The hippocampus has been the focus of memory research for decades. While the functional role of this structure is not fully understood, it is widely recognized as being vital for rapid yet accurate encoding and retrieval of associative memories. Since the discovery of adult hippocampal neurogenesis (AHN) in the dentate gyrus (DG) by Altman and Das in the 1960s, many theories and models have been formulated to explain the functional role it plays in learning and memory. These models postulate different ways in which new neurons are introduced into the DG and their functional importance for learning and memory. Few, if any, previous models have incorporated the unique properties of young adult-born dentate granule cells (DGCs) and their developmental trajectory. In this thesis, we propose a novel computational model of the DG that incorporates the developmental trajectory of these DGCs, including changes in synaptic plasticity, connectivity, excitability and lateral inhibition, using a modified version of the restricted boltzmann machine (RBM). Our results show superior performance on memory reconstruction tasks for both recent and distally learned items, when the unique characteristics of young DGCs are taken into account. The unique properties of the young neurons contribute to reducing retroactive and proactive interference, at both short and long time scales, despite the reduction in pattern separation due to their hyperexcitability. Our replacement model is subsequently extended to support learning dependent regulation of neurogenesis and apoptosis, using a convergence based approach to network growing and pruning. This hybrid additive and replacement model provides a more realistic and flexible approach to investigating the role of neurogenesis regulation in learning and memory. Finally, we incorporate the dentate gyrus model into a full hippocampal circuit to assess cued recall performance. Once again, our neurogenesis model shows decreased proactive and retroactive interference. / Thesis / Master of Science (MSc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/20666 |
| Date | January 2016 |
| Creators | Finnegan, Rory |
| Contributors | Becker, Suzanna, Neuroscience |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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