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Krasnow Institute > Monday Seminars > Abstracts A STATISTICAL FRAMEWORK FOR PRESENTING DEVELOPMENTAL NEUROANATOMY Stephen L. Senft The subject matter of Neuroscience consists of many millions of neurons whose arbors intertwine in ways more intricate than we can envision. Although there may be numerous global constraints on the operations of neural systems, which may govern brain use irrespective of morphology, it seems overwhelmingly likely that the adaptive functions of brain tissue mainly reflect a myriad of precisely timed events constrained by precisely sculpted structures. Hence there is a pressing need to envision these anatomical complexities. While there is an astonishing wealth of information on brain anatomy in the literature (from electron and light micrographs, to elegant tracings, to confocal volume images and CT, MRI and PET scans), it is piecemeal and not readily available in the form of models that can be queried about functional operation. I will present an approach for SYTHESIZING anatomically plausible 3D neural networks -- grown from sets of rules and statistical constraints derived (or derivable) from published data. This approach could lead to a set of interactive graphical tools for examining neural networks and their biophysical properties at cellular and subcellular detail, both during ontogeny and in the adult state.
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