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Dynamic Population Coding of Category Information in Inferior Temporal and Prefrontal Cortex

¹Department of Brain and Cognitive Sciences and ²The McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, ³The Picower Institute for Learning and Memory, ⁴Department of Neurobiology, The University of Chicago, Chicago, Illinois; and ⁵Department of Ophthalmology and Program in Neuroscience, Children's Hospital Boston, Harvard Medical School, Massachusetts

Submitted 8 February 2008; accepted in final form 14 June 2008

Most electrophysiology studies analyze the activity of each neuron separately. While such studies have given much insight into properties of the visual system, they have also potentially overlooked important aspects of information coded in changing patterns of activity that are distributed over larger populations of neurons. In this work, we apply a population decoding method to better estimate what information is available in neuronal ensembles and how this information is coded in dynamic patterns of neural activity in data recorded from inferior temporal cortex (ITC) and prefrontal cortex (PFC) as macaque monkeys engaged in a delayed match-to-category task. Analyses of activity patterns in ITC and PFC revealed that both areas contain "abstract" category information (i.e., category information that is not directly correlated with properties of the stimuli); however, in general, PFC has more task-relevant information, and ITC has more detailed visual information. Analyses examining how information coded in these areas show that almost all category information is available in a small fraction of the neurons in the population. Most remarkably, our results also show that category information is coded by a nonstationary pattern of activity that changes over the course of a trial with individual neurons containing information on much shorter time scales than the population as a whole.

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