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High-probability grants
According to our matching algorithm, Mark P. Beenhakker is the likely recipient of the following grants.
Years |
Recipients |
Code |
Title / Keywords |
Matching score |
2001 — 2003 |
Beenhakker, Mark |
F31Activity Code Description: To provide predoctoral individuals with supervised research training in specified health and health-related areas leading toward the research degree (e.g., Ph.D.). |
Motor Pattern Selection From a Multifunctional Network @ University of Pennsylvania
DESCRIPTION:(from applicant's abstract) An objective of neuroscience research is to understand the neuronal basis of behavior at the cellular level. Implicit in this goal is the desire to understand how the CNS malfunctions in disorders such as schizophrenia and Parkinson's disease. These goals require an understanding of how neural networks are organized and how they can be influenced to produce different behaviors. One approach to this end is to understand how distinct sensory pathways modify neural network activity to produce different behaviors. The crustacean stomatogastric nervous system (STNS) is a model system whose network activity has been studied for over thirty years. Researchers studying this system have acquired the nearly unique position in neuroscience of knowing how a discrete network of neurons generates the activity patterns underlying behavior at the detailed cellular level. This deep level of understanding of the STNS makes this model system a prime candidate for studying how information carried by different sensory pathways relevant to a single behavior is processed and integrated within the nervous system to yield distinct neural outputs. Therefore, the proposal outlined below focuses on determining how activation of distinct sensory pathways modulates the output of the stomatogastric ganglion (STG) of the rock crab, Cancer borealis. An interdisciplinary approach will be used, including intra- and extracellular recordings and neuroanatomical techniques. The hypothesis guiding this proposal is that motor pattern selection from a multifunctional neural network results from different sensory pathways evoking different motor patterns as a consequence of their having distinct actions on overlapping subsets of (a) modulatory projection neurons and (b) STG network neurons.
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