AM0041: Mathematical Methods in the Brain Sciences

Class goals:

1. Become more comfortable with mathematical notation and reasoning
2. Learn introductory concepts from
    - differential equations
    - probability and statistics
3. Learn basic elements of mathematical programming on a computer using MATLAB


The grades in this class are based on 2 exams and 9 homeworks.  The exams correspond to each of the two main topics covered in the course: differential equations, and probability and statistics.  They are not cumulative.  There is no cumulative final exam, but exam #2 may be scheduled during the final exam period. 

The exams each count for about 45% of your grade and all of the homeworks together count for about 10%.  I say "about" because if someone has very good homeworks (indicating that they put a lot of effort into the class), then I may use that to boost their grade a little.  This can make a difference if you are borderline between two letter grades at the end of the year.  The final grade cutoffs are above 90% for A, above 80% for B, above 70% for C and for pass.  I may lower these depending on the distribution of grades, but I will not raise them.  (So, if you have a 90 or above at the end of the year, then you will get an A regardless of the curve, if any.)

Homeworks are not accepted for grades after the solutions have been posted.  However, if you turn in a late homework, it can still help boost a borderline grade as mentioned above.  Limited collaboration is allowed on homeworks, but everyone must turn in their own version with distinct solutions and distinct MATLAB code.  No copying is allowed.  Obviously, no collaboration is allowed on exams.  The use of any materials (e.g., homeworks, solutions, MATLAB code, exams, etc.) from previous years is strictly forbidden.

Class Outline:

0. Introduction

    0.1 Two Main Topics
          0.1.1 Differential Equations
          0.1.2 Probability and Statistics

    0.2 MATLAB
          0.2.1 Plotting sinusoids
          0.2.2 Computing Fibonacci numbers
          0.2.3 A simple differential equation
          0.2.4 Numerical integration

1. Differential Equations

    1.1 One-dimensional Differential Equations
          1.1.1 Differential equations and their numerical integration
          1.1.2 Linear equations
          1.1.3 Some non-linear equations
          1.1.4 Qualitative analysis

    1.2 Two-dimensional Differential Equations
          1.2.1 Examples
          1.2.2 Qualitative analysis
          The Phase Plane
          1.2.3 Pairs of linear equations
          Stability analysis
          1.2.4 Pairs of nonlinear equations
          Stability and local analysis
          Example: two neurons
          Linear approximations near equilibria
          Limit cycles and the Poincare-Bendixson Theorem
          Example: a two-neuron oscillator

2. Probability and Statistics

    2.1 Probabilities and Random Variables
          2.1.1 Probability spaces
          Sample spaces
          Conditional probabilities
          2.1.2 Random variables
          Probability distributions
          Mean, variance and standard deviation
          The Normal density function
          The Binomial probability distribution

          The Poisson probability distribution

    2.2 Limit Laws of Probability
          2.2.1 The Law of Large Numbers (LLN)
          Independent random variables
          Identically distributed random variables
          Law of Large Numbers
          2.2.2 The Central Limit Theorem (CLT)
          Examples (with MATLAB)
          Standardized sums
          Central Limit Theorem
          2.2.3 Example: limit cycles in populations of neurons

    2.3 Hypothesis Testing
          2.3.1 Basic elements of a hypothesis test
          Main idea
          Critical region and significance level
          2.3.2 Examples
          Voter preference
          Deployment of defibrillators
          Approximate tests using CLT
          Extreme fishing
          Poisson spiking

Click here to download a pdf format of this page.