Neural Systems
The subject of neuroscience is truly multidisciplinary. It encompasses such disciplines as anatomy, physiology, molecular and cell biology, psychology, and, more recently, computer science and physics. The most interesting discoveries in neuroscience are made at the crossroads of these disciplines, and neuroscience research at its forefront is often driven by interdisciplinary collaborations. At CIRCS, professors Donald O’Malley, Latika Menon, and Armen Stepanyants are actively involved in basic and applied neuroscience research. Research projects include computational modeling and theoretical studies of learning and memory formation in the brain, unraveling neural circuits in zebrafish and mammalian brain, building neurodevises, and developing tools for neuroimage processing. Two of these projects are outlined below.
Professor Menon, in collaborations with Donald O’Malley and Armen Stepanyants, is developing a nano-bio-device for stimulation and detection of electrical activity in a cultured neural network at subcellular resolution. This “neurochip” consists of an array of conducting gold nanowires integrated with electrodes at the one end and directly interfaced with cultured neuronal cells at the other end. The neurochip will make it possible to address important basic questions related to biophysical processes in single neural cells and learning and memory formation in cultured neuronal networks. The technology being developed has the potential for advanced clinical applications. The neurochip can be used to interface the nervous system and to provide finely tuned control necessary for next-generation brain implants and prosthetic devices. This is a highly interdisciplinary project encompassing several areas of research ranging from electrochemistry to microchip engineering to experimental and theoretical neuroscience.
Quantitative methods of analysis of neural circuits rely on large datasets of neurons reconstructed accurately in three dimensions. Due to the complexity of neuron morphology, large datasets of reconstructed neurons must be generated with automated algorithms. Professor Stepanyants is developing algorithms and a software tool to automate neuron reconstruction from confocal microscopy stacks of images. The automated reconstruction tool will make it possible to extract quantitative information from a wide variety of neuron labeling and imaging techniques.