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DECRIPTION

This package contains a MatLab implementation of a model for cortical
neurons in visual area MT, as described in detail in the references
given at the bottom of this document.  The model takes as input a
discretized visual stimulus sequence (movie), and computes firing rate
responses of two successive neural populations, corresponding to
direction-selective complex cells in visual area V1, and
"pattern-selective" neurons in area MT.  Further information, as well
the most recent versions of the code, are available at

    http://www.cns.nyu.edu/~lcv/MT-model.html

RGC front-end and project status:

- This toolbox is being extended with an optional retinal ganglion cell (RGC)
  layer in front of V1 (enabled by default; set pars.rgc.enabled = 0 to recover
  exact legacy behavior). For the RGC front-end's design, current status, and
  usage, see AGENTS.md at the repository root and the docs/ folder. The base
  V1/MT model is described below and in the references.
- Native MATLAB implementations of the core 3D operations mean no MEX
  compilation is required for normal use (legacy C/MEX sources remain in the
  mex/ subfolder for reference).

Authors:  Timothy Saint and Eero P. Simoncelli
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INSTALLATION

1) Download and unpack the folder containing the code.  You can put
   the folder anywhere on your system, but we'll assume it's in a
   folder/directory named "MTmodel".

2) No MEX compilation is required for normal use. The core model now runs
   with native MATLAB implementations.

   Optional: Legacy C/MEX sources are still included in the mex subfolder
   for reference and experimentation. Core model internals call explicit
   native helper functions, so default execution does not depend on MEX.

3) RGC front-end (optional, enabled by default):

   pars = shPars;   % pars.rgc.enabled == 1

   The stimulus passes through an RGC layer before reaching V1. Set
   pars.rgc.enabled = 0 to recover the original no-RGC model exactly. For the
   RGC modes, the class-based parameterization (pars.rgc.classes), lesioning,
   and calibration, see AGENTS.md and the docs/ folder.

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USING THE SOFTWARE

0) Start matlab, and put the MTmodel folder in your path:

   addpath(genpath('PATHNAME-OF-MTmodel'));

1) Start by going through the shTutorial1.m file in the "help"
   folder.  This will show you how to compute responses of various
   stages of the model to any stimulus, and how to generate tuning
   curves.

2) Over time, we will be releasing extensions to the model, as well as 
   addition demonstrations and tutorials.  Check back at
        http://www.cns.nyu.edu/~lcv/MT-model.html
   for current status.

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REFERENCES

E P Simoncelli and D J Heeger.  A Model of Neuronal Responses in
  Visual Area MT.  Vision Research, 38(5):743-761, March 1998.     
  [Full journal article, with model details]
  http://www.cns.nyu.edu/~eero/ABSTRACTS/simoncelli96-abstract.html

E P Simoncelli, W D Bair, J R Cavanaugh, and J A Movshon. Testing and
  Refining a Computational Model of Neural Responses in Area MT.
  ARVO, 1996.
  [Conference presentation and abstract on testing predictions of the 
   model regarding bimodality of grating direction-tuning curves at
   slow speeds.  Slides available online]
  http://www.cns.nyu.edu/~eero/ABSTRACTS/ARVO-abstracts.html

D J Heeger, E P Simoncelli, and J A Movshon.
   Computational Models of Cortical Visual Processing.
   Proc. National Academy of Science. 93:623-627. January, 1996.
   [Brief description of V1 and MT models]
   http://www.cns.nyu.edu/~eero/ABSTRACTS/pnas95-abstract.html

E P Simoncelli and D J Heeger.
   A velocity-representation model for MT cells. ARVO, 1994.
   [Early conference presentation  and abstract.  Slides available online]
   http://www.cns.nyu.edu/~eero/ABSTRACTS/ARVO-abstracts.html

Eero P Simoncelli. Distributed Analysis and Representation of Visual Motion.
   PhD thesis, Massachusetts Institute of Technology, Department of
   Electrical Engineering and Computer Science, Cambridge MA, January 1993.
   [Original version of the model, which did not include normalization 
    of the MT stage]  
   http://www.cis.upenn.edu/~eero/ABSTRACTS/simoncelli-phd-abstract.html