The concept of ?representation? is a central pillar in the interpretive framework of neuroscience. Characterizing neural representations is thus a central concern in every domain of cognitive neuroscience. Though considerable attention has been paid to the ?content? of neural representations, as in what information in encoded, far fewer studies have considered the ?format? of neural representations or how this information is organized. Yet, this latter property is of paramount importance. The format in which a population of neurons represents information constrains how accessible that information is to downstream circuits, and so necessarily shapes circuit and network level computation, as well as behavior. However, our views about the format of neural representations in humans are largely informed by single neuron electrophysiology carried out in other species. A critical property of a neural representation?s format is its dimensionality, which controls a trade-off between the flexibility of a representation and its efficiency. The dimensionality of neural task representations in human prefrontal cortex is believed to be critical to cognitive control, or our ability to flexibly guide our cognition in a goal-directed manner. Current methods for estimating the dimensionality of neural representations rely on analyzing activity patterns over multiple voxels and are hampered in regions of the brain like the prefrontal cortex which have a different underlying spatial micro-structure of neuronal populations. A different approach for estimating dimensionality based on measuring repetition suppression effects has been proposed but never implemented. In the absence of a reliable method for estimating dimensionality, important questions about human neural representations remain unaddressed, and the state of neural representations currently plays little part in our thinking about neurological or psychiatric disorders. Across its aims, the proposed research program seeks to develop and validate a new repetition suppression based non-invasive method for building whole-brain, voxel-resolution maps of representational dimensionality in the human brain. This method will be employed to test important hypotheses about human task representations. In particular, based on prior work in macaques, we hypothesize that neural populations in the prefrontal cortex serve to integrate different sources of information and represent them in a high-dimensional format that make this information useful and accessible for downstream neurons. In addition, we hypothesize that the maintenance of a high-dimensional representation is critical for behavioral performance. We test these hypotheses in an experiment that applies the repetition suppression based method for estimating dimensionality with fMRI. More broadly, this method will enable systematic, quantitative studies of representational format across several domains of cognitive neuroscience, in both healthy people at different developmental stages, and patients with brain disease or disorder.
Understanding the format of neural representations is crucial to understanding how brain networks produce cognition and behavior. Few reliable methods exist for the study of the dimensionality of neural representational formats in humans, and thus its contributions to cognitive deficits remains unexamined. The proposed research program seeks to develop an important new non-invasive method for building whole-brain, voxel-resolution maps of representational dimensionality in the human brain, thus allowing systematic, quantitative studies of representational format in both healthy people at different developmental stages, and patients with brain disease or disorder.
