Curriculum and Program Requirements
Fast Track Master's Program Information
The Fast Track Masters Program is available only to graduates of UMass Amherst who have worked as undergraduates in the lab of an NSB faculty member. Learn more about eligibility, graduate school admission and course requirements.
First day of classes: Tuesday, September 2, 2014
Last day of classes: Friday, December 2014
UMass Dean of Students Office Academic Honesty Policy.
NEUROS&B 617 - Molecular, Cellular and Developmental Neurobiology
class schedule #74295
Note: Beginning with the Fall 2011 semester, the catalog number for the core course, NEUROS&B 692C is being changed to NEUROS&B 617. Proposed Instructors: Eric Bittman, James Chambers, Abbie Jensen.
The objective of this 3 credit course is to provide NSB and MCB graduate students with the background necessary to understand the molecular and cellular proceses underlying brain development and neural functioning. The course brings together a number of faculty who have both training and expertise in the topics covered. An understanding of molecular and cellular neurobiology and neural development is becoming increasingly important, especially with the advent of transgenic animals and their use in a wide range of research fields. Textbook: Principles of Neural Science, Author: Kandel et al, Publisher: McGraw Hill, Edition: 5, Year Published: 2013
Grading: 20% Attendance, participation and presentations 80% Two exams: one at midterm and one during finals week.
NEUROS&B 696, Independent Study
class schedule # 74289
NEUROS&B 796, Independent Study
class schedule # 74293
By Arrangement with Faculty Sponsor
Independent student research in neuroscience and behavior. The work is supervised by a faculty sponsor who determines direction of the project, reports required, grade and credit awarded. The project may consist of laboratory research, library research, or some combination of the two. Credit is variable (1-6 credits) and independent study may be repeated each semester. May be taken for a letter grade or graded Satisfactory (SAT). A SAT is similar to the undergraduate Pass (P) and is defined as passing for graduate credit. The SAT can be used toward graduation but does not calculate into the GPA (grade point average). Students signing up for their first independent study should select NSB 696; for subsequent independent study credits, select NSB 796.
NEUROS&B 699, Master's Thesis
class schedule #74292 (for NSB fast track master students and terminal master's students only)
Independent research and writing of master's thesis. Research carried out and reported under supervision of students research advisor as partial fulfillment of requirements for a Master of Science degree in Neuroscience and Behavior. No more than 10 credits may be applied towards a M.S. degree in NSB. Minimum credit, 1; maximum, 10.
NEUROS&B 899, Ph.D. Dissertation
class schedule #74294
Variable Credits 1-9 credits
NSB doctoral students may not register for NSB 899 until the doctoral comprehensive examination is passed. At this time the student should have chosen a dissertation topic and the Dissertation Committee should be formed by the student in consultation with his/her advisor. The committee must consist of at least four members of the graduate faculty, from at least two different departments, and including at least three NSB core faculty members. Committee members will be available for advising and consultation throughout the planning, execution, and writing of the dissertation.
GRADSCH 999, Continuous Enrollment
Graduate students not enrolled for any course credits but who are candidates for a degree, must pay a program fee each semester (excluding summer terms) for continuous registration until the degree for which the student has been accepted has been formally awarded. Deadline for enrollment under this option is the end of the add/drop period -Monday, September 15. Use SPIRE registration #77699 and the Bursar's Office will bill for the $275.00 Program Fee. This Bursar's bill will be due around mid-October. Any student who does not pay this fee by the deadline date and later seeks readmission or applies for graduation, shall pay the accumulated program fees plus a readmission fee of $125.00.
BIOLOGY 523, Histology
lecture schedule #73572
4 credits, MWF 12:20-1:10 PM - Room 329 Integrated Sciences Building
Lab 1 - Tuesdays 1:25-4:25 PM, schedule #73573 - Room 264 Integrated Sciences Bldg.
or Lab 2 - Wednesdays 1:25-4:25 PM, schedule #73574 - Room 264 Integrated Sciences Bldg.
Dr. Elizabeth Connor
Office: 353 Morrill Science Center 4 South Wing
Course Website Histology is a study of cell structure and how it relates to the cell and organ function. The fine structor of cells, tissues, and organs is explored at the microscopic level and related to the physiology of the organ system. Tissues (nervous, muscle, connective, and epithelial) are explored in detail and their specializations are discussed in selected organ systems (circulatory, digestive, urinary, endocrine, and other glands, and lymphatic). Lab includes light microscopic identification of cells, tissues, and organs; related electron micrographs, introduction to microtechnique, demonstrations in the Electron Microscopy and Image Analysis facility. Group projects involving sectioning, staining, and immunohistochemistry. Students develop competency with light microscopy and are well prepared for coursework in graduate and medical school. Course assessment is based on exams, quizzes, and lab practicals, attendance and projects.
Textbook: Histology: A Text and Athlas: With Correlated Cell. Author: Ross and Pawlina. Publisher: Lippincott Williams & Wilkins, 5th edition Year Published: 2006, Price $74.95 ISBN 0781772214
BIOLOGY 550, Animal Behavior
Lecture Section A: schedule #73554
4 credits, Tuesday & Thursday 9:30-10:45 AM - Room 203 Morrill Science Center III
Lab 1: Tuesdays 1:25-4:25 PM, schedule #73584 - Room 339 Morrill II
or Lab 2: Wednesdays 1:25-4:25 PM, schedule #73585 - Room 339 Morrill II
Instructor: Dr. Jeffrey Podos
Lecture Section B: schedule #79179
4 credits, Tuesday & Thursday 8:30-9:45 AM - Room N201 Morrill Science Center IV
Lab 1: Mondays 1:25-4:25 PM, schedule #79180 - Room 339 Morrill II
or Lab 2: Thursdays 1:25-4:25 PM, schedule #79181 - Room 339 Morrill II
Instructor: Dr. Teri Orr
Animals have evolved a remarkable diversity of behavioral patterns, used in wide ranging ecological and social contexts. Our goal in the first part of this course will be to examine the mechanisms that underlie the expression of behavior. For example, how do birds locate prey, how do crayfish avoid becoming prey, how do crickets and birds develop species-specific communication signals, and how do animals navigate through their worlds? To help answer these questions we will turn to neurobiological, hormonal, genetic, and developmental perspectives. Our next goal in the course will be to examine the evolutionary bases of behavior, asking for example why animals move, forage, hide, communicate, and socialize as they do. To address these questions we make use of optimality theory and other behavioral ecological perspectives. Other topics in the course will include sexual selection, human behavior, and the role of behavior in establishing biodiversity. Textbook - John Alcock, Animal Behavior: An Evolutionary Approach, 9th edition, year published: 2009. See publisher's website: www.sinauer.com
BIOLOGY 891A, Graduate Program Seminar
Wednesdays 4:00-5:00 PM, section 3
Attendance at the Fall 2014 Neuroscience & Behavior Program Colloquia. Researchers from other institutions present their work to faculty, postdoctoral students, graduate students, and undergraduate students. In this context graduate students learn about the latest developments in a range of fields and receive valuable exposure to different lecturing styles. Students registering for this 1 credit (pass/fail) are encouraged to read in advance the scientific reprint pertaining to the lecture.
PSYCH 791A: Human Development
Monday & Wednesday 10:10-11:25 AM - TBD
Instructors: Dr. Maureen Perry-Jenkins and Dr. Lisa Scott
The purpose of this course is to critically examine contemporary issues and topics in the field of human development. The course will provide an overview of current theory and research related to development across the life course. Special emphasis will be placed on issues and debates that have dominated the field and continue to be a source of controversy and impetus for research. Using an interdisciplinary approach, we will explore social, cognitive, physical, and biological factors that can shape the course of human development. Attention will also be paid to how cultural context shapes and gives meaning to development. No textbook.
BIOLOGY 542 - Ichthyology
4 credits, lecture schedule #73558
Monday, Wednesday and Fridays 10:10-11:00 AM - Room N201 Morrill IV
Lab 1: Wednesdays 1:25-4:25 PM, schedule #73559 - Room 206 Morrill III
or Lab 2: Fridays 1:25-4:25 PM, schedule #73563 - Room 206 Morrill III
Instructor: Dr. Cristina Cox Fernandes
UMass Amherst Natural History Collections
This is an introductory course designed to familiarize students with the diversity of fishes. We will provide an overview of the biology, evolution and ecology of fishes. A phylogenetic approach will be used to look at major primitive to advanced fish groups. No prior coursework is required to take this course, but students are expected to have a general biology background and be enthusiastic in learning about this diverse group of organisms. The textbook to be used is "The Diversity of Fishes" by Helfman, Collete and Facey 2009. Only selected portions of the text will be required during the course. The lab is designed to supplement the lecture course with hands-on dissection, anatomy of preserved specimens and dry skeletons and identification of major lineages.
BIOLOGY 572, Neurobiology
3 credits, schedule #77940
Tue/Th 1:00-2:15 PM - Room S231 Integrated Learning Center
Instructor: Dr. Gerald Downes
Lecture with discussion and some laboratory exercises. Biology of nerve cells and cellular interactions in nervous systems. Lectures integrate structural, functional, molecular, and developmental approaches. Topics include neuronal anatomy and physiology, neural induction and pattern formation, development of neuronal connections, membrane potentials and neuronal signals, synapses, sensory systems, control of movement, systems neuroscience, and neural plasticity. Textbook: Bear et al Neuroscience: Exploring the Brain (Lippincott, Wms & Wilkins). SPARK quizzes, two midterm exams, and final exam. Prerequisite: Biol. 285 or both Psych 330 and intro biology.
NEUROS&B 891C, Biological Rhythms
variable 1-3 credits - Room N321 Morrill Science
Coordinator: Eric Bittman
This Journal Club will focus on neurobiology and modeling of circadian rhythms in mammals. The circadian clock is comprised of a network of cell-autonomous oscillators whose function depends upon transcriptional-translational feedback loops. The master pacemaker is entrained by environmental signals and regulates slave oscillators throughout the organism. This is an exciting and highly multidisciplinary field: mathematical modeling as well as molecular neurobiology are essential to understand these rhythms. The five-college clocks group brings together students and faculty from several departments. Faculty participants include Dr. Eric Bittman (UMass, Biology), Dr. Tanya Leise (Amherst College, Mathematics & Computer Science). Students may enroll for 1 credit, and will be expected to present one paper or figures from papers, and to participate in discussions.
All NSB graduate students must take at least one course to satisfy the quantitative requirement. The course(s) to be taken will be determined by the student's guidance committee. In most cases the requirement will be satisfied by taking one or more statistics courses, such as:
Psychology 640 and 641
Public Health 640
PSYCH 640, Statistical Inference in Psychology I
Lecture Section A: Monday-Wednesday-Friday 1:25-2:15 PM, schedule #74508 - Room 207 Tobin Hall
Discussion 1: Thursdays 4:00-5:00 PM, schedule #74510 - Room 207 Tobin Hall
Lab A: Wednesdays 2:30-3:30 PM, lab schedule #74509 - Room 207 Tobin Hall
Instructor: Andrew Cohen
Textbooks: (1) Roberts & Russo, A Student's Guide to Analysis of Variance, Routledge
(2) Maxwell & Delaney, Designing Experiments and Analyzing Data, 2nd edition Erlbaum
Lecture Section B: MWF 1:25-2:15 PM, schedule #77849 - Room 207 Tobin Hall
Discussion B: Thursdays 4:00-5:00 PM, schedule #77850 - Room 207 Tobin Hall
Lab B: Mondays 2:30-3:30 PM, schedule #77851 - Room 207 Tobin Hall
Instructor: David Arnold
The goal of this course is to provide students with an understanding of the basic statistical concepts underlying data analysis and with a working knowledge of how to display data and conduct and interpret appropriate analyses. The Psych 640/641 deals with the description of data, probability, basic inferential concepts, and thorough coverage of analysis of variance, as well as the use of contrasts to test specific hypotheses, and bivariate correlation and regression.
Continuation of Psych 640. Introduction to analysis of variance and correlational techniques, related to the general
problem of inference in the social sciences. Psych 641 is most appropriate for students who took 640 during the fall semester.
ECO 692 - Analysis of Environmental Data
3 credits, schedule #73897
Tuesday & Thursday 8:30-9:45 AM - Room 308 Holdsworth Hall - Course Syllabus
ECO 697S - Analysis of Environmental Data Lab
2 credits, lecture schedule #73898
Lab: Wednesdays 1:25-4:25 PM
Instructor: Dr. Kevin McGarigal
The lab introduces the statistical computing language R and provides hands-on experience using R to screen and adjust data, examine deterministic functions and probability distributions, conduct classic one- and two-sample tests, utilize bootstrapping and Monte Carlo randomization procedures, and conduct stochastic simulations for ecological modeling.
STATISTC 501, Methods of Applied Statistics
3 credits- Sample Course Syllabus
Tuesday & Thursday 2:30-3:45 PM - Room 51 Goessman
Instructor: Joanna Jeneralczuk
Prerequisites: Knowledge of high school algebra, junior standing or higher
Text: A text book with MINITAB CD (to be announced)
Description: MINITAB oriented statistical methods. Exploratory data analysis - population frequency distribution, empirical distribution, dot plots, stem and leaf plots, histogram quantities, interquartile range, box plots, sample mean, sample variance; Bivariate Data - side by side box plots, bivariate data, scatter plots, correlation coefficient, fitting a line to a bivariate data set (least squares method); Probability theory - sample space, events and their probabilities, random sampling, random variables and their distributions, expected value and variance of a random variable (discrete or continuous), the normal distribution; Sampling distribution - simple random sample, central limit theorem, sampling distribution of mean and proportion; Estimation and hypothesis testing for means and proportions - point estimation, interval estimation, testing hypotheses; Analysis of categorical data - multinomial experiments, chi-square goodness - of fit test, contingency tables; Analysis of variance - testing the equality of two or more ppopulation means; Linear and multiple regression - method of least squares, interpreting of computer output; Nonparametric Tests if time permits. Students are required to bring their laptop computers to classes for on site practice.
Spring 2015, Spring 2017, etc.
ECO 797S Applied Multivariate Statistics for Ecological Data
Instructor: Dr. Kevin McGarigal
Textbook: McGarigal, K, S.A. Cushman, and S. Stafford. 2000. Multivariate Statistics for Wildlife and Ecology Research. Springer-Verlag, New York.
ECO 697SA - Special Topics Advanced Statistical Ecology
lecture schedule #78247
Tuesday and Thursday 10:00-11:15 AM
Lab - Wednesdays 10:10-12:05 PM, schedule #79050 - Room 301 Holdsworrth
This course is taught every other fall semester. The course is lecture-based, but hands-on analysis of the datasets discussed in class will be done either in class or as homework. About a third of class time will be spent going over the extensive examples in the text. In addition, each student will present an analysis of their own data (or a simulation of their data) to the class.Review the assumptions of linear regression and then explore what you can do if you violate those assumptions. For example, a non-linear relationship between your independent predictor variables and your dependent variable means that instead of linear regression, you use additive modeling (GAM). Non-normal distributions may lead to using the Generalized Linear Model (GLM), such as Logistic Regression for binomial data, Poisson Regression for count data, or Negative Binomial Regression for overdispersed count data. Complications such as nested data, temporal or spatial correlation, heterogeneity in variance or repeated measurements all push you from linear regression to linear mixed effects modeling. By looking at several violations of assumptions, we will explore more of the landscape of statistics (univariate only, not multivariate). We will make frequent use of ecological examples, and students are encouraged to examine and use their own data sets. We will also simulate datasets to examine how various data sets look when they meet the assumptions and when they don’t.
Texts: Both texts are available for free download as PDFs via the UMass library on SpringerLink).
Z1: Zuur, A.F., E.N. Ieno, N.J. Walker, A.A. Saveliev and G.M. Smith. 2009. Mixed Effects Models and Extensions in Ecology with R. Springer, New York.
Z2: Zuur, A.F., E.N. Ieno and E.H.W.G. Meesters. 2009. A Beginner’s Guide to R. Springer, New York.
Grading: Homework – 25%
Weekly homework assignments will be required that go over some of the concepts and methods discussed in class.
Term Project – 75%
Students will be required to analyze a dataset for their term project. If the data has been collected, then students will be asked to use their fitted parameters to simulate a “duplicate” dataset and use it to test whether their original dataset actually met all the assumptions of the model. If a student has not collected data, then the student will simulate a fake data set and analyze that to determine how large a difference the analysis can detect, and whether the statistics can recover the ‘true’ parameters from the simulated data.