Neuroscience Tutorials

I have created a public repository containing Matlab and Python tutorials designed to teach statistics and neuroscience concepts in the context of code that you can use in your own research. These tutorials can be accessed in a browser HERE or it can be cloned on your computer with the following terminal command:

git clone https://github.com/mikemanookin/NeuroscienceTeachingCode.git

I will continue to upload tutorials to this repository, so running ‘git pull‘ periodically will keep you up to date.

Proficiency in programming languages such as MATLAB, Python, or R has become an essential tool for neuroscience research. Developing programming skills requires years of consistent work. We have compiled some resources to help you learn to program. We also provide more advanced resources. The Allen Brain Institute has kindly provided resources for learning Python.

• Matlab for the Behavioral Sciences: Ione Fine and Geoff Boynton have kindly made the PDF version of their book available to us free of charge. This book provides an excellent overview of basic programming techniques as well as more advanced methods in the context of data analysis.
• Introduction to Matlab Programming from Vanderbilt U.
• Advanced MATLAB Resources from MathWorks
• Allen Institute Python Resources
• R Programming Tutorial
• Patrick Mineault put together a nice overview of better coding practices that could save you time and keep your projects organized.
• This GitHub repository teaches basic math concepts using Python.

Integration

• Grant Sanderson’s tutorials on basic calculus concepts with nice illustrations of the concepts.
• Khan Academy Calculus Tutorials

Differentiation

• Grant Sanderson’s tutorials on basic calculus concepts with nice illustrations of the concepts.
• Khan Academy Calculus Tutorials

Linear algebra is a very important tool in neuroscience and machine learning. Below, are some resources for learning these concepts. I recommend starting with the videos from Grant Sanderson illustrating the basic concepts of linear algebra. In preparation for the neuroscience course work, gaining a solid conceptual understanding of matrix multiplication will help in understanding the more advanced topics covered in the course work such as eigendecomposition and singular value decomposition.

• Sanderson’s illustrations of basic concepts of linear algebra. I highly recommend watching these before diving into an online course.
• Gilbert Strang’s online linear algebra course at MIT.

Derivatives are important for modeling several neural processes and phenomena such as neural network behaviors. Taking some time to familiarize yourself with these concepts will prepare you for coursework and research that applies mathematical concepts to biological systems. Eric Shea-Brown runs a very nice course on computational neuroscience (highly recommended) that uses differential equations in this way.

• Grant Sanderson does a great job of illustrating the concepts in his YouTube series.
• Khan Academy has some very nice videos covering concepts in differential equations.

Videos introducing basic ideas in statistics.

• Basics of probability distributions
• Measuring the difference between two distributions: t-tests
• Khan Academy course on statistics and probability

Methods for determining the differences between two distributions.

• Signal Detection Theory

Information Theory

• Entropy
• Mutual Information

Adrienne Fairhall and Raj Rao here at UW have put together a very nice online introduction to computational neuroscience, which can be accessed HERE. This course is a very nice overview of computational neuroscience concepts and does not take much time to complete.

Steve Brunton’s YouTube channel contains many excellent videos that cover many topics that are useful to neuroscience research including linear algebra and Fourier analysis.

Here is a nice resource for learning computational neuroscience concepts:

• Ella Batty’s Computational Neuroscience Tutorial

You will find it useful to store your data in arrays and matrices. This will allow you to compute values, etc, and save you a lot of time. Matlab and Python are powerful tools for working with arrays and matrices. Gaining proficiency with one of these languages will likely save you a lot of time in the long run.

Defining arrays.

      % Define a numerical vector
      x = [7, 9, 12, 5, 2, 13, 9];

      % Define a cell containing strings.
      c = {'seven', 'nine', 'twelve', 'five', 'two', 'thirteen', 'nine'};
     

For loops.

      %% For-loop examples

      % Go through the values defined in a for loop and print the values to the
      % command line.
      for j = 1 : 10
          disp(j);
      end

      % Let's loop through all of the values in the numerical array and display
      % what we've found to the command line.
      for j = 1 : length(x)
          disp(x(j));
      end

      % Now, let's do the same for the cell containing our strings.
      for j = 1 : length(c)
          disp(c{j});
      end
    

While loops.

      %% While-loop examples
      done = false;
      count = 0;
      while ~done
          count = count + 1;
          disp(x(count));
          if count >= length(x)
              done = true;
          end
      end
    

If-then-else statements.

      %% If-then-else statement examples

      % Let's loop through all of the values in the numerical array and display
      % what we've found to the command line.
      for j = 1 : length(x)
          if (x(j) == 5)
              disp('I found 5!');
          elseif (x(j) == 7)
              disp('I found 7!');
          else
              disp(['I found ',num2str(x(j)),'!']);
          end
      end

      % Now, let's do the same for the cell containing our strings.
      for j = 1 : length(c)
          if strcmp(c{j}, 'five')
              disp('I found five!');
          elseif strcmp(c{j}, 'seven')
              disp('I found seven!');
          else
              disp(['I found ',c{j},'!']);
          end
      end
    

switch-case-otherwise statements.

      %% switch-case-otherwise statement examples

      % Let's loop through all of the values in the numerical array and display
      % what we've found to the command line.
      for j = 1 : length(x)
          switch x(j)
              case 5
                  disp('I found 5!');
              case 7
                  disp('I found 7!');
              otherwise
                  disp(['I found ',num2str(x(j)),'!']);
          end
      end

      % Now, let's do the same for the cell containing our strings.
      for j = 1 : length(c)
          switch c{j}
              case 'five'
                  disp('I found five!');
              case 'seven'
                  disp('I found seven!');
              otherwise
                  disp(['I found ',c{j},'!']);
          end
      end
    

Searching for values in an array.

      %% Searching for values in an array.

      % Now, let's search for specific values in our array. I'll show you two
      % ways of doing this.

      % 1) Logical indices. This technique returns a true/1 if the index in the
      % array matches the desired value and false/0 for all other indices.

      tf = (x == 9); % For the numerical array; find values equal to 9
      tf2 = (x >= 7); % Find all of the values greater than or equal to 7.

      % Now for the cell containing strings: strcmp
      tf3 = strcmp(c, 'nine');
      % If you want to find matches while ignoring upper/lower case: strcmpi
      tf4 = strcmpi(c, 'Nine');

      % 2) The second way is to search for the index numbers in the array that
      % contain the desired values. If the value isn't there, it will return
      % empty.
      tf5 = find(x == 9);

      tf6 = find(strcmpi(c, 'nine'));
    

You will find it useful to store your data in arrays and matrices. This
will allow you to compute values, etc, and save you a lot of time. Matlab
and Python are powerful tools for working with arrays and matrices.
Gaining proficiency with one of these languages will likely save you a
lot of time in the long run.

Defining arrays.

      # Define a numerical array
      x = [7, 9, 12, 5, 2, 13, 9];

      # Define an array containing strings.
      c = ['seven', 'nine', 'twelve', 'five', 'two', 'thirteen', 'nine'];
     

For loops.

      ## For-loop examples

      # Go through the values defined in a for loop and print the values to the
      # command line.
      for j in range(0, 10):
          print(j)

      # Let's loop through all of the values in the numerical array and display
      # what we've found to the command line.
      for value in x:
          print(value)

      # Now, let's do the same for the cell containing our strings.
      for value in c:
          print(value)
    

While loops.

     ## While-loop examples.
     done = False;
     count = 0;
     while count < len(x):
         print(x[count])
         count += 1;
    

If-then-else statements.

     ## If-then-else statement examples

     # Let's loop through all of the values in the numerical array and display
     # what we've found to the command line.
     for j in range(0, len(x)):
         if (x[j] == 5):
             print("I found 5!")
         elif (x[j] == 7):
             print("I found 7!")
         else:
             print("I found %d!" % x[j])

     # Now, let's do the same for the cell containing our strings.
     for j in range(0, len(c)):
         if (c[j] == "five"):
             print("I found five!")
         elif (c[j] == "seven"):
             print("I found seven!")
         else:
             print("I found %s!" % c[j])

Searching for values in an array.

     ## Searching for values in an array.

     # Search for the index numbers in the array that contain the desired
     # values. If the value isn't there, it will return empty.
     # For the numerical array; find values equal to 9.
     tf = [i for i in range(len(x)) if x[i] == 9]
     print tf

     # Find all of the values greater than or equal to 7.
     tf2 = [i for i in range(len(x)) if x[i] >= 7]
     print tf2

     tf3 = [i for i in range(len(c)) if c[i] == "nine"]
     print tf3