/mazer-jackson

code base for analysis written by Brandon Jackson for Jamie Mazer's lab

Primary LanguageMATLAB

mazer-jackson

Summary

Code base for V4 cell analysis written by Brandon Jackson for Jamie Mazer's lab

Examples

Examples are contained in the Examples/ directory. Each example consists of a matlab file containing demo code and png files that show the output of the sample code.

Code Overview

There are two general kinds of classes in the code base: those that are objects designed to facilitate model building and testing and those that perform analysis. They are explained in turn below.

Model Building Classes

Philosophy

These classes contain several different kinds of objects with the intention of making it easier to perform analysis tasks like loading stimulus images, building models, fitting models, re-running an experiment with a model, and saving a model's spike train to a new p2m file.

Fitters

Fitters are used to fit Models to a given p2m file.

Classes:

  • AnglePlayFitter (@todo)
  • GratRevFitter

Loaders

Loaders are used to load stimulus images. All loaders have consistent public APIs to make it easy for Models or other classes to interact with multiple stimulus classes. All loaders must be passed a p2m file in the constructor so they can generate the correct stimuli.

For example, here is the code to load random stimuli for both an AnglePlay stimulus and a GratRev stimulus.

angleplay_pf = pffind('romeo0295*curvplay');
APL = AnglePlayLoader(angleplay_pf);
angleplay_stimulus = APL.randomStimulus();

gratrev_pf = pffind('romeo0295*gratrev*001');
GRL = GratRevLoader(gratrev_pf);
gratrev_stimulus = GRL.randomStimulus();

These functions both use the public function randomStimulus().

The stimuli generated by the loaders are downsampled versions of the ones presented in the experiments. The amount of downsampling is controlled via the SuperLoader.DOWNSAMPLE_SCALE constant. The stimulus image appears centered in the frame. The stimuli are double matrices with range [0,1].

Shared Public Methods:

  • randomStimulus()
  • plotRandomStimuli()

Classes:

  • AnglePlayLoader
  • GratRevLoader
  • GratRevBarLoader
  • GridCurvLoader
  • SuperLoader
  • WaveRev2Loader (@todo)

Models

Models are used to generate responses to stimuli. All loaders have consistent public APIs to make it easy for Writers or other classes to interact with multiple models.

Shared Public Methods:

  • stimulate()

Classes:

  • ComplexCellModel
  • GaborModel
  • SimpleCellModel
  • SuperModel

Writers

Writers take p2m files and re-run experiments, presenting the stimuli (loaded via a Loader class) to a Model, and then recording the responses to a new synthetically-generated p2m file.

In addition to using a model to generate a spike train, it is also possible to generate a poisson spike train using the homogeneousPoisson() and inhomogeneousPoisson() methods.

Shared Public Methods:

  • writeFromModel()
  • discardRandomSpikes()
  • homogeneousPoisson()
  • inhomogeneousPoisson()

Classes:

  • AnglePlayWriter
  • GratRevWriter
  • SuperWriter

Analysis Classes

Cores

These classes contain the bulk of the analysis code for each experiment type.

  • AnglePlay
  • GratRev
  • GridCurv
  • WaveRev2 (@todo)

Utils

These classes perform analysis tasks that are shared (in theory) across multiple tasks.

  • PFUtil
  • RasterUtil
  • SuperUtil

There are two exceptions, which contain util functions specific to a stimulus class. These might be merged into their respective Core classes as static methods in the near future:

  • AnglePlayUtil
  • GratRevUtil

Finally there is a class that handles creating Gabor filters:

  • GaborUtil

Misc

  • RatesDB: performs analysis of many cells and stores results in a searchable database format
  • STRF: Spatio-Temporal Receptive Field analysis class

Scripts

  • Dependencies
    • sdf
  • Interfaces
    • pangleplay
    • pgridcurv2
    • preel
    • pstrf
    • pstrf_delta
  • Utils
    • pffind

To-Do List

  • I need to ensure that the ouputs of all model classes are consistent, and then document it, perhaps in a how-to-write-a-model guide.
  • A new system for organizing analysis code is necessary, because at the moment it is very difficult to figure out whether code is in RasterUtil, SuperUtil, or PFUtil. For example, perhaps SuperUtil.autocorrelogram() should be moved to a class dedicated to correlograms? Should they be base classes that the Core classes extend? For example right now I have defined a custom explainableVariance() method in AnglePlay but it should intelligently override the RasterUtil version somehow.
  • AnglePlayUtil and GratRevUtil should be made static methods of their respective Core classes
  • SuperUtil.bootstrappedAutocorrelogram() should be merged into SuperUtil.autocorrelogram(), and perhaps moved to a new class
  • Poisson analysis code should be moved out of SuperUtil and into its own class