Title:
Morphologically simplified conductance based neuron models: principles of construction and use in parameter optimization

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dc.contributor.advisor Jaeger, Dieter
dc.contributor.author Hendrickson, Eric B. en_US
dc.contributor.committeeMember Butera, Robert
dc.contributor.committeeMember Calabrese, Ronald
dc.contributor.committeeMember Lee, Robert H.
dc.contributor.committeeMember Prinz, Astrid
dc.contributor.committeeMember Smith, Yoland
dc.contributor.department Biomedical Engineering en_US
dc.date.accessioned 2010-06-10T16:30:13Z
dc.date.available 2010-06-10T16:30:13Z
dc.date.issued 2010-04-02 en_US
dc.description.abstract The dynamics of biological neural networks are of great interest to neuroscientists and are frequently studied using conductance-based compartmental neuron models. For speed and ease of use, neuron models are often reduced in morphological complexity. This reduction may affect input processing and prevent the accurate reproduction of neural dynamics. However, such effects are not yet well understood. Therefore, for my first aim I analyzed the processing capabilities of 'branched' or 'unbranched' reduced models by collapsing the dendritic tree of a morphologically realistic 'full' globus pallidus neuron model while maintaining all other model parameters. Branched models maintained the original detailed branching structure of the full model while the unbranched models did not. I found that full model responses to somatic inputs were generally preserved by both types of reduced model but that branched reduced models were better able to maintain responses to dendritic inputs. However, inputs that caused dendritic sodium spikes, for instance, could not be accurately reproduced by any reduced model. Based on my analyses, I provide recommendations on how to construct reduced models and indicate suitable applications for different levels of reduction. In particular, I recommend that unbranched reduced models be used for fast searches of parameter space given somatic input output data. The intrinsic electrical properties of neurons depend on the modifiable behavior of their ion channels. Obtaining a quality match between recorded voltage traces and the output of a conductance based compartmental neuron model depends on accurate estimates of the kinetic parameters of the channels in the biological neuron. Indeed, mismatches in channel kinetics may be detectable as failures to match somatic neural recordings when tuning model conductance densities. In my first aim, I showed that this is a task for which unbranched reduced models are ideally suited. Therefore, for my second aim I optimized unbranched reduced model parameters to match three experimentally characterized globus pallidus neurons by performing two stages of automated searches. In the first stage, I set conductance densities free and found that even the best matches to experimental data exhibited unavoidable problems. I hypothesized that these mismatches were due to limitations in channel model kinetics. To test this hypothesis, I performed a second stage of searches with free channel kinetics and observed decreases in the mismatches from the first stage. Additionally, some kinetic parameters consistently shifted to new values in multiple cells, suggesting the possibility for tailored improvements to channel models. Given my results and the potential for cell specific modulation of channel kinetics, I recommend that experimental kinetic data be considered as a starting point rather than as a gold standard for the development of neuron models. en_US
dc.description.degree Ph.D. en_US
dc.identifier.uri http://hdl.handle.net/1853/33905
dc.publisher Georgia Institute of Technology en_US
dc.subject Ion channel kinetics en_US
dc.subject Computational modeling en_US
dc.subject Sensitivity analysis en_US
dc.subject Optimization techniques en_US
dc.subject Neuron models en_US
dc.subject.lcsh Neural networks (Neurobiology)
dc.subject.lcsh Neurosciences
dc.subject.lcsh Mathematical optimization
dc.subject.lcsh Computational neuroscience
dc.title Morphologically simplified conductance based neuron models: principles of construction and use in parameter optimization en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.corporatename Wallace H. Coulter Department of Biomedical Engineering
local.contributor.corporatename College of Engineering
relation.isOrgUnitOfPublication da59be3c-3d0a-41da-91b9-ebe2ecc83b66
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
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