JOHNS HOPKINS UNIVERSITY

Department ofBiomedical Engineering
Instructor Name:
Matthew Kerr, B.S.
(Example: Jesse James, Ph.D.) / Course Title: From Coin Flips to Brains
318 Hackerman Hall

(410) 516-5466 / Course Number: EN.580.304.13
Distribution: N/A
Instructor Office Hours/Location: TBA / Credits: 1
Class Hours: 1:30-3:30 PM, MTTr
Classroom: TBA / Dates: 1/5/2014-1/23/2014
COURSE DESCRIPTION
Have you ever wondered if an equation could capture the human brain? This course will show you how flipping coins can be the basis for modeling brain function. It will provide both the concepts and practical hands-on training to implement Point Process and Generalized Linear Models. The hands-on training will focus on modeling neural data, although the technique has many other applications. Some MATLAB experience and having taken at least one course in basic statistics is suggested.
COURSE LEARNING OBJECTIVES
Students should obtain a good working knowledge and basic understanding of the following areas:
1 / Theory Underlying Poisson Processes
2 / Theory Underlying Point Processes
3 / Theory of Generalized Linear Models
4 / Applying of Generalized Linear Models to Modeling Neural Data
5 / Applying Goodness of Fit Measures to Models of Neural Data
REQUIRED MATERIALS
TEXTBOOKS
SUPPLIES / Laptop, MATLAB
COURSE WEBSITE
EVALUATION AND GRADING
Grades will be based on the following assessments:
ASSESSMENT TYPE / PERCENT OF FINAL GRADE
Assignments / 60
Exams / 0
Participation / 40
ATTENDANCE POLICY
One absence is allowed (“no questions asked”); additional absences need to be excused.

JOHNS HOPKINS POLICIES AND SUPPORT SERVICES

This course is governed by the policies set forth in The Johns Hopkins University Undergraduate Student Handbook, which contains information on a wide variety of topics, such as support services, and policies relating to student rights and responsibilities. This course is governed by the policies set forth in this document.

Some JHU student support services you may find useful include:

SUPPORT SERVICE / LOCATION / PHONE NUMBER / WEBSITE
Library E-Reserves / /
Summer & Intersession Programs / 3505 N. Charles Street, Suite 101 / 410-516-4548

CLASSROOM ACCOMODATIONS FOR STUDENTS WITH DISABILITIES

If you are a student with a documented disability who requires an academic adjustment, auxiliary aid or other similar accommodations, please contact The Office of Student Disability Services at ,

call 410-516-4720 or visit 385 Garland Hall.

STATEMENT OF DIVERSITY AND INCLUSION

Johns Hopkins University is a community committed to sharing values of diversity and inclusion in order to achieve and sustain excellence. We believe excellence is best promoted by being a diverse group of students, faculty and staff who are committed to creating a climate of mutual respect that is supportive of one another’s success.Through its curricula and clinical experiences, we purposefully support the University’s goal of diversity, and in particular, work toward an ultimate outcome of best serving the needs of students. Faculty and candidates are expected to demonstrate an understanding of diversity as it relates to planning, instruction, management, and assessment.

A WORD ON ETHICS

The strength of the university depends on academic and personal integrity. In this course, you must be honest and truthful. Ethical violations include cheating on exams, plagiarism, reuse of assignments, improper use of the Internet and electronic devices, unauthorized collaboration, alteration of graded assignments, forgery and falsification, lying, facilitating academic dishonesty, and unfair competition.

Report any violations you witness to the instructor.

COURSE SCHEDULE

Week / Dates / Topics / Assignments/Reading
1 / 1/6 / Review of Probability / Refresh on random variables, distributions, and independence property
1 / 1/8 / Review of Basic statistics / Definition and purpose of a statistic; mean, variance, likelihood
1 / 1/9 / Binomial Process/ Poisson Process / Properties of Poisson process, explain derivation from series of coin flips
2 / 1/13 / Point Process / Explain extension of Poisson process with history dependence
2 / 1/15 / Application to Neural Data / Neuron bursting and refractory period, time rescaling, goodness of fit testing, discuss project
2 / 1/16 / Generalized Linear Models / Likelihood calculation, use in estimating model parameters
3 / 1/22 / Generalized Linear Model Application / Code demonstration/project Part I
3 / 1/24 / Hands-on Tutorial / Code demonstration/project Part II