Basics of Programming (Thurs. / Fri.; Springer)

 

  • Reading a line of code
  • Variables – x=2
    • Entering
    • Retrieving
    • Mathematical Operations + - * / ^
    • Order of Operations
  • Arrays
    • Loading
    • Visualizing
    • Retrieving Values
    • Altering Values
  • Basic Functions – Analyzing a dataset
    • Length, size
    • Mean, median, standard deviation
    • Sort
  • Functions of 2D arrays
  • Entering Arrays
    • Bracket operator
    • Colon operator
    • Comma semicolon
  • Conditional Retrieval
    • > 
    • find
  • Ploting
    • 2D: colors, lines, labels
    • 3D
  • Scripts
  • Conditional States and Control Flow
    • For loops
    • If, Elseif, Else
    • !, ==, >, <, >=
    • Or and And (| || & &&)
    • While
    • Counters
  • Cell Arrays
    • Entering
    • Retrieving
    • fieldnames
  • Function
    • Simple functions
    • Scope
  • Data Structures
  • Strings – dealing with text and mixed data
  • Parsing data

 

Statistics with MATLAB (Monday, AM; Born)

 

  • Rattus binomialis: When is he guessing?
    • all you need is 'rand'
    • simulations and probability
    • 'for' loops
    • tricky indexing into arrays
    • 'tic' and 'toc' to time code execution
    • binomial distribution
    • pdf's and cdf's

 

  • Bootstrap Bill: Statistics for the common man
    • operations along columns of 2D matrix
    • use of 'repmat'
    • simple descriptive statistics: mean, median, standard error
    • plotting functions: 'plot', 'errorbar', 'polar'
    • handle graphics
    • bootstrapping: null hypothesis; permutation test; confidence intervals

 

  • Sorting spikes: The joys of dimensionality reduction
    • thresholding to find peaks in data
    • using 'diff' to find events in continuous data
    • PCA/SVD to reduce dimensionality of data
    • 3D plotting
    • ISI histograms

 

Image Processing I: Computer Processing (Monday, PM; Springer)

 

  • Visualizing an image in MATLAB
    • loading images – imread and dragging
    • displaying images –imshow, imagesc, surf
    • data types – double, mat2gray
  • Manipulating Pixels
    • Background Subtraction
    • Rescaling
    • Transform – e.g. log

 

  • Manipulating Field of pixels – Filters
    • Intensity Preserving
      • Smoothing Filters
    • Non-Intensity Preserving
      • Contrast
      • Vertical Lines
      • Horizontal Lines
      • Edges
      • Contrast – Laplacian
      • Laplacian of Gaussian (LoG)

 

Image Processing II: Images and Your Brain (Tuesday, AM; Born)

 

  • Horace Barlow: Image Statistics and "Suspicious Coincidences"
    • loading and displaying images
    • histograms and probability density functions
    • nearby pixels are similar: correlation with 'corrcoef'
    • center-surround: LoG and 'imfilter'
    • orientation selectivity: finding "suspicious coincidences with 'colfilt'

 

  • David Marr: Where's Abe?
    • image manipulation: blkproc
    • edge detection: LoG and thresholding
    • image filtering in the frequency domain: 'fft2', 'ifft2'
    • band-rejection filters
    • zero-crossings and the "raw primal sketch"
    • better recognition through subtraction

 

Image Processing III: Segmentation

 

  • Finding cells or features
    • Thresholding
    • Bwlabel
    • Cleaning up the features – imopen, imclose, imerode imdialate
  • Quantitating Cell
    • Regionprops
    • Extracting values from pixels

 

Modeling with SIMBIOLOGY

(Tuesday, PM; Springer)

 

  • Launching simulink
    • The hydrogen model – mRNA synthesis and decay
      • Drawing a model
      • Setting the rate equations
      • Running a simulation
      • Running a parameter scan
      • Extract the data into MATLAB to manipulate
  • Models
    • Enzymatic reactions
    • Binding unbinding reaction
    • Ligand Receptor with Sink/Drug pharmacology with Sink
    • Competitive Inhibition
    • Multisite Phosphorylation