Yang, Xinruo
(2024)
The Influence of STDP Asymmetry on the Formation and Stability of Neuronal Assemblies in Spiking Neural Networks.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
This is the latest version of this item.
Abstract
Neuronal assemblies are strongly interconnected groups of neurons that coordinate their activity to perform specific functions, and as such are believed to be fundamental units of neural computation. The formation and stability of these assemblies are influenced by synap- tic plasticity rules, particularly the fine timescale learning induced by spike-timing-dependent plasticity (STDP). This dissertation investigates the role of STDP temporal asymmetry (or causality) in the formation and stability of neuronal assemblies in networks of spiking neu- ron models. The first part of the thesis extends the theoretical framework developed by Ocker and Doiron to study the stability of overlapping neuronal assemblies under symmet- ric and asymmetric STDP rules. While previous work focused on isolated assemblies, this study aims to understand how overlapping assemblies subjected to STDP maintain their distinct identities without merging into one another, inspired by empirical evidence from sensory cortex and hippocampus studies. The second part introduces Somatostatin (SST) neurons into the network model, motivated by recent experimental findings on their unique inhibitory STDP characteristics. The study focuses on how the asymmetric STDP rule from SST to excitatory neurons influences the network, particularly in creating lateral inhibition and further facilitating excitatory neuronal assembly formation. This is contrasted with networks featuring symmetric STDP rules for inhibitory synapses, highlighting the role of synaptic plasticity symmetry in neural connectivity and homeostasis. The mathematical methods used to analyze the model are based on the theory developed by Ocker and Doiron, with an extension to consider the influence of all second-order motifs. This dissertation pro- vides a step towards understanding how the asymmetry of STDP rules shapes the formation and stability of neuronal assemblies, offering insights into the principles underlying neural connectivity and memory representation.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
27 August 2024 |
| Date Type: |
Publication |
| Defense Date: |
25 June 2024 |
| Approval Date: |
27 August 2024 |
| Submission Date: |
31 July 2024 |
| Access Restriction: |
2 year -- Restrict access to University of Pittsburgh for a period of 2 years. |
| Number of Pages: |
227 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Dietrich School of Arts and Sciences > Mathematics |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Spiking Neural Network, Spike-Timing-Dependent Plasticity (STDP), Neuronal Assemblies, Synaptic plasticity, Mean-Field Theory, Recurrent Neural Networks, Inhibitory Plasticity, Somatostatin Neurons, Parvalbumin Neurons, Lateral Inhibition, Overlapping Assemblies, Asymmetric STDP, Computational Neuroscience, Neural Circuit Modeling, Hebbian Learning |
| Date Deposited: |
27 Aug 2024 13:12 |
| Last Modified: |
27 Aug 2024 13:12 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/46836 |
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The Influence of STDP Asymmetry on the Formation and Stability of Neuronal Assemblies in Spiking Neural Networks. (deposited 27 Aug 2024 13:12)
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