The CNBC Graduate Training Program consists of 4 required areas: Core Courses, Brain Bags, Ethics Education, and the CNBC Retreat.
Students are required to complete core courses in Cognitive Neuroscience,Neurophysiology, Systems Neuroscience and Computational Neuroscience. The minimum passing grade for a core course is “B”. Students are expected to complete all of the core courses by the end of their third year. Students are encouraged to take advantage of elective courses when they are offered.
Students are required to give a Brain Bag presentation at some point during their time at the CNBC. Ideally this presentation is given during the 2nd or 3rd year. In order to remain in good standing with the CNBC and be eligible for funding students must present by the end of their 3rd year and also attend 2 Brain Bags per semester.
Ethics education has become an essential component of scientific training programs. Many of our students are involved in experimental work with animals or human subjects, and need to learn about the regulations governing these activities and the moral obligations of scientists toward their subjects. In addition, all scientists must deal with professional issues such as authorship disputes, questions of scientific integrity, and ownership of intellectual property.
Students are expected to meet their ethics training obligation early in their graduate career, normally in the first or second year. There are several options for completing the requirement. Although there is no formal obligation for recurrent training, the CNBC encourages both students and faculty to continue to participate in Ethics Roundtable activities in order to hear new perspectives and keep abreast of recent developments.
The CNBC Retreat happens every year. The goal of our retreat is to foster scientific and social interactions among faculty, post-docs, and students affiliated with the CNBC. The program includes a full agenda of scientific presentations and discussions, as well as other informational, social, and recreational events. Retreat attendance is a required part of the CNBC program, and the CNBC Education Committee has adopted a policy that students must participate in the retreat each year to remain in good standing. However, we realize that sometimes a scheduling conflict makes attendance difficult, and therefore, each student will receive one “opt-out”. That is, a CNBC student may pick one year when they do not attend the retreat. We encourage you to save this for when you really need it, as additional opt-outs will not be available. Students who are not in good standing will not be eligible for funding.
Cognitive Neuroscience: this requirement is satisfied by CMU Psych 85-765 / Pitt NROSCI 2005: Cognitive Neuroscience. The course focuses on human sensory and cognitive processing from the perspective of modern neuroscience. Topics include sensation, perception, attention, memory, language and decision making in normal and pathological conditions. Various psychiatric and neurologic disorders (e.g., autism, schizophrenia and stroke) are discussed in terms of their effects on cognitive function. Research methodologies including evoked potentials, depth electrodes, imaging (PET, MRI), neural behavioral assessment and modeling are examined. An important focus is the relationship between neurophysiological data and information processing models of cognition. This course is usually offered every fall. Please check current course schedule for availability.
Neurophysiology: an introduction to the biophysics of excitable membranes and basic cellular neurophysiology, including resting and action potentials, the electrophysiology of synaptic transmission and integration of synaptic inputs. This requirement can be satisfied by any of:
- 03-762: Advanced Cellular Neuroscience. This course is a graduate version of 03-362. Students will attend the same lectures as the students in 03-362, plus an additional once weekly meeting. In this meeting topics covered in the lectures are addressed in greater depth, often through discussions of papers from the primary literature. Students will read and be expected to have an in depth understanding of several classic papers from the literature including work by Hodgkin and Huxley on action potentials and by Katz and Eccles on synaptic transmission. Generation and use of genetically modified animals also will be discussed. Performance in this portion of the class will be assessed by supplemental exam questions. Prerequisites: 03-121 This course is usually offered every fall. Please check current course schedule for availability.
- BIOENG 2585: Quantitative Cellular Neuroscience. This course is designed to be a comprehensive introduction to cellular neuroscience for engineers. Modern cellular neuroscience is an interdisciplinary field that seeks to understand the function of single cells and populations in the context of the thinking brain. This course provides a survey of cellular brain science ranging from molecules to simple neural circuits. In addition to principles and theory this class will also cover basic quantitative concepts and provide opportunity to analyze real-life data from molecular and cellular neural engineering. In the context of each cellular brain function we will also address cellular dysfunction with translational engineering applications to neurological brain disease.
- NROSCI/MSNBIO 2100: Cellular and Molecular Neurobiology (required for students in the Program in Neuroscience.) This course is usually offered every fall. Please check current course schedule for availability. Syllabus available.
- INTBP 2000/2005: Foundations of Biomedical Science (for MD/PhD students only)
Systems Neuroscience: This requirement can be satisfied by either of the following courses. For MD/PhD students, the systems neuroscience requirement is satisfied by taking an equivalent course offered by the medical school.
- 03-763: Systems Neuroscience. This course is a graduate version of 03-363. Students will attend the same lectures as the students in 03-363, plus an additional once weekly meeting. In this meeting, topics covered in the lectures will be addressed in greater depth, often through discussions of papers from the primary literature. Students will read and be expected to have an in depth understanding of several classic papers from the literature as well as current papers that illustrate cutting edge approaches to systems neuroscience or important new concepts. Use of animals as research model systems will also be discussed. Performance in this portion of the class will be assessed by supplemental exam questions as well as by additional homework assignments.This course is usually offered every spring. Please check current course schedule for availability.
- BIOENG 2586: Quantitative Systems Neuroscience. Systems neuroscience is the field that attempts to relate the activity of populations of neurons to perception, cognition, and behavior. This course examines major scientific results in systems neuroscience, and the computational principles of brain function they illustrate. Neuroscience topics include sensory processing and motor control. Computational principles include signals and systems, statistics, machine learning, and control theory. Engineering applications in neural prosthetics will be discussed. Course format consists of interactive lectures, student-led discussions of important publications, guided analysis of neuroscience data, and designing an original set of experiments.
- NROSCI 2102/2103: Systems Neurobiology. The course focuses on the integrative functioning of the nervous system. It includes a neuroanatomy laboratory section using wet specimens, slides and atlases of human and animal brains. Topics include somatosensory, auditory, vestibular, visual and motor systems, sleep and arousal, and learning and memory. During some of the systems sections, students read and discuss selected journal articles. Several CNBC faculty members are instructors for this course. This course is usually offered every spring. Please check current course schedule for availability.
Computational Neuroscience: Any of these courses will satisfy the computational neuroscience core requirement. Students may take whichever one best meets their needs.
- Psych 85-719: Introduction to Parallel Distributed Processing. This course explores connectionist (or artificial neural network) models of cognitive and linguistic behavior. Students use PDP simulator software to experiment with various models. This course is usually offered every spring. Please check current course schedule for availability.
- CS 15-883: Computational Models of Neural Systems. This course examines models of information processing in brain areas such as the hippocampus, cerebellum, basal ganglia, thalamus, and visual cortex. The course also looks briefly at synaptic learning rules and models of invertebrate learning. Students will have the opportunity to experiment with Matlab implementations of some of the models discussed in class. This course is offered every other fall, in odd numbered years. Please check current course schedule for availability.
- 36-759: Statistical Models of the Brain
This course should be of interest to anyone wishing to see the way statistical ideas play out within the brain sciences, and it will provide a series of case studies on the role of stochastic models in scientific investigation. Statistical ideas have been part of neurophysiology and the brain sciences since the first stochastic description of spike trains, and the quantal hypothesis of neurotransmitter release, more than 50 years ago. Many contemporary theories of neural system behavior are built with statistical models. For example, integrate-and-fire neurons are usually assumed to be driven in part by stochastic noise; the role of spike timing involves the distinction between Poisson and non-Poisson neurons; and oscillations are characterized by decomposing variation into frequency-based components. In the visual system, V1 simple cells are often described using linear-nonlinear Poisson models; in the motor system, neural response may involve direction tuning; and CA1 hippocampal receptive field plasticity has been characterized using dynamic place models. It has also been proposed that perceptions, decisions, and actions result from optimal (Bayesian) combination of sensory input with previously-learned regularities; and some investigators report new insights from viewing whole-brain pattern responses as analogous to statistical classifiers. Throughout the field of statistics, models incorporating random “noise” components are used as an effective vehicle for data analysis. In neuroscience, however, the models also help form a conceptual framework for understanding neural function. This course will examine some of the most important methods and claims that have come from applying statistical thinking. This course is usually offered every fall. Please check current course schedule for availability.