Effect of spike-timing dependent plasticity rule choice on memory capacity and form in spiking neural networks

dc.contributor.authorArthur, Derek
dc.contributor.authorUniversity of Lethbridge. Faculty of Arts and Science
dc.contributor.supervisorTatsuno, Masami
dc.date.accessioned2024-01-23T20:05:36Z
dc.date.available2024-01-23T20:05:36Z
dc.date.issued2023
dc.degree.levelMasters
dc.description.abstractThe strengthening of synapses between coactivating neurons is believed to be an important underlying mechanism for learning and memory. Hebbian learning of this type has been observed in the brain, with the degree of synaptic strength change dependent on the relative timing of pre-spike arrival and post-spike emission called spike timing dependent plasticity (STDP). Another important feature of learning and memory is the existence of neural spike-timing patterns. Early work by Izhikevich (2006) argued that STDP spontaneously produces structures known as polychronous groups, defined by the network connectivity, that can produce such patterns. However, studies involving STDP face two important issues: how the STDP rule distributes synaptic weights and not knowing what STDP rule is used in the brain. This highlights the importance of understanding the fundamental properties of different STDP rules to determine their effect on the outcome of computational studies. This study focuses on the comparison of two STDP rules, one used by Izhikevich (2006), add-STDP, that produces a bimodal weight distribution, and log-STDP which produces a lognormal weight distribution. The comparison made is between the number of polychronous groups produced and the number of spike-timing patterns, or cell ensembles, found with another detection method that is applicable to experimental data. The number of polychronous groups found with add-STDP was significantly larger as were their sizes and durations. In contrast, the number of cell ensembles found in log-STDP was considerably larger, however, sizes and lifetimes were comparable. Lastly, the activity of cell ensembles in the log-STDP simulations has a non-trivial relationship with the dynamics of synaptic weights in the network, whereas no relationship was found for add-STDP.
dc.identifier.urihttps://hdl.handle.net/10133/6661
dc.language.isoen
dc.proquest.subject0317
dc.proquest.subject0800
dc.proquest.subject0306
dc.proquestyesYes
dc.publisherLethbridge, Alta. : University of Lethbridge, Dept. of Neuroscience
dc.publisher.departmentDepartment of Neuroscience
dc.publisher.facultyArts and Science
dc.relation.ispartofseriesThesis (University of Lethbridge. Faculty of Arts and Science)
dc.subjectSpike timing dependent plasticity
dc.subjectNeural networks
dc.subjectCell ensembles
dc.subjectSynaptic weights
dc.subjectPolychronous groups
dc.subjectMemory
dc.subject.lcshSynapses
dc.subject.lcshNeural networks (Neurobiology)
dc.subject.lcshDissertations, Academic
dc.titleEffect of spike-timing dependent plasticity rule choice on memory capacity and form in spiking neural networks
dc.typeThesis
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