Cellular, Computational and Cognitive Neuroscience

Course Directors: David W. Tank and Michael Berry, Princeton University
Laboratory Directory: Alan Gelperin, Princeton University
Course Dates: June 10, 2018 – July 6, 2018
Application Deadline: March 1, 2018

This intensive new course, Cellular, Computational and Cognitive Neuroscience (C3N) is designed to emphasize two major ways that scientists trained in the physical and information sciences contribute to the advance of neuroscience. The first way is through applying quantitative thinking to neuroscience; typically this quantitative thinking has been honed in the courses students have taken during their previous instruction in classic disciplines like physics and mathematics and it helps expand brain research beyond qualitative descriptive science. This is increasingly important as modern neuroscience moves more and more towards the study of neural circuits in vivo during behavior.  Intuition about how neural circuits and their cellular elements actually work in vivo during behavior is often wrong, in part because understanding the dynamics of highly-nonlinear elements interacting in networks is notoriously difficult. The second way that physical and information sciences-trained students can contribute to neuroscience is through the development of novel neurotechnologies. From patch recording to optical imaging, there are numerous historical examples of physicists and engineers developing disruptive new technologies that gain widespread use in modern neuroscience.  With the advent of large-scale neural recording with microelectrode arrays and optical sensors, statistical analysis of large datasets of neural activity patterns presents a tremendous opportunity for contributions by statisticians, mathematicians, and computer scientists. Our previous intensive summer course, Biophysics and Computation in Neurons and Networks, emphasized the intellectual contributions of quantitative thinking. Our new course, “Cellular, Computational and Cognitive Neuroscience,” retains this emphasis but adds a new dimension: more instruction in large scale recording methods, ranging from large scale electrode and optical recording (and optogenetic stimulation) to mathematical analysis of neural dynamics within the datasets produced by these methods. These new topics will be represented in course lectures, exposure to novel neurotechnologies currently operational in faculty research laboratories, and new laboratory exercises that represent the state of the art in large-scale recording with multi-electrodes (tetrodes, silicon probes, and arrays) and optical imaging. Similarly, lectures will discuss and laboratory visits will demonstrate more advanced methods in human brain imaging like multi-variate pattern analysis (MVPA) and diffusion-weighted MRI for tract tracing (DWMRI).

This course will introduce students with quantitative training in the physical sciences, mathematics, engineering, or computer science to the concepts and research methodologies of modern neuroscience. Topics covered will range from cellular biophysics to systems neuroscience, including core methods of electrophysiology as well as imaging methods for the study of single neurons, networks of neurons, and human brain dynamics during execution of behavioral computations. The course is unique in its focus on neural dynamics at several scales of complexity – cells, circuits, intact brains – and the combination of didactic lectures and laboratory exercises, including cellular biophysics, synaptic interactions and plasticity in neuronal networks, and fMRI imaging of targeted brain regions in human subjects. The capstone of this course will be one-week student-designed research projects integrating concepts and methodologies encountered during the initial formal lectures and laboratory exercises. Course work will include morning lectures and tutorials and laboratory exercises selected to complement and extend the themes presented in morning lectures.

Lectures and laboratory exercises will show students how to think rigorously about a central set of core issues in modern neuroscience, make clear the importance of integrating across multiple levels of analysis, and provide examples of the analysis of quantitative neural data that reveal commonalities in the underlying dynamical processes. The course will provide all students, whether primarily interested in theory or primarily interested in experimental analysis, with a first-hand understanding of the strengths and weaknesses of the experimental methods and data underlying key concepts in cellular and systems neuroscience. Students will also have a substantive research experience during the final week of the course to foster communication and creative thinking about a laboratory project of special interest to each student.

A central tenet underlying this course is that neuroscience is an extremely rich source of important and tractable problems that demand the synergistic interaction of quantitative theory and experiment for their solution. These fundamental questions in modern neuroscience span the gamut of complexity from computation in single dendrites to the biophysical basis of consciousness and executive function in the human brain. These topics have provided fertile ground for entry into neuroscience by students with quantitative training in cognate scientific disciplines such as mathematics, physics, engineering, and computer science, as represented by several of the faculty teaching the course. Furthermore, progress on these critical questions will be augmented by recruiting students into neuroscience with prior expertise in these cognate disciplines. We anticipate that the course will contribute to this intellectual migration, which has been and continues to be of vital importance to the growth of modern neuroscience.

This course is aimed at students in the physical sciences, mathematics, engineering, chemistry, and computer science who give evidence of commitment to a career in research and wish to explore the opportunities for research at the interface of their discipline with neuroscience. Graduate students, postdoctoral fellows and early-stage faculty are particularly appropriate candidates, although especially well qualified candidates at other career stages will also be considered. Applicants should have a strong background in one of these areas and an in-depth exposure to one or more complementary disciplines. The course is limited to 16 students, and is supported by a Burroughs Wellcome Fund Interfaces Short Course Award and an award from the National Institute of Mental Health (1R25MH104163).