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Functional Relevance of Homeostatic Intrinsic Plasticity in Neurons and Networks
KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). School of Informatics, University of Edinburgh, UK.
2016 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

Maintaining the intrinsic excitability of neurons is crucial for stable brain activity. This can be achieved by the homeostatic regulation of membrane ion channel conductances, although it is not well understood how these processes influence broader aspects of neuron and network function. One of the many mechanisms which contribute towards this task is the modulation of potassium channel conductances by activity-dependent nitric oxide signalling. Here, we first investigate this mechanism in a conductance-based neuron model. By fitting the model to experimental data we find that nitric oxide signalling improves synaptic transmission fidelity at high firing rates, but that there is an increase in the metabolic cost of action potentials associated with this improvement. Although the improvement in function had been observed previously in experiment, the metabolic constraint was unknown. This additional constraint provides a plausible explanation for the selective activation of nitric oxide signalling only at high firing rates. In addition to mediating homeostatic control of intrinsic excitability, nitric oxide can diffuse freely across cell membranes, providing a unique mechanism for neurons to communicate within a network, independent of synaptic connectivity. We next conduct a theoretical investigation of the distinguishing roles of diffusive homeostasis mediated by nitric oxide in comparison with canonical non-diffusive homeostasis in cortical networks. We find that both forms of homeostasis robustly maintain stable activity. However, the resulting networks differ, with diffusive homeostasis maintaining substantial heterogeneity in activity levels of individual neurons, a feature disrupted in networks with non-diffusive homeostasis. This results in networks capable of representing input heterogeneity, and linearly responding over a broader range of inputs than those undergoing non-diffusive homeostasis. We further show that diffusive homeostasis interferes less than non-diffusive homeostasis in the synaptic weight dynamics of networks undergoing Hebbian plasticity. Overall, these results suggest a novel homeostatic mechanism for maintaining stable network activity while simultaneously minimising metabolic cost and conserving network functionality.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016. , iii, 118 p.
TRITA-CSC-A, ISSN 1653-5723 ; 2016:08
National Category
Bioinformatics (Computational Biology)
Research subject
Computer Science
URN: urn:nbn:se:kth:diva-185747ISBN: 978-91-7595-970-2 (print)OAI: diva2:923346
Public defence
2016-05-23, Konstantinbågen, Drottning Kristinas väg 4 (videokonferens), KTH, Stockholm, 10:00 (English)

Joint Doctoral Program in Neuroinformatics.

Public defence Monday, 23 May 2016, at 9.00 a.m. in Room 1,15, Meeting and Training suite, 1st Floor, Library, Univ Edinburgh, School of Informatics (can be joined via videoconference from Konstantinbågen, Drottning Kristinas väg 4, Kungliga Tekniska högskolan, Stockholm.

QC 20160426

Available from: 2016-04-26 Created: 2016-04-26 Last updated: 2016-04-26Bibliographically approved

Open Access in DiVA

Thesis(13889 kB)