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High-probability grants
According to our matching algorithm, Jerry L. Chen is the likely recipient of the following grants.
Years |
Recipients |
Code |
Title / Keywords |
Matching score |
2019 — 2021 |
Chen, Jerry L |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Cortical Interactions Underlying Sensory Representations @ Boston University (Charles River Campus)
PROJECT SUMMARY Sensory perception involves processing incoming sensory input and interpreting that information through rules generated from prior experience. Stimulus features need to be bound together to form more complex sensory representations and then associated with a valence or action outcome to give meaning to those representations. In the mammalian neocortex, the formation of sensory representations is believed to occur through processing that is distributed across several cortical areas. Beyond this general framework, the exact circuits and computations involved in transforming sensory information into increasingly abstract representations remain unknown. To achieve a deeper mechanistic understanding, it is necessary to close the loop between theoretical models and experimental work and to identify common mechanisms through comparative approaches across model systems. Many of the leading theoretical models for sensory processing have been derived from experimental studies performed in humans or non-human primates. It has been difficult to validate or refine these computational models because of the limited experimental access to tools for circuit-level dissection in those species. However, novel tools for circuit dissection are now available for applications in mice. Using newly developed whisker-based mouse behaviors that recapitulate perceptual tasks in primates, we propose to investigate how stimulus information is encoded and transformed across primary somatosensory, secondary somatosensory, and perirhinal cortex, three prominent reciprocally connected areas that function at increasingly complex stages of sensory processing. Our goal is to achieve a circuit-level understanding for how stimuli are bound to generate higher-order representations, how such representations are associated with action outcomes, and how they are learned and recalled as required for behavior. This work will provide critical new insight into how local and long-range cortical circuits function to build internal representations and evaluate the external environment. By testing and improving upon models of cortical function across mammalian species, we will seek to derive common principles of circuit function that explain how increasingly invariant and abstract representations may be encoded and implemented in the neocortex.
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