In Parkinson's disease (PD), large parts of the brain transition into states of enhanced neural synchronization.
These phase transitions have been associated with the death of dopaminergic neurons as well as with impaired motor function.
In this thesis, we address the much-debated question of how parkinsonian synchronization depends on dopamine depletion in the basal ganglia (BG).
To this end, we develop spiking neural network (SNN) models of BG circuits and study them via bifurcation analysis.
First, we derive mean-field models that allow to account for various forms of short-term plasticity in SNNs.
We show that such short-term plasticity mechanisms can lead to highly synchronous, periodic bursting dynamics and discuss the relevance of this bursting regime for PD.
Second, we find that the external pallidum, an important part of the BG, cannot cause parkinsonian oscillations autonomously.
However, our results suggest that the external pallidum may contribute to the emergence of cross-frequency coupling that has been reported for parkinsonian oscillations.
Finally, we describe an open-source Python toolbox that we developed to implement and analyze mean-field models of neural dynamics.
Together, this thesis provides insight into BG synchronization processes as well as the mathematical basis and software for future studies of neural synchronization.:1 Introduction
1.1 A complex systems perspective of the brain
1.2 Brain function and the phase transition to synchronized neural activity
1.3 Low-dimensional manifolds of synchronized neural activity
1.4 Phase transitions to synchronized neural activity in Parkinson’s disease
1.5 Thesis overview
2 Mathematical Models and Methods
2.1 A non-linear oscillator model of neural activity
2.2 Dynamical systems methods for the study of neural network models
2.3 Dynamics of a single QIF neuron
3 Low-Dimensional Dynamics in Spiking Neural Networks
3.1 Mean-field approaches in neuroscience
3.2 Dynamics of QIF networks with post-synaptic STP
3.3 Dynamics of QIF networks with spike-frequency adaptation
3.4 Mean-field dynamics of QIF networks with pre-synaptic STP
3.5 Discussion
4 Phase Transitions and Neural Synchronization in the External Pallidum
4.1 A new perspective on GPe structure and function
4.2 GPe model definition and analysis
4.3 Phase transitions in the GPe under static and periodic input
4.4 Discussion
5. Modeling of Neural Mean-Field Dynamics Via PyRates
5.1 Computational modeling in neuroscience
5.2 The Framework
5.3 Pre-implemented methods for neural modeling workflows
5.4 Results
5.5 Discussion
6. Conclusion and Outlook
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:76894 |
| Date | 07 December 2021 |
| Creators | Gast, Richard |
| Contributors | Knösche, Thomas R., Möller, Harald E., Deco, Gustavo, Universität Leipzig |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | German |
| Detected Language | English |
| Type | info:eu-repo/semantics/acceptedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 10.1371/journal.pone.0225900, 1932-6203, 10.1162/neco_a_01300, 0899-7667, 10.1523/JNEUROSCI.0094-21.2021, 0270-6474, 10.1103/PhysRevE.104.044310 |
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