Meeting Times
Lectures: 2 sessions / week, 90 minutes / session
Recitations: 1 session / week, 1 hour / session
Prerequisites
Physics II (8.02, 8.021, or 8.022), 6.0002 Introduction to Computational Thinking and Data Science, and 9.01 Introduction to Neuroscience or permission of the instructor.
Note: several modules of 8.02 can be found in the Open Learning Library.
Course Description
This course introduces quantitative approaches to understanding brain and cognitive functions. Topics include mathematical description of neurons, the response of neurons to sensory stimuli, simple neuronal networks, statistical inference and decision making. It covers foundational quantitative tools of data analysis in neuroscience: correlation, convolution, spectral analysis, principal components analysis. Mathematical concepts include simple differential equations and linear algebra.
Homework Assignments
There will be a total of seven (7) homework assignments. Release and due dates are indicated on the class schedule. Assignments are due by 11:59 pm on the due date.
Excused extensions on assigned work will be given only for significant illness or family crisis. If an excused extension or postponement is requested, you must notify me prior to the class period for which the work is due.
You will be allowed four (4) free days of unexcused extensions on homework assignments to flexibly manage scheduling difficulties across the semester. Once these free days have been used, late work will be penalized at 20% per day.
Additionally, the lowest problem set grade will be dropped in calculating your final grade.
Software Requirements
Assignments require the use of MATLAB^{®} version 2017b. Therefore, it is essential that you install this software on your laptop.
Note: MIT OpenCourseWare does not provide student access or discounts for MATLAB software. It can be purchased from The MathWorks^{®}. For more information about MATLAB Pricing and Licensing, contact The MathWorks directly.
Policy on Problem Set Collaboration
Collaboration is encouraged on problem sets, but you must write up your own solutions and develop your own MATLAB code. List the names of all your collaborators on the top of each problem set submission.
Midterm Exam
There will be two midterm exams, which will be held in class. Bring a calculator for the exams. For the second midterm, a takehome programming exercise will be assigned. Instructions for submission will be provided with assignment.
Final Exam
The final exam will be focused on the material presented after the second midterm. However, we will include a question pertaining to the material covered in the first midterm and a question for the material covered in the second midterm.
Grading
Grades are not matched to a specific curve in this subject. If everyone in the class does well, everyone can get an A. Grades will be assigned based on your overall, weighted class average using the weighting scheme presented below:
Activities  Percentages 

Homework Assignments  50% 
2 Midterm Exams  30% (15% each) 
Final Exam  20% 
Class Schedule
L = Lecture
R = Recitation
SES #  TOPICS  KEY DATES 

L1  Course Overview and Ionic Currents  PSet 1 assigned 
R1  Intro to MATLAB and Ionic Currents  
L2  RC Circuit and Nernst Potential  
L3  Nernst Potential and Integrate and Fire Models  
R2  RC Model, Nernst Potential  
L4  Hodgkin Huxley Model Part 1  
No Class 
PSet 1 due PSet 2 assigned 

R3  Integrate and Fire Model, Hodgkin Huxley Model  
L5  Hodgkin Huxley Model Part 2  
L6  Dendrites  
L7  Synapses 
PSet 2 due PSet 3 assigned 
Midterm Review  
R5  Review Session  
Midterm Exam  
L8  Spike Trains  PSet 4 assigned 
R6  Spike Train Analysis  
L9  Receptive Fields  PSet 3 due 
L10  Time Series  
R7  Spike Triggered Average, Poisson Process  
L11  Spectral Analysis Part 1  PSet 4 due 
L12  Spectral Analysis Part 2  PSet 5 assigned 
R8  Spectral Analysis  
L13  Spectral Analysis Part 3  
Midterm 2 Review  
R9  Midterm 2 Review  
Midterm Exam 2  
R10  Help With PSet 5  
L14  Rate Models and Perceptrons 
PSet 5 due Midterm Programming assigned 
L15  Matrix Operations  
R11  Perceptons and Matrices  Midterm Programming due 
L16  Basis Sets  PSet 6 assigned 
L17  Principal Components Analysis  
R12  Principal Components Analysis  
L18  Recurrent Networks 
PSet 6 due PSet 7 assigned 
L19  Neural Integrators  
R13  Networks  
L20  Hopfield Networks  PSet 7 due 
L21  Sequence Generation in Songbirds  
R14  Final Review 