Upcoming Events
Neuroscience Student Research Update: "Developing Machine Learning Algorithms to Investigate the Maturation of Hippocampal Networks"
Sep 23, 2024, 4:30 - 5:30 PM
KB 229
IPN PhD Student Diego Gonzalez will present his research progress "Developing Machine Learning Algorithms to Investigate the Maturation of Hippocampal Networks."
Abstract
The ability to recall contextual memories develops during the late postnatal period in rodents and humans after birth. Infant humans and juvenile rats are able to retrieve contextual memories immediately after encoding but long-term memory is not displayed. By two to four years of age in humans (three weeks of age in rodents), long term contextual memories are readily retrievable and spatially strategies become available to navigate to known goal locations. This developmental change in long-term memory retrieval coincides with several developmental changes in the hippocampus, including the maturation of hippocampal network activity, in particular slow gamma oscillations. However, there has been limited work in juvenile rodents linking spatial navigation to particular network oscillations in the hippocampus. Thus, it remains to be seen how changes in hippocampal oscillations relate to the onset of adult-like spatial navigation during this critical developmental period. Additionally, traditional methods of analyzing network activity may suffer flaws due to inherent assumptions regarding sinusoidal periodicity of hippocampal network oscillations that may not be accurate. Machine Learning Models (MLMs) that do not rely on these assumptions may prove to be more valid and reliable. By focusing on dimension reduction, models such as Diffusion Mapped Delay Coordinates (DMDC) and Singular Value Deconstruction (SVD) avoid the assumptions inherent to more traditional analyses and may provide novel insights into underlying signal properties. Therefore, the goal of my project is to determine if developmental changes to slow gamma oscillations are related the maturation of spatial memory retrieval in juvenile rodents and compare traditional analyses to MLMs. Results will not only improve our understanding of cognitive development but also will shed light on the biological underpinnings of various adult neuropsychiatric diseases, including schizophrenia and Alzheimer’s disease.