Lu, Yihe (2018) Neuronal Signal Modulation By Dendritic Geometry. PhD thesis, University of Warwick.

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Abstract

Neurons are the basic units in nervous systems. They transmit signals along neurites and at synapses in electrical and chemical forms. Neuronal morphology, mainly dendritic geometry, is famous for anatomical diversity, and names of many neuronal types reflect their morphologies directly. Dendritic geometries, as well as distributions of ion channels on cell membranes, contribute significantly to distinct behaviours of electrical signal filtration and integration in different neuronal types (even in the cases of receiving identical inputs in vitro).

In this thesis I mainly address the importance of dendritic geometry, by studying its effects on electrical signal modulation at the level of single neurons via mathematical and computational approaches. By ‘geometry’, I consider both branching structures of entire dendritic trees and tapered structures of individual dendritic branches. The mathematical model of dendritic membrane potential dynamics is established by generalising classical cable theory. It forms the theoretical benchmark for this thesis to study neuronal signal modulation on dendritic trees with tapered branches. A novel method to obtain analytical response functions in algebraically compact forms on such dendrites is developed. It permits theoretical analysis and accurate and efficient numerical calculation on a neuron as an electrical circuit. By investigating simplified but representative dendritic geometries, it is found that a tapered dendrite amplifies distal signals in comparison to the non-tapered dendrite. This modulation is almost a local effect, which is merely influenced by global dendritic geometry. Nonetheless, global geometry has a stronger impact on signal amplitudes, and even more on signal phases. In addition, the methodology employed in this thesis is perfectly compatible with other existing methods dealing with neuronal stochasticity and active behaviours. Future works of large-scale neural networks can easily adapt this work to improve computational efficiency, while preserving a large amount of biophysical details.

Item Type:	Thesis or Dissertation (PhD)
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QP Physiology
Library of Congress Subject Headings (LCSH):	Dendrites -- Mathematical models, Geometry, Computational neuroscience -- Research
Official Date:	August 2018
Dates:	Date Event August 2018 UNSPECIFIED
Institution:	University of Warwick
Theses Department:	Centre for Complexity Science
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Timofeeva, Yulia
Extent:	ix, 158 leaves : illustrations, charts
Language:	eng

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