potential of functionally distinct cell classes involved in prediction, as well as the existence of universal cell classes across different areas.
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