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Generative Biophysical Modeling of Dynamical Networks in the Olfactory System

Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1820)

Abstract

Generative models are computational models designed to generate appropriate values for all of their embedded variables, thereby simulating the response properties of a complex system based on the coordinated interactions of a multitude of physical mechanisms. In systems neuroscience, generative models are generally biophysically based compartmental models of neurons and networks that are explicitly multiscale, being constrained by experimental data at multiple levels of organization from cellular membrane properties to large-scale network dynamics. As such, they are able to explain the origins of emergent properties in complex systems, and serve as tests of sufficiency and as quantitative instantiations of working hypotheses that may be too complex to simply intuit. Moreover, when adequately constrained, generative biophysical models are able to predict novel experimental outcomes, and consequently are powerful tools for experimental design. We here outline a general strategy for the iterative design and implementation of generative, multiscale biophysical models of neural systems. We illustrate this process using our ongoing, iteratively developing model of the mammalian olfactory bulb. Because the olfactory bulb exhibits diverse and interesting properties at multiple scales of organization, it is an attractive system in which to illustrate the value of generative modeling across scales.

Key words

Generative model Compartmental model Olfactory bulb Subthreshold oscillations Network Gamma oscillations 

Notes

Acknowledgments

NIDCD grants R03 DC013872 to G.L., R01 DC014701 and R01 DC014367 to T.A.C.

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Authors and Affiliations

  1. 1.Department of PsychologyCornell UniversityIthacaUSA
  2. 2.Department of PsychiatryUniversity of North CarolinaChapel HillUSA

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