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 