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Biologically motivated reinforcement learning in spiking neural networks
I consider the problem of Reinforcement Learning (RL) in a biologically feasible neural network model, as a proxy for investigating RL in the brain itself. Recent research has demonstrated that synaptic plasticity in the higher regions of the brain (such as the cortex and striatum) depends on neurom...
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| Format: | Thesis |
| Language: | English |
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Department of Mathematics and Applied Mathematics
2023
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| _version_ | 1867613284654383104 |
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| access_status_str | Open Access |
| author | Rance, Dean |
| author2 | Shock, Jonathan |
| author_browse | Rance, Dean Shock, Jonathan |
| author_facet | Shock, Jonathan Rance, Dean |
| author_sort | Rance, Dean |
| collection | Thesis |
| description | I consider the problem of Reinforcement Learning (RL) in a biologically feasible neural network model, as a proxy for investigating RL in the brain itself. Recent research has demonstrated that synaptic plasticity in the higher regions of the brain (such as the cortex and striatum) depends on neuromodulatory signals which encode, amongst other things, a response to reward from the environment. I consider which forms of synaptic plasticity rules might arise under the guidance of an Evolutionary Algorithm (EA), when an agent is tasked with making decisions in response to noisy stimuli (perceptual decision making). By proposing a general framework which captures many proposed biologically feasible phenomenological synaptic plasticity rules, including classical SpikeTime-Dependent Plasticity (STDP) rules and the triplet rules, and rate-based rules such as Oja's Rule and BCM rules, as well as their reward-modulated extensions (such as Reward-Modulated Spike-Time-Dependent Plasticity (R-STDP)), I allow a general biologically feasible neural network the ability to evolve the rules best suited for learning to solve perceptual decision-making tasks. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/37753 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:33:41.762Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Department of Mathematics and Applied Mathematics |
| publisherStr | Department of Mathematics and Applied Mathematics |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/37753 Biologically motivated reinforcement learning in spiking neural networks Rance, Dean Shock, Jonathan applied mathematics I consider the problem of Reinforcement Learning (RL) in a biologically feasible neural network model, as a proxy for investigating RL in the brain itself. Recent research has demonstrated that synaptic plasticity in the higher regions of the brain (such as the cortex and striatum) depends on neuromodulatory signals which encode, amongst other things, a response to reward from the environment. I consider which forms of synaptic plasticity rules might arise under the guidance of an Evolutionary Algorithm (EA), when an agent is tasked with making decisions in response to noisy stimuli (perceptual decision making). By proposing a general framework which captures many proposed biologically feasible phenomenological synaptic plasticity rules, including classical SpikeTime-Dependent Plasticity (STDP) rules and the triplet rules, and rate-based rules such as Oja's Rule and BCM rules, as well as their reward-modulated extensions (such as Reward-Modulated Spike-Time-Dependent Plasticity (R-STDP)), I allow a general biologically feasible neural network the ability to evolve the rules best suited for learning to solve perceptual decision-making tasks. 2023-04-17T13:50:30Z 2023-04-17T13:50:30Z 2022 2023-04-17T13:49:44Z Master Thesis Masters MSc http://hdl.handle.net/11427/37753 eng application/pdf Department of Mathematics and Applied Mathematics Faculty of Science |
| spellingShingle | applied mathematics Rance, Dean Biologically motivated reinforcement learning in spiking neural networks |
| thesis_degree_str | Master's |
| title | Biologically motivated reinforcement learning in spiking neural networks |
| title_full | Biologically motivated reinforcement learning in spiking neural networks |
| title_fullStr | Biologically motivated reinforcement learning in spiking neural networks |
| title_full_unstemmed | Biologically motivated reinforcement learning in spiking neural networks |
| title_short | Biologically motivated reinforcement learning in spiking neural networks |
| title_sort | biologically motivated reinforcement learning in spiking neural networks |
| topic | applied mathematics |
| url | http://hdl.handle.net/11427/37753 |
| work_keys_str_mv | AT rancedean biologicallymotivatedreinforcementlearninginspikingneuralnetworks |