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Towards brains in the cloud: a biophysically realistic computational model of olfactory bulb

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The increasing availability of experimental data and computational power have resulted in increasingly detailed and sophisticated models of brain structures. Biophysically realistic models allow detailed investigations of the mechanisms that operate within those structures. In this work, published mouse experimental

The increasing availability of experimental data and computational power have resulted in increasingly detailed and sophisticated models of brain structures. Biophysically realistic models allow detailed investigations of the mechanisms that operate within those structures. In this work, published mouse experimental data were synthesized to develop an extensible, open-source platform for modeling the mouse main olfactory bulb and other brain regions. A “virtual slice” model of a main olfactory bulb glomerular column that includes detailed models of tufted, mitral, and granule cells was created to investigate the underlying mechanisms of a gamma frequency oscillation pattern (“gamma fingerprint”) often observed in rodent bulbar local field potential recordings. The gamma fingerprint was reproduced by the model and a mechanistic hypothesis to explain aspects of the fingerprint was developed. A series of computational experiments tested the hypothesis. The results demonstrate the importance of interactions between electrical synapses, principal cell synaptic input strength differences, and granule cell inhibition in the formation of the gamma fingerprint. The model, data, results, and reproduction materials are accessible at https://github.com/justasb/olfactorybulb. The discussion includes a detailed description of mechanisms underlying the gamma fingerprint and how the model predictions can be tested experimentally. In summary, the modeling platform can be extended to include other types of cells, mechanisms and brain regions and can be used to investigate a wide range of experimentally testable hypotheses.

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Date Created
2019