9.40 | Spring 2018 | Undergraduate

Introduction to Neural Computation


Meeting Times

Lectures: 2 sessions / week, 90 minutes / session

Recitations: 1 session / week, 1 hour / session


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 take-home 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.


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

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