 My PhD thesis work was published in Neuron, and was the subject of several online news reports: CNN.com news report Newsweek.com blog article For details regarding adult neurogenesis, see the Scholarpedia article on neurogenesis, which I helped author. We also have recently published review articles in Nature Reviews Neuroscience, Trends in Cognitive Science, and Hippocampus. My research has focused on several areas: New neurons continuously integrate into the adult brain in a region known as the dentate gyrus (DG), which is thought to be critically important in the formation of episodic memories. Potentially thousands of these neurons integrate into this region on a daily basis, suggesting that they may have a significant influence on our abilities to form new memories. Recently, I developed a computational model that helped generate three distinct predictions for what these new neurons contribute to DG function. I am currently working with neurobiologists specializing in animal behavioral tasks and electrophysiology in the development of studies that test these predictions specifically. In addition, I am continuing to investigate neurogenesis from a modeling perspective in order to hopefully reveal other functionally relevant features of this unique process. Why is a computational understanding of neurogenesis important? 2. Effect of network scaling on the validity of computational models Models in computational neuroscience, as in most engineering and science fields, typically simplify the system being studied by reducing complexity. This reductionism includes both simplification based on details (analysts don't model every single stock transaction made to understand the stock market) as well as scale (engineers don't start modeling a bridge or airplane by building a full-sized model). Likewise, in neuroscience, modelers typically look at neural systems by making simplifying assumptions about the details (not all neuron models are channel based, nor do they need to be) and by reducing scale. Regarding this latter point, we are interested in specifically understanding whether building a model with substantially fewer neurons is like understanding a small-scale replica of an airplane, or if it is like trying to understand the whole stock market by looking at a single company. By using cloud computing, via the Amazon Web Services platform, we are directly investigating whether a complex neural network models behave fundamentally differently if they are real-sized (i.e., mouse sized) or reduced-sized (typical models are at least 100x smaller). 3. Effects of neuromodulation on DG function Another form of complexity in the brain that is typically avoided by computational neuroscience is the role of neuromodulation. Neuromodulators such as acetylcholine, serotonin, dopamine, and norepinephrine affect many neural systems in the brain in distinct and unique ways, yet they still represent one of the great unexplored domains of neuroscience. The DG receives substantial inputs from each of these systems, and each appears to have potent effects on the proliferation and/or maturation of new neurons. I am currently exploring ways of integrating existing studies of these modulatory systems with physiology data generated within the Gage laboratory by using my modeling framework to help understand how this modulation affects memory formation and adult neurogenesis. Furthermore, these neuromodulators are increasingly implicated in neurological and psychiatric diseases. The understanding of how neuromodulation affects brain networks is one of my key long-term goals is what I am calling "Translational Computational Neuroscience," which is specifically the development of a computational neuroscience of neurological and psychiatric diseases. I believe that in order to develop effective therapeutic interventions for these disorders, we must have a strong theoretical foundation upon which to study and investigate how the normal brain can experience dysfunction. |