Standard Curriculum

4 mandatory courses

2 major electives also required

2 laboratory rotations required

9 credits are required per semester until comprehensive examination is passed

Total credit requirement: 46

 

Year 1

Fall semester

Neuroscience (includes laboratory component, required, 6 credits)

Graduate Biochemistry (required course, 4 credits)

 

Journal club (1 credit)

NBS seminar series (1 credit)

NBS work-in-progress (1 credit)

Lab rotation (3 credits; 2 rotations are required, timing is optional)

 

Spring semester

Cellular and Molecular Neuroscience (required, 3 credits)

Responsible Conduct in Research (Ethics; required course, 1 credit)

Scientific Writing (strongly encouraged,  2 credits)

 

Journal club (1 credit)

NBS seminar series (1 credit)

NBS work-in-progress (1 credit)

Lab rotation  (3 credits)

 

Year 2

Fall semester

Major Elective (Example: Molecular and Cellular Biology I, 6 credits)

Elective (Examples: Selected topics in the Limbic System, 3cr; Selected Readings in the Limbic System, 1cr; Current Topics in Neuropharmacology, 1cr;  Basic Mechanisms of Clinical Neuroscience, 1cr)

 

Journal club (1 credit)

NBS seminar series (1 credit)

NBS work-in-progress (1 credit)

Lab rotation (3 credits)

 

Spring semester

Major elective (Examples: Molecular and Cellular Biology II, 6 cr; Dendritic Spines: Structure, Function, Plasticity, 2 cr; Computational motor control and neuro-  robotics, 3 cr)

Graduate Statistics (optional, 2 credits)

Elective  (Examples: Directed readings, 1-3 credits; Human immunology, 2 cr; Current topics in Neuropharmacology, 1 cr; Emerging Concepts in medicine, 2 cr; Reverse Genetics, 4 cr)

 

Journal club (1 credit)

NBS seminar series (1 credit)

NBS work-in-progress (1 credit)

Thesis research/ lab rotation

 

Subsequent years

Fall and spring semesters

Journal club

NBS seminar Series

NBS work-in-progress

Thesis research

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Courses

Neuroscience

Time: Registered for in the fall. Course will run from approximately Nov. 10 to Feb. 6.

The course includes neuroanatomy, neurophysiology, and systems and behavioral neuroscience. Students are evaluated with practical and written exams.

6.000 Credit Hours

Required course for NBS students.

 

Graduate Biochemistry

Course director: Greg Gick

Time: Offered annually in the fall semester. Course meets three times per week for 2 hours per session.

Faculty: Gick, Rushbrook, Feinman, Carty, Quadros, and Gintzler

Core course. Topics include proteins, protein purification and analysis, enzymes and kinetics, bioenergetics, carbohydrate chemistry, lipid metabolism, amino-acid metabolism, nucleotide metabolism, metabolic integration, and hormone signaling. Grades are based on the results of four written examinations and one oral presentation. The topic of the oral presentation is selected at random by the instructor from eight assigned topics, all of which must be prepared. There is no required text; individual lecturers suggest a written source of information to supplement the lecture material.

Required course for MCB and NBS students.

 

Cellular and Molecular Neuroscience

Course directors: Peter Bergold and Nick Penington

Time: Switching to spring semester in 2014-2015. Offered annually.

Faculty: Bergold, Penington, Kass, Bianchi, Kubie, Stelzer, Gintzler, and Tiedge

Required course for NBS students.

 

List of class meetings:

Responsible conduct in Research

Time: spring semester

This course is designed to acquaint PhD and MD/PhD candidates in the sciences with the ethical and legal principles and practices that will guide the manner in which they conduct and report scientific research now and in the future. The goals of the course are to provide an ethical framework from which to identify and consider dilemmas arising in the course of their or other’s research, to create an appreciation of the importance and value of ethical principles to science, and to become sensitive to the ethical and legal implications and questions that surface in the pursuit of new and untried scientific discoveries. To assure a better fusion of science and ethics, the course is taught by a team consisting of an attorney/ethicist and a scientist. The ethicist, Professor Herb, provides the continuity and consistency of material while the scientist, a faculty member, brings the scientific perspective, methodology, and context. Experts in areas such as patent law may be invited as guest lecturers. The course is planned to begin at a point that would be most logical—the beginning of a research project—and proceed along the continuum of scientific research: how a project is developed and structured; if and how it gets funded; who gets credit; what, where, and how it gets published; what can go wrong; what the implications of the research may be to human subjects and animal subjects; and what the implications of the research itself may be in a socioeconomic context. (Example: the Human Genome Project.) Instruction is both didactic and interactive. For each session, students are expected to read the assignment, reflect, and write a one-page paper on the material and be prepared to engage in in-depth discussions. The cultural diversity of the student body is not only acknowledged, but special efforts are made to explain differing cultural values.

1.000 Credit Hours

Required course for all students in the School of Graduate Studies.

 

Graduate Statistics

Time: spring semester

Number of credits: 2

Course director : Jay Weedon, Ph.D., Scientific Computing Center. Call X7424 for appointments.

Grades & assessments: Grading will be pass/fail. There will be no formal exams; grades will be based on attendance, participation, and completion of assignments.

Books: There is no textbook. If you’re interested in owning a reference text you may want to consider one of the following (the library doesn't own these but you can inspect my copies):

Wardlaw AC (2000) Practical Statistics for Experimental Biologists, 2nd. ed. NY: Wiley. $28.

Sokal RR & Rohlf FJ (1995) Biometry, 3rd ed. NY: WH Freeman. $93.

Bailey NTJ (1995) Statistical Methods in Biology, 3rd ed. Cambridge UK: Cambridge UP. $42.

Content: General methodological issues; the how, why & when of statistics; SPSS software.

Syllabus:

Part I: Methodology

1. Independent & dependent variables; hypothetico-deductive vs. inductive methods. Nuisance variables & confounding; covariates. Causality vs. association. Types of data. Minimizing experimental variability. Reliability vs. accuracy. Making comparisons. Missing data & selection bias. Censored data. Randomization. Blocking & matching. Dose-response effects; categorizing scales. Numerical precision. Researcher & subject expectation.
2. Samples & populations: replication of experiments. Between-subjects vs. within-subjects factors; split-plot, crossover designs. Interactions among factors. Data normalization; area under curve;. Single-subject designs. Ensuring adequate experimental manipulation. Regression to the mean. Importance of planning an experiment, including an analysis plan; data snooping; planned vs. post-hoc comparisons.

Part II: Statistical Core

1. Data matrix. Distributions. Descriptive statistics. Definition & measurement of “experimental error”. Introduction to SPSS software.
2. Inferential methods: parameters vs. statistics. Hypothesis testing. The meaning of a p-value. Type I & type II errors. Effect size. Statistical vs. scientific significance. Power analysis and sample size. Equivalence studies.

Part III: Statistical Toolbox

1. Comparison of means: t-tests; Wilcoxon tests; analysis of variance; Kruskal-Wallis test.
2. Analysis of multiple independent variables: Factorial ANOVA; mixed models.
3. Correlation & linear regression. Analysis of dichotomous & count data. Analysis of censored data.
4. Analysis of two-way frequency tables: Chi-square test; Fisher’s exact test.

Part IV: Student Research

1. Methodological discussion of students’ ongoing studies.

 

Computational motor control and neuro-robotics

Spring semester

3 credits, major elective for NBS students

Overview: The aim of this course is to teach the student the basics of computational motor control. We will use information from robotics, control theory and neuroscience in order to model the human arm, muscles and sensory apparatus. We will also use simple neural network simulations to model parts of the brain as they learn and adapt to novel dynamical environments. Our focus will be on reaching and pointing movements. When planning a reaching movement we must determine where the target is in external space from visual information. We must then relate this visual information of the external world (extrinsic coordinates) to our own body (intrinsic coordinates) and generate a motor command that brings the hand to the target. We will discuses the neural mechanisms that represent the transformation of information from the external world and between extrinsic and intrinsic coordinates. An underlying theme in our discussions will be how to translate the neural information into robotic motion for the use of a brain machine interface. We will use matlab in order to form simulations of a biomechanical arm as well as neural networks that will control our simulated arm.

 

Textbook: The Computational Neurobiology of Reaching and Pointing.

Authors: Reza Shadmehr and Steven P. Wise

 

Percentage of Final Grade:

Homework and simulations 25%, Weekly quizzes 25%, Midterm project 25%, Final exam 25%

 

1. Introduction to Computational Motor Control and Brain machine
interfacing. What motor learning is and what brain areas are involved. What generates force?

2. Limb stability and feedback control

3. Computing End-Effector location theory and experiment

4. Computing Difference Vectors

5. Coding of movement direction and force

6. Proprioception vs. Vision in motor control

7. Planning to reach or point

8. Internal models of Dynamics

9. Feed forward models and the efference copy

10. Next-State planners and control policies

11. Generalization in motor learning

12. Remapping

13. Signal dependent noise

14. Optimal control

15. Consolidation and motor memory

 

Dendritic Spines: Structure, Function, Plasticity

Course faculty: Henri Tiedge, Ilham Muslimov, Jun Zhong

2 credits, major elective for NBS students

This course covers basic concepts and current literature on structure, function, development, and plasticity of dendritic spines. Emphasis is on critical evaluation of latest research topics with a strong basic understanding of essential concepts, as well as an appreciation of the major questions that drive current research in these areas. The lectures will cover key aspects of spinal structure, function, plasticity, and development, including protein contents, morphological variants, synaptic signaling, regulation of translation, spinogenesis, fast and slow types of spinal plasticity, intra-spinal motility, transduction pathways, adhesion, spino-skeletal dynamics and disease-related variations.

Faculty present lectures and assign relevant research paper(s) for student discussion.

 

Basic Mechanisms of Clinical Neuroscience

Course director: Lisa Merlin

1 credit

Time: Offered annually in the fall semester. Typically starts second week in September. Contact Dr. Lisa Merlin by E-mail for start date.

Faculty: variable

Elective. Joint elective for second-year medical students and graduate students.  Graduate students are expected to attend all sessions. Topics typically include epilepsy, traumatic brain injury, stroke, learning and memory.

 

Electrical Instrumentation

Course director: John N. Carter

Topics include: Review of AC and DC circuits; Frequency Analysis; Grounds and power supply leads, wiring and connection techniques; Semiconductors; Bipolar Transistor; Introduction to Operational Amplifiers; Op-Amp as an amplifier; Instrumentation amplifiers; Operational Circuits; Digital Logic; Filters; Analog Conversion, Phase Lock Loops, Synchronous Detection.

Course includes practical labs in which students learn proper use of the digital multimeter, proper soldering technique, proper grounding technique; build circuits; design and construct amplifiers, and more!

Grade is based on attendance, class participation, lab book, and a 45-min mid-term exam

 

See also courses listed under MCB