Self-referential forces are sufficient to explain different dendritic morphologies

Dendritic morphology constrains brain activity, as it determines first which neuronal circuits are possible and second which dendritic computations can be performed over a neuron's inputs. It is known that a range of chemical cues can influence the final shape of dendrites during development. H...

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Main Authors: Heraldo eMemelli, Benjamin eTorben-Nielsen, James eKozloski
Format: Article
Language:English
Published: Frontiers Media S.A. 2013-01-01
Series:Frontiers in Neuroinformatics
Subjects:
Online Access:http://journal.frontiersin.org/Journal/10.3389/fninf.2013.00001/full
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spelling doaj-9bb154b98bf948bdb75b9c54f0f00b412020-11-24T21:02:53ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962013-01-01710.3389/fninf.2013.0000138867Self-referential forces are sufficient to explain different dendritic morphologiesHeraldo eMemelli0Benjamin eTorben-Nielsen1James eKozloski2Stony Brook UniversityThe Hebrew UniversityIBM Research DivisionDendritic morphology constrains brain activity, as it determines first which neuronal circuits are possible and second which dendritic computations can be performed over a neuron's inputs. It is known that a range of chemical cues can influence the final shape of dendrites during development. Here, we investigate the extent to which self-referential influences, cues generated by the neuron itself, might influence morphology. To this end, we developed a phenomenological model and algorithm to generate virtual morphologies, which are then compared to experimentally reconstructed morphologies. In the model, branching probability follows a Galton-Watson process, while the geometry is determined by "homotypic forces" exerting influence on the direction of random growth in a constrained space. We model three such homotypic forces, namely an inertial force based on membrane stiffness, a soma-oriented tropism, and a force of self avoidance, as directional biases in the growth algorithm. With computer simulations we explored how each bias shapes neuronal morphologies. We show that based on these principles, we can generate realistic morphologies of several distinct neuronal types. We discuss the extent to which homotypic forces might influence real dendritic morphologies, and speculate about the influence of other environmental cues on neuronal shape and circuitry.http://journal.frontiersin.org/Journal/10.3389/fninf.2013.00001/fullsimulationmorphologyDendritecomputationalGrowth coneModel
collection DOAJ
language English
format Article
sources DOAJ
author Heraldo eMemelli
Benjamin eTorben-Nielsen
James eKozloski
spellingShingle Heraldo eMemelli
Benjamin eTorben-Nielsen
James eKozloski
Self-referential forces are sufficient to explain different dendritic morphologies
Frontiers in Neuroinformatics
simulation
morphology
Dendrite
computational
Growth cone
Model
author_facet Heraldo eMemelli
Benjamin eTorben-Nielsen
James eKozloski
author_sort Heraldo eMemelli
title Self-referential forces are sufficient to explain different dendritic morphologies
title_short Self-referential forces are sufficient to explain different dendritic morphologies
title_full Self-referential forces are sufficient to explain different dendritic morphologies
title_fullStr Self-referential forces are sufficient to explain different dendritic morphologies
title_full_unstemmed Self-referential forces are sufficient to explain different dendritic morphologies
title_sort self-referential forces are sufficient to explain different dendritic morphologies
publisher Frontiers Media S.A.
series Frontiers in Neuroinformatics
issn 1662-5196
publishDate 2013-01-01
description Dendritic morphology constrains brain activity, as it determines first which neuronal circuits are possible and second which dendritic computations can be performed over a neuron's inputs. It is known that a range of chemical cues can influence the final shape of dendrites during development. Here, we investigate the extent to which self-referential influences, cues generated by the neuron itself, might influence morphology. To this end, we developed a phenomenological model and algorithm to generate virtual morphologies, which are then compared to experimentally reconstructed morphologies. In the model, branching probability follows a Galton-Watson process, while the geometry is determined by "homotypic forces" exerting influence on the direction of random growth in a constrained space. We model three such homotypic forces, namely an inertial force based on membrane stiffness, a soma-oriented tropism, and a force of self avoidance, as directional biases in the growth algorithm. With computer simulations we explored how each bias shapes neuronal morphologies. We show that based on these principles, we can generate realistic morphologies of several distinct neuronal types. We discuss the extent to which homotypic forces might influence real dendritic morphologies, and speculate about the influence of other environmental cues on neuronal shape and circuitry.
topic simulation
morphology
Dendrite
computational
Growth cone
Model
url http://journal.frontiersin.org/Journal/10.3389/fninf.2013.00001/full
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