Twin Cities campus

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Twin Cities Campus

Neuroscience Ph.D.

Neuroscience
Medical School
Link to a list of faculty for this program.
Contact Information
Department of Neuroscience, 6-145 Jackson Hall, 321 Church Street SE, Minneapolis, MN 55455 (612-626-6474; fax: 612-626-6460)
Email: neurosci@umn.edu
Website: http://www.neuroscience.umn.edu
  • Program Type: Doctorate
  • Requirements for this program are current for Spring 2024
  • Length of program in credits: 48
  • This program requires summer semesters for timely completion.
  • NSCI 5551 Cell & Molecular Neurobiology Lab is held at the Itasca Biological Station in Shevlin, Minnesota the first semester of the program.
  • Degree: Doctor of Philosophy
Along with the program-specific requirements listed below, please read the General Information section of this website for requirements that apply to all major fields.
Neuroscience is an interdisciplinary field of inquiry. The objects of this inquiry, the brain and nervous system, are sufficiently complex and unique among biological systems to require experimental and analytical approaches that cross the traditional boundaries of molecular and cell biology, behavioral biology, biochemistry, genetics, pharmacology, physiology, and psychology. In some instances, neuroscientific inquiry may also encompass computer science, information processing, engineering, physics, and mathematics.
Program Delivery
  • via classroom (the majority of instruction is face-to-face)
Prerequisites for Admission
Special Application Requirements:
Applicants whose native language is not English are required to take the TOEFL and obtain a minimum score of 625 (paper) or 107 (Internet); or obtain 6.5 on the IELTS examination. There is no minimum GPA requirement to apply.
International applicants must submit score(s) from one of the following tests:
  • TOEFL
    • Internet Based - Total Score: 107
    • Paper Based - Total Score: 625
  • IELTS
    • Total Score: 6.5
The preferred English language test is Test of English as Foreign Language.
Key to test abbreviations (TOEFL, IELTS).
For an online application or for more information about graduate education admissions, see the General Information section of this website.
Program Requirements
24 credits are required in the major.
24 thesis credits are required.
Plan A: Plan A requires 23 major credits, up to credits outside the major, and 10 thesis credits. The final exam is oral.
This program may be completed with a minor.
Use of 4xxx courses toward program requirements is permitted under certain conditions with adviser approval.
A minimum GPA of 3.00 is required for students to remain in good standing.
At least 2 semesters must be completed before filing a Degree Program Form.
The neuroscience PhD curriculum begins in the summer session with the intensive laboratory course in cellular and molecular neurobiology (NSC 5551), held partially at the Itasca Biological Station and Laboratories. While taking courses, students explore research opportunities in the faculty's laboratories and thereby select a thesis advisor.
Course Credits
All students are required to complete a minimum of 24 course credits and 24 thesis credits. Students must take the following courses or, with DGS approval, substitute courses to meet these minimums. NSC 8321: Career Skills and Understanding Responsibilities as a Neuroscientist will be taken in each semester of the first year and in the spring semester of the second year (0.5 credit per semester).
Required Coursework (12.5 credits)
NSC 5551 - Itasca Cell and Molecular Neurobiology Laboratory (4.0 cr)
NSC 5461 - Cellular and Molecular Neuroscience (3.0 cr)
NSC 5561 - Systems Neuroscience (4.0 cr)
NSC 8321 - Career Skills and Understanding Responsibilities as a Neuroscientist (0.5 cr)
Quantitative/Computational Coursework (3 credits)
Students select one of the following:
NSC 8111 - Quantitative Neuroscience (3.0 cr)
NSCI 5551 - Statistical Foundations of Systems Neuroscience (3.0 cr)
Elective Coursework
Students select ELECTIVE courses from the course options below in order to reach or exceed the minimum 24 course credits for the degree. Substitute courses can be used with DGS approval.
Take 2 or more course(s) from the following:
· BMEN 5411 - Neural Engineering (3.0 cr)
· BMEN 8101 - Biomedical Digital Signal Processing (3.0 cr)
· BMEN 8501 - Dynamical Systems in Biology (3.0 cr)
· BMEN 8502 - Physiological Control Systems (3.0 cr)
· BMEN 8511 - Systems and Synthetic Biology (3.0 cr)
· CSCI 5521 - Machine Learning Fundamentals (3.0 cr)
· CSCI 5523 - Introduction to Data Mining (3.0 cr)
· CSCI 5525 - Machine Learning: Analysis and Methods (3.0 cr)
· HINF 5660 - Applied Causal Discovery (3.0 cr)
· HINF 8220 - Computational Causal Analytics (3.0 cr)
· MATH 5447 - Theoretical Neuroscience (4.0 cr)
· NSC 5462 - Neuroscience Principles of Drug Abuse (2.0 cr)
· NSC 5661 - Behavioral Neuroscience (2.0 cr)
· NSC 8111 - Quantitative Neuroscience (3.0 cr)
· NSC 8211 - Developmental Neurobiology (2.0-4.0 cr)
· NSC 8334 - Laboratory Neuroscience (1.0-3.0 cr)
· NSC 8481 - Advanced Neuropharmaceutics (4.0 cr)
· NSCI 5501 - Neurodegenerative Diseases, Mechanisms to Therapies (3.0 cr)
· NSCI 5505 - Mind and Brain (4.0 cr)
· NSCI 5551 - Statistical Foundations of Systems Neuroscience (3.0 cr)
· PSY 5063 - Introduction to Functional MRI (3.0 cr)
· PSY 5065 - Functional Imaging: Hands-on Training (3.0 cr)
Thesis Credits
Take at least 24 doctoral thesis credits.
NSC 8888 - Thesis Credit: Doctoral (1.0-24.0 cr)
 
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NSC 5551 - Itasca Cell and Molecular Neurobiology Laboratory
Credits: 4.0 [max 4.0]
Grading Basis: S-N or Aud
Typically offered: Every Summer
Intensive lab introduction to cellular and molecular aspects of research techniques in contemporary neurobiology; held at Itasca Biological Station. Electrophysiological investigations of neuronal properties, neuropharmacological assays of transmitter action, and immunohistochemical studies in experimental preparations. prereq: Neuroscience grad or instr consent
NSC 5461 - Cellular and Molecular Neuroscience
Credits: 3.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Lectures by team of faculty, problem sets in important physiological concepts, discussion of original research papers. prereq: NSc grad student or instr consent
NSC 5561 - Systems Neuroscience
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Principles of organization of neural systems forming the basis for sensation/movement. Sensory-motor/neural-endocrine integration. Relationships between structure and function in nervous system. Team taught. Lecture, laboratory. prereq: NSc grad student or instr consent
NSC 8321 - Career Skills and Understanding Responsibilities as a Neuroscientist
Credits: 0.5 [max 2.0]
Grading Basis: S-N or Aud
Typically offered: Every Fall & Spring
Information that falls outside of core neuroscience academic curriculum. Areas of practical value for graduate school and career development. Career skills, writing skills, responsible conduct in research. prereq: Neurscience grad major or instr consent
NSC 8111 - Quantitative Neuroscience
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Principles of experimental design and statistical analysis in neuroscience research. Includes an introduction to computer programming for data analysis using both classic and modern quantitative methods.
NSCI 5551 - Statistical Foundations of Systems Neuroscience
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Spring Even Year
The purpose of this course is to provide the student with a familiarity with the mathematical and statistical techniques to practice contemporary systems neuroscience. Topics are chosen with a focus on current areas of active research, as well as problems that have driven the field over the past twenty years. The class will combine lectures with discussions of important systems neuroscience papers, and will move at a fast pace. It is intended for graduate students and ambitious undergraduates. One major difference between this course and other math and statistics courses is the focus on systems neuroscience. Our examples will come from the Systems Neuroscience field. Our research priorities will come from Systems Neuroscience and our Friday paper discussions will draw exclusively from scholarly papers in Systems Neuroscience.
BMEN 5411 - Neural Engineering
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Theoretical basis. Signal processing techniques. Modeling of nervous system, its response to stimulation. Electrode design, neural modeling, cochlear implants, deep brain stimulation. Prosthetic limbs, micturition control, prosthetic vision. Brain machine interface, seizure prediction, optical imaging of nervous system, place cell recordings in hippocampus. prereq: 3401 recommended
BMEN 8101 - Biomedical Digital Signal Processing
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Signal processing theory for analyzing real world digital signals. Digital signal processing and mathematically derived algorithms for analysis of stochastic signals. Spectral analyses, noise cancellation, optimal filtering, blind source separation, beamforming techniques. prereq: [[MATH 2243 or MATH 2373], [MATH 2263 or MATH 2374]] or equiv
BMEN 8501 - Dynamical Systems in Biology
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Nonlinear dynamics with specific emphasis on behavior of excitable systems (neurons/cardiac myocytes). prereq: Grad student in engineering or physics or math or physiology or neuroscience
BMEN 8502 - Physiological Control Systems
Credits: 3.0 [max 3.0]
Prerequisites: 8101 or equiv
Grading Basis: A-F only
Typically offered: Every Spring
Simulation, identification, and optimization of physiological control systems. Linear and non-linear systems analysis, stability analysis, system identification, and control design strategies, including constrained, adaptive, and intelligent control. Analysis and control of physiological system dynamics in normal and diseased states. prereq: 8101 or equiv
BMEN 8511 - Systems and Synthetic Biology
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Systems/synthetic biology methods used to characterize/engineer biological systems at molecular/cellular scales. Integration of quantitative experimental approaches/mathematical modeling to elucidate biological design principles, create new molecular/cellular functions.
CSCI 5521 - Machine Learning Fundamentals
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Problems of pattern recognition, feature selection, measurement techniques. Statistical decision theory, nonstatistical techniques. Automatic feature selection/data clustering. Syntactic pattern recognition. Mathematical pattern recognition/artificial intelligence. Prereq: [2031 or 2033], STAT 3021, and knowledge of partial derivatives
CSCI 5523 - Introduction to Data Mining
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Data pre-processing techniques, data types, similarity measures, data visualization/exploration. Predictive models (e.g., decision trees, SVM, Bayes, K-nearest neighbors, bagging, boosting). Model evaluation techniques, Clustering (hierarchical, partitional, density-based), association analysis, anomaly detection. Case studies from areas such as earth science, the Web, network intrusion, and genomics. Hands-on projects. prereq: 4041 or equiv or instr consent
CSCI 5525 - Machine Learning: Analysis and Methods
Credits: 3.0 [max 3.0]
Typically offered: Fall Even Year
Models of learning. Supervised algorithms such as perceptrons, logistic regression, and large margin methods (SVMs, boosting). Hypothesis evaluation. Learning theory. Online algorithms such as winnow and weighted majority. Unsupervised algorithms, dimensionality reduction, spectral methods. Graphical models. prereq: Grad student or instr consent
HINF 5660 - Applied Causal Discovery
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Which genes cause cancer? Does cholesterol cause heart attacks? Computational causal discovery (especially from observational data) is a recently developed and developing field at the intersection of statistics and machine learning, with numerous and important untapped applications in scientific and medical research. This course provides a foundation for students to go on to apply causal discovery methods to their own data sets. The focus of this course is on developing the students? ability to identify when and why to use computational causal discovery methods, how to determine what methods are appropriate to use in a given context, and how to interpret and report the results. Students in this course will gain hands-on experience applying causal discovery algorithms, develop an understanding of the computational challenges one faces when using causal discovery algorithms, and learn the best practices for using causal discovery algorithms.
HINF 8220 - Computational Causal Analytics
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Identifying causal relationships and mechanisms is the ultimate goal of natural sciences. This course will introduce concepts and techniques underlying computational causal discovery and causal inference utilizing both observational and experimental data. Example applications of the above mentioned techniques in the domain of health sciences include reconstructing the molecular pathways underlying a particular disease, identifying the complex and interacting factors influencing a mental health disorder, and evaluating the potential impact of a public health policy. The course emphasizes both on the theoretical foundations and the practical aspects of causal discovery and causal inference. Students will gain hands-on experience with applying major causal discovery algorithms on simulated and real data.
MATH 5447 - Theoretical Neuroscience
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Nonlinear dynamical system models of neurons and neuronal networks. Computation by excitatory/inhibitory networks. Neural oscillations, adaptation, bursting, synchrony. Memory systems. prereq: 2243 or 2373 or 2574
NSC 5462 - Neuroscience Principles of Drug Abuse
Credits: 2.0 [max 2.0]
Course Equivalencies: Phcl 5462/Nsc 5462
Typically offered: Periodic Spring
Current research on drugs of abuse, their mechanisms of action, characteristics shared by various agents, and neural systems affected by them. Offered biennially, spring semester of even-numbered years. prereq: instr consent
NSC 5661 - Behavioral Neuroscience
Credits: 2.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Neural coding/representation of movement parameters. Neural mechanisms underlying higher order processes such as memorization, memory scanning, and mental rotation. Emphasizes experimental psychological studies in human subjects, single cell recording experiments in subhuman primates, and artificial neural network modeling. prereq: Grad NSc major or grad NSc minor or instr consent
NSC 8111 - Quantitative Neuroscience
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Principles of experimental design and statistical analysis in neuroscience research. Includes an introduction to computer programming for data analysis using both classic and modern quantitative methods.
NSC 8211 - Developmental Neurobiology
Credits: 2.0 -4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
How neuronal types develop. Emphasizes general mechanisms. Experimental data demonstrating mechanisms. prereq: Neuroscience grad student or instr consent
NSC 8334 - Laboratory Neuroscience
Credits: 1.0 -3.0 [max 10.0]
Grading Basis: S-N or Aud
Typically offered: Every Fall & Spring
Guided research. prereq: Grad NSc major
NSC 8481 - Advanced Neuropharmaceutics
Credits: 4.0 [max 4.0]
Course Equivalencies: CMB 8481/NSc 8481/Phm 8481
Grading Basis: A-F or Aud
Typically offered: Fall Even Year
Delivery of compounds to central nervous system (CNS) to activate proteins in specific brain regions for therapeutic benefit. Pharmaceutical/pharmacological issues specific to direct drug delivery to CNS. prereq: instr consent
NSCI 5501 - Neurodegenerative Diseases, Mechanisms to Therapies
Credits: 3.0 [max 3.0]
Course Equivalencies: Nsci 4501/Nsci 5501
Grading Basis: A-F only
Typically offered: Every Fall
With a rapid increase in population aging in western educated industrialized rich democratic (WEIRD) societies, neurodegenerative disorders such as Alzheimer?s disease have become an alarming health priority due to the current absence of disease-modifying therapies. The objective of this course is to acquire a fundamental appreciation for the most common degenerative disorders of the nervous system as well as to integrate central notions shared across these diseases and emerging concepts in the field.
NSCI 5505 - Mind and Brain
Credits: 4.0 [max 4.0]
Grading Basis: A-F only
Typically offered: Every Spring
This course is intended as an introduction to the new views on the relationship between mind and brain. Over the last several decades, a new view of cognition and neural processing has been developed based on the concepts of algorithm, representation, computation, and information processing. Within this theoretical framework, psychological constructs are computational processes occurring across physical neural systems. We will take a neuroscience and psychological perspective in which the physical neuroscience instantiates but does not diminish the psychological constructs. Although our conceptual framework will be computational, this course will not require or expect any mathematical or computer background. At the completion of this class, you will understand the implications of the physical nature of the brain, how mentation is explicable from physical processes, and how decision-making arises from those same physical processes. Importantly, you will also understand the limitations of current knowledge and the methodologies being used to push those limitations. This class is not intended as a final step in this understanding, but as a first step into these issues. At the conclusion of the class, you should have sufficient understanding to continue more in-depth reading and study in these issues. There are no official prerequisites. However, I have found that students who have EITHER a strong computational background (computer science, mathematics, economics, physics) OR have taken an introductory neuroscience course (e.g. NSCI 2100) have done better in the class than students with no background. However, I have seen students come in with very little background and do well in the class if they engage with the class and work hard.
NSCI 5551 - Statistical Foundations of Systems Neuroscience
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Spring Even Year
The purpose of this course is to provide the student with a familiarity with the mathematical and statistical techniques to practice contemporary systems neuroscience. Topics are chosen with a focus on current areas of active research, as well as problems that have driven the field over the past twenty years. The class will combine lectures with discussions of important systems neuroscience papers, and will move at a fast pace. It is intended for graduate students and ambitious undergraduates. One major difference between this course and other math and statistics courses is the focus on systems neuroscience. Our examples will come from the Systems Neuroscience field. Our research priorities will come from Systems Neuroscience and our Friday paper discussions will draw exclusively from scholarly papers in Systems Neuroscience.
PSY 5063 - Introduction to Functional MRI
Credits: 3.0 [max 3.0]
Grading Basis: A-F only
Typically offered: Every Fall
How to understand and perform a brain imaging experiment. Theory and practice of functional MRI experimental design, execution, and data analysis. Students develop experimental materials/acquire and analyze their own functional MRI data. Lectures/lab exercises. prereq: Jr or sr or grad or instr consent
PSY 5065 - Functional Imaging: Hands-on Training
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Basic neuroimaging techniques/functional magnetic resonance imaging (fMRI). First half of semester covers basic physical principles. Second half students design/execute fMRI experiment on Siemens 3 Tesla scanner. prereq: [3801 or equiv], [3061 or NSCI 3101], instr consent
NSC 8888 - Thesis Credit: Doctoral
Credits: 1.0 -24.0 [max 100.0]
Grading Basis: No Grade
Typically offered: Every Fall & Spring
(No description) prereq: Max 18 cr per semester or summer; 24 cr required