We want to know whether those two signals are sufficiently similar,” Song said. We’ll also have a model of a rat hippocampus navigating a virtual environment, just like the rat is doing, and generating signals. “On the one hand, we have rats navigating their environment and generating all sorts of signals about spatial representation. The modeling framework would then look for discrepancies between this model and the real hippocampus using a “discriminative” machine learning model. One model is the “generative” realistic model of the existing world-in this case, the hippocampus-developed from neuroscience knowledge and hypotheses. The framework uses two models, which must work together to create a more accurate model. Song and his collaborators will be using a machine learning framework known as a generative adversarial network, which until now has been used for image processing. “To me that’s the Holy Grail of neuroscience–how those basic mechanisms work together to create a very complex function,” Song said. Song said that while the hippocampus was one of the better-known areas of the brain in terms of its anatomy and function, there was still a great deal that the research community does not know about how its neurons work in concert to form higher-level cognitive function such as navigation and memory. “In lower-level mammals, the main function of the hippocampus is spatial navigation, and this is the function I will be looking at in this proposal, using rats as the animal model,” he said. It’s the autobiographical memory, as opposed to learning new skills,” Song said. “The main function of the hippocampus in higher-level mammals, such as humans, is episodic memory, especially the memory of your past experiences. The virtual model would then be cross-referenced with a real-life rodent hippocampus using machine learning to ensure its accuracy, in order to better understand the biology of the hippocampus and generate experimentally testable hypotheses about how it works.ĭong Song, research associate professor in the Department of Biomedical Engineering He said that while his previous BRAIN initiative grant was all about collecting data from the brain, the latest project would draw on decades of collected brain data and knowledge to build a computational model that could mimic the function of a real hippocampus. Song was part of a team awarded a $6 million BRAIN Initiative grant in 2020 to develop polymer electrodes to monitor brain signals. The research team will also include Michael Bienkowski, an assistant professor of physiology and neuroscience from Keck School of Medicine of USC. The project has secured a grant of nearly $1.2 million from the NIH Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative: Theories, Models and Methods for Analysis of Complex Data from the Brain.ĭong Song, a research associate professor in USC’s Department of Biomedical Engineering, will lead the hippocampus model project, along with BME post-doctoral researcher Gene Yu (now at Duke University), David Packard Chair in Engineering Ted Berger, and Dean’s Professor of Biomedical Engineering Vasilis Marmarelis. It’s the seahorse-shaped structure in the center of the organ that is responsible for forming our new memories.įor the first time, USC Viterbi School of Engineering researchers will develop a highly-complex computational model of the hippocampus designed to function exactly like the real thing. The hippocampus is one of the most important parts of our brain. The neural connections in a cross section of a mouse’s hippocampus, the area of the brain responsible for memories.
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