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 DESCRIPTION (provided by applicant): The brain is a massively interconnected network of regions, each of which contains neural circuits that process information related to combinations of sensory, motor and internal variables. Adaptive behavior requires that these regions communicate: sensory and internal information must be evaluated and used to make a decision, which must then be transformed into a motor output. Despite the importance of this question, we know relatively little about the principles of how spiking activity in one region influences activiy in downstream areas, particularly in the context of cognitive operations like decision-making. Here we propose to address this question by focusing on how the ventral striatum (VS), a region critical for motivational control of behavior receives and processes information from two important upstream regions, the orbitofrontal cortex (OFC) and the hippocampus (HP). We have assembled a unique team of scientists with complementary expertise studying the HP (Frank), OFC and VS (Kepecs), using synergistic technologies for large-scale recordings using novel polymer electrodes (Frank/Tolosa) with improved optogenetic identification of projections (Kepecs), and a team of statistical and computational researchers providing complementary analytical expertise in dimensionality reduction (Machens), statistical modeling (Eden/Kramer) and normative models (Ganguli). Our combined expertise will allow us to (1) measure large populations of neurons across the brain regions, (2) identify and (3) manipulate the neurons connecting them in order to (4) test for the first time a range of hypotheses about different modes and circuits for information transmission across regions. Beyond revealing how the OFC, HP and VS communicate during learning and decision-making, our approach will provide new experimental tools and computational methods for systems neuroscience, as well as new insights into the general principles of information transmission across regions.
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Rigorous Research Principles for Practicing Neuroscientists
Sleep Spindle Dynamics as a Clinical Biomarker of Aging, Alzheimer's Disease, and Trisomy 21
  • 批准号:
    10733629
  • 项目类别:
  • 资助金额:
    $257.69万
  • 财政年份:
    2023
  • 负责人:
    Uri Tzvi Eden
  • 依托单位:
Statistical machine learning tools for understanding neural ensemble representations and dynamics
Measuring, Modeling, and Modulating Cross-Frequency Coupling
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