Bridging together two highly in-demand courses, Horizon Academic is proud to announce a new research track offering in Computational Neuroscience for the summer of 2023. If you’re thinking about conducting independent research this summer as a high school student, we encourage you to consider applying for this new offering. An Assistant Professor of Neuroscience from the University of Chicago will be leading the course. Please feel free to contact us at firstname.lastname@example.org if you'd like to hear more about the instructor's background!
The Computational Neuroscience course is primarily aimed at high school students that are interested in learning how the brain processes information and how we can understand parallels between organic and computational mechanisms for processing information, including neural networks, image recognition, and the process of creating predictions. Neuroscience is the study of the brain, and computational neuroscience divides this study into three subspecialties: neural coding, biophysics of neurons, and neural networks.
The course will start with an introduction to the structure and function of the central nervous system, and then include a study of the neurophysiology of the neuron, electrophysiological approaches to record from neurons, as well as mathematical and computer-based models that help explain existing biological data. We then introduce the basic computational methods of the brain at the cellular level and the network level, with the aim of explaining what nervous systems do and how they function. Basic techniques of modeling biophysics, excitable membranes, small network and large-scale network systems will be introduced. The range of topics include simulations of electrical properties of membrane channels, single cells, neuronal networks, learning and memory models, and models of synaptic transmission, thereby providing a theoretical framework that encapsulates our emerging understanding of the sensory, motor, and cognitive functions of the brain. One of the course’s main goals is to provide students with a broad overview of the many practical applications in the field of computational neuroscience and review neuroengineering methods and technologies that enable the study of and therapeutic solutions for diseases of the brain or damage to the CNS, particularly for research or clinical application in the neurosciences.
Some sample research topics students are free to choose from include:
1. How can the basic cellular and network-level organization of neurons in selected systems be defined?
2. How do the properties of cells that make up the nervous system, including the propagation of electrical signals used for cellular communication, relate to their function in organized neural circuits and systems?
3. How are biophysical models of neural systems that emulate electrical behavior of neurons constructed?
4. How can mathematical analyses of data recorded during neurophysiology experiments be performed to describe the principles of neural information coding in sensory and motor systems?
5. What hypotheses can be formulated by captured mathematical models, as possible explanations for observed relationships between experimental outcomes and manipulations?
6. What are the principles of electrophysiological techniques and imaging technologies?
7. What are the applications of neural engineering in sensory, motor, neurological and mental disorders?
8. What are the principles, methodologies and applications of the main engineering techniques used to study and interact with neural systems?
9. How can intracellular recordings be carried into the lab efficiently with all its components — from handing the animal to preparing solutions, slicing the brain and patching onto cells?