Core and Elective Courses
There is a separate schedule of classes available for CNBC-related courses for the current academic year.
Note: 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.
The CNBC program includes four core course requirements:
- 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. Syllabus available.
- 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.
- NROSCI/MSNBIO 2100 and 2101: Cellular and Molecular Neurobiology (required for students in the Program in Neuroscience.)
2100 - This course is the first component of the introductory graduate sequence designed to provide an overview of cellular and molecular aspects of neuroscience. This course covers protein chemistry, regulation of gene expression, nerve cell biology, signal transduction, development, and neurogenesis in a lecture format.
2101 - This course is the second component of the introductory graduate sequence designed to provide an overview of cellular and molecular aspects of neuroscience. This course covers the electrical properties of neurons, signal propagation in nerve cells, and synaptic transmission.
Prerequisites: A background in basic biology and permission of the instructor are required.
This two-course sequence 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)
- 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.
- 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 four 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 usually offered every fall. Please check current course schedule for availability.
- MATH 3375: Computational Neuroscience Methods. This course will present the fundamentals of neural modeling, with a focus on establishing the computations performed by single neurons and networks of neurons. The aim of the course is to provide students with the necessary knowledge and toolbox from which to simulate neural dynamics within the context of a processing task. Topics to be covered include Hodgkin-Huxley model of a neuron, dendritic integration, reduced neuron models, modeling synaptic dynamics, behavior of small networks of neurons, Weiner analysis of a spike train, spike train statistics, information theory applied to neural ensembles.This course is usually offered every fall. 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 brainsciences 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 other spring (odd years). Please check current course schedule for availability.
Potential Elective Courses
CNBC students are encouraged to take advantage of a wide range of additional courses offered in affiliated departments across the two universities, ranging from molecular biology to judgment and decision making, and encompassing a wide range of methods and approaches. In consultation with their advisors, students choose courses appropriate to their scientific interests. Many of these courses are taught by CNBC faculty, and in some cases the syllabus has been tailored specifically to the CNBC program.
The CNBC publishes a list of available electives prior to the start of each semester. Listed below are some of the courses that may be taken as electives, by department.
Biological Sciences (Carnegie Mellon): NMR in Biomedical Sciences; Molecular Biology of Eukaryotes; The Biology of the Brain.
Computer Science (Carnegie Mellon): Introduction to Artificial Neural Networks; Machine Learning; graduate core course in Artificial Intelligence.
Electrical and Computer Engineering / Biomedical Engineering (Carnegie Mellon): Neural signal processing.
Mathematics (Pitt): Mathematical Neurophysiology; Neural Modeling Seminar; Dynamical Systems in the Plane.
Neurobiology (Pitt): Sensory/Motor Functions of the Cerebral Cortex; Reaching and Grasping; Developmental Neurobiology; Molecular Physiology of Synapses; Issues in Cortical Physiology.
Neuroscience (Pitt): Neurochemistry and Neurotransmission; Biological Bases of Psychiatric Disorders; Biochemistry and Signal Transduction; Seminar in Biophysics. The following undergraduate courses may also be useful for some students: Biological Basis of Memory and Learning; Neurophysiology; Neurochemisty; Developmental Neuroscience; Functional Neuroanatomy; Synaptic Transmission.
Psychology (Carnegie Mellon): Biological Foundations of Behavior; Cognitive Processes and Problem Solving; Cognitive Development; Cognitive Neuropsychology; Psychology of Reading; Perception and Perceptual Development; Language and Thought; Visual Cognition; Functional Neural Circuits; Cognitive Modeling.
Psychology (Pitt): Research Methods in Cognition; Learning and Memory; Perception and Attention; Research Methods in Biophsychology; Psychophysiology; Language and Cognition; Human Cognition: Research Methods; Human Cognition: Language; Human Cognition: Learning and Memory; Cognition and the Brain.
Robotics (Carnegie Mellon): Computer Vision; Advanced Perception; Fundamentals of AI in Robotics and Engineering.
Statistics (Carnegie Mellon): Quantitative Methods in Neuroscience; Statistics for Laboratory Sciences; Experimental Design for Behavioral and Social Sciences; Statistical Methods for Behavioral and Social Scientists; Computational Analysis.