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• W2896940273 abstract "In order to understand the relationship between neuronal organization and behavior, precise methods that identify and quantify functional cellular ensembles are required. This is especially true in the quest to understand the mechanisms of memory and its engram. Brain structures involved in memory formation and storage, as well as the molecular determinates of memory are well known, however, the microanatomy of functional neuronal networks remain largely unidentified. We developed a novel approach to visualize memory allocation in neuronal networks through quantitative topographic measurement. Brain nuclei with some subdivisions are well defined - our approach allows for the identification of new functional micro-regions within established subdivisions. We present a set of analytic methods relevant for measurement of discrete neuronal data across a diverse range of brain sub-structures. We provide a novel methodology for the accurate measurement and quantitative comparison of functional micro-neural network activity across matched individual brains using micro-binning and heat mapping within brain sub-nuclei. We applied these techniques to the measurement of different memory traces, paving the way for greater understanding of the allocation of memory encoding within sub-nuclei and its memory extinction mediated change. These approaches can be used to understand other functional and behavioral questions, including sub-circuit organization, normal memory function and the complexities of pathology. Precise micro-mapping of functional neuronal topography provides essential data to decode network activity underlying behavior." @default.
• W2896940273 created "2018-10-26" @default.
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• W2896940273 date "2018-10-16" @default.
• W2896940273 modified "2023-09-25" @default.
• W2896940273 title "Functional Neuronal Topography: A Statistical Approach to Micro Mapping Neuronal Location" @default.
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• W2896940273 doi "https://doi.org/10.3389/fncir.2018.00084" @default.
• W2896940273 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6198090" @default.
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• W2896940273 hasPublicationYear "2018" @default.
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