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IGMN Requirements


Students interested in applying for admission into the IGMN program should contact Rebecca Prosser (

  1. The student’s home department (i.e., the department in which the student is currently pursuing an advanced degree) must have an approved degree program with the IGMN Program Committee. That program will specify the courses chosen from the IGMN approved list that are considered appropriate by the student’s home department.
  2. The student’s Admission to Candidacy form must contain all courses required for the chosen Neuroscience degree program set off in a group and labeled as “Courses Required for the Minor in Neuroscience.” It may be that a student does not decide to apply for participation in the Program until he/she has already completed one or two approved Neuroscience courses. In that case, the student’s major professor should file a program change with the cooperating departments and assist the student in obtaining a IGMN program faculty member to serve on the student’s committee.
  3. The student’s graduate committee must include one member of the IGMN prograM faculty.


Program requirements are for graduate students pursuing either a Masters or PhD degree. Options consist of courses/training across multiple complementary disciplines (Neurobiology, Neuropsychology, and Engineering, plus a Workshop in Computational Neuroscience. The specific elective courses chosen must be reviewed and approved by the Program Committee. Specific program requirements for a given academic unit are available from the College Representative or the Chair of the Program Committee.

  • The minor in Neuroscience requires 9 hours total (3 courses), with 3 hours (1 course) from Neurobiology (BCMB 550), 3 hours (1 course) from Neuropsychology (PSYC 524, PSYC 525, or PSYC 527) and 3 hours (1 course) from the list of approved electives.
  • The minor in Neuroscience also requires students to complete the Workshop on Computational Neuroscience, which will be offered at least 1/year.
Required Workshop
Workshop on Computational Neuroscience The workshop is designed to provide a survey of computational neuroscience.  Focus topics include an introduction to the concepts, tools, and methods for advanced computing applied to the field of neuroscience. Participants will be introduced to the use of Python, R, and Matlab, and will apply these tools to example datasets.  The workshop will also provide a survey of omics datasets and data analysis methods with which to explore them.
Required Neurobiology Course
BCMB 550 Advanced Concepts in Neurobiology/Physiology Concepts related to neurobiology/ physiology with information taken from current literature. Predominantly lecture format with student participation. Specific subject area to be announced.
Neuropsychology Electives (choose 1)
PSYC 524 Brain and Behavioral Development Survey of experience-dependent changes in brain and behavior development.
PSYC 525 Psychopharmacology Effects of psychoactive drugs on mood and behavior, emphasizing the mechanisms of drug action on neurotransmitter systems. Topics include the relationship between behavior and endogenous neurochemical activity, therapeutic agents used to treat mental disorders, and drugs of abuse.
PSYC 527 Advances in Behavioral Neuroscience Advanced analysis of the structure and function of the nervous system with an emphasis on neurobiological mechanisms controlling behavior.
Electives (choose 1)
BME 503 Biological Numerical Methods The complexity of biomedical systems presents significant mathematical challenges. Therefore, modeling and simulation are essential to understanding these systems and numeric tools are the basis/means for accomplishing this. This course is a survey of essential numeric tools routinely applied to solving biomedical engineering problems that are implemented primarily via Matlab scientific programming language.
BME 511 Biotransport Processes Introduction of an integrative set of computational problem solving tools providing numerical foundations for Biomedical Engineering. This course will apply numerical methods to applications in systems, organs, cellular, and molecular systems.
BME 574 Medical Imaging Introduction is provided of the basic principles of image acquisition, formation, and processing, along with clinical applications of different imaging modalities for predicting disease outcome and treatment evaluation. Clinical site visits provide experience with imaging modalities covered in class.
BME 580 Computational Cell Biology Introduction to dynamical modeling in molecular and cellular biology. Topics include: models and analysis of neurons and other excitable systems, fast and slow time scales, whole-cell models, intercellular communication, cell cycle controls, molecular motors, and stochastic and nonlinear dynamics in biological systems.
BME 674 Multidimensional Medical Imaging Analysis Fundamentals of multidimensional image analysis, computer vision, machine learning, deformable registration, statistical and multidimensional modeling and their applications in analysis and characterization of both functional imaging (FMRI, DTI) and anatomical imaging (CT, MRI and Ultrasound).
COSC 526 Introduction to Data Mining A comprehensive introduction to the field of data mining. Topics covered include data preprocessing, predictive modeling (e.g., decision trees, SVM, Bayes, K-nearest neighbors, bagging, boosting), model evaluation techniques, clustering (hierarchical, partitional, density-based), classification, association analysis, and anomaly detection. Case studies from text mining, electronic commerce, social science, and bioinformatics are covered. All programming projects are student-designed (no standard packages permitted).
COSC 527 Biologically-inspired Computation Recent developments in computational methods inspired by nature, such as neural networks, genetic algorithms, evolutionary programming, ant-swarm optimization, artificial immune systems, swarm intelligence, cellular automata, multi-agent systems, cooperation, and competition.
COSC 528 Introduction to Machine Learning Theoretical and practical aspects of machine learning techniques that enable computer systems to learn from experience. Methods studied include concept learning, decision tree learning, neural networks, Bayesian learning, instance-based learning, genetic algorithms, rule learning, analytical learning, and reinforcement learning.
COSC 594 Computational Cognitive Neuroscience (special topics course, proposed as regular course, 521) Survey of computational cognitive neuroscience. The focus is on the neuroscience of cognitive processes, including perception, categorization, memory, language, action, and executive control. Course work makes use of computer simulations of neural networks to model cognitive processes and to test hypotheses about their neural implementations

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