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 4, 2012
Last day of classes: Friday, December 7, 2012
UMass Dean of Students Office Academic Honesty Policy .
class schedule #74271
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, Elizabeth Connor, 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: Neuroscience, D. Purves et al. (editors). Sinauer Associates & Lange; 4th edition; 2008; ISBN: 978-0-87893-697-7
Grading: 20% Attendance, participation and presentations 80% Two exams: one at midterm and one during finals week.
class schedule # 74265
class schedule # 74269
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.
class schedule #74268 (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.
class schedule #74270
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.
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 17. Use SPIRE registration #89928 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.
lecture schedule #73536
3 credits, MWF 12:20-1:10 PM - Room 329 Integrated Sciences Building
Lab 1 - Tuesdays 1:25-4:25 PM, schedule #73537 - Room 264 Integrated Sciences Bldg.
or Lab 2 - Wednesdays 1:25-4:25 PM, schedule #73538 - 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
lecture schedule #73517
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. Jeffey Podos 
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 
Wednesdays 4:00-5:00 PM, section 3
Attendance at the Fall 2012 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.
Monday & Wednesday 10:10-11:25 AM - Room 521B Tobin Hall
Instructors: Dr. Maureen Perry-Jenkins 
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.
This is a one credit course which will meet once per week for an hour (Mondays 5:00-6:00 PM) - Room N201 Morrill IV
Instructor: Dr. Tobias Baskin 
Course Syllabus 
This is a course for graduate students (PHD or MS), based on the premise that training in writing is warranted for a career that includes a great deal of writing. My major goal in the course will be to teach students how to control the flow of ideas within a paragraph. Exerting this control is a matter of knowing how different parts of a sentence influence the way that sentence is read. We will develop an understanding of reading and see how taking advantage of the way in which we read helps guide how we write. We will do exercises from a teand students will write a paper (ie as if for publication) on their own data during the semester. Grading will be based on participation. No one will be graded on how "well" or "poorly" they write. Required text: Joseph M. Williams, Style, Lessons in Clarity and Grace (Tenth Edition). ISBN-13: 978-0-205-74746-7.
lecture schedule #73522
Monday, Wednesday and Fridays 10:10-11:00 AM - Room N201 Morrill IV
Lab 1: Wednesdays 1:25-4:25 PM, schedule #73523 - Room 206 Morrill III
or Lab 2: Fridays 1:25-4:25 PM, schedule #73527 - 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.
Tue/Th 1:00-2:15 PM - Room 203 Morrill Science Center 3-South Wing
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.
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
Lecture Section A: Monday-Wednesday-Friday 1:25-2:15 PM, schedule #34311 - Room 207 Tobin Hall
Discussion 1: Thursdays 4:00-5:00 PM, schedule #34313 - Room 504 Tobin Hall
Lab A: Mondays 2:30-3:30 PM, lab schedule #34312 - Room 207 Tobin Hall
Instructor: David Arnold 
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 #34359
Discussion B: Thursdays 4:00-5:00 PM, schedule #34361 - Room 207 Tobin Hall
Lab B: Wednesdays 2:30-3:30 PM, schedule #34360 - Room 207 Tobin Hall
Instructor: Caren Rotello 
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.
Tuesday & Thursday 9:30-10:45 AM - Room 308 Holdsworth Hall - Course Syllabus 
lecture schedule #35887
Lab: Wednesdays 1:25-4:25 PM - Room 301 Holdsworth Hall
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.
3 credits- Sample Course Syllabus 
Tuesday & Thursday 1:00-2:15 PM - Room 219 Lederle Graduate Tower
Instructor: Dr. Hui Hsieh 
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.
Instructor: Dr. Kevin McGarigal 
Course Description 
Textbook: McGarigal, K, S.A. Cushman, and S. Stafford. 2000. Multivariate Statistics for Wildlife and Ecology Research. Springer-Verlag, New York.
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.