A considerable body of research has documented the existence of neural signals that encode categories in primate prefrontal cortex. Comparatively little is known regarding how these neural representations are derived from sensory inputs, or more specifically, how neural signals that encode features are converted into neural signals encoding categories. Understanding that transform at a circuit level would shed needed light into the computational origin of abstract neural signals in prefrontal cortex. Here we analyze neural signals that encode features and categories in prefrontal cortex of monkeys performing a task that requires them to flexibly map one form of neural signal to the other. At a behavioral level, we show that rules influence which features of visual stimuli are sampled to compute the category of the stimulus. At the neural level, we show that the neural representation of features is relatively rule-independent, however the functional linkage between feature and category signals is re-routed as a function of which rule is in force. These results suggest that prefrontal circuits carrying out the feature-to-category transform can be dynamically reconfigured as a consequence of cognitive rules.
University of Minnesota Ph.D. dissertation. 2020. Major: Neuroscience. Advisor: Matthew Chafee. 1 computer file (PDF); 131 pages.
Neural Basis Of Rule-Dependent Flexible Mapping Of Features To Categories In Prefrontal Cortex.
Retrieved from the University of Minnesota Digital Conservancy,
Content distributed via the University of Minnesota's Digital Conservancy may be subject to additional license and use restrictions applied by the depositor.