//OVERVIEW//
This package contains software files related to simulations of the MOtion DEcision (MODE) model, as described in:
Grossberg, S. and Pilly, P.K. (2008). Temporal dynamics of decision-making during motion perception in the visual cortex. Vision Research, 48(12), 1345-1373.
These files, if properly run, will replicate all the simulation figures reported in the article.
//REQUIREMENTS//
PROGRAMMING LANGUAGE
The software has been tested using MATLAB 7 (32 bit) and MATLAB R2008a (64 bit). Newer versions of MATLAB should work fine.
OPERATING SYSTEM
Any operating system that can support MATLAB 7 (32 bit) or MATLAB R2008a (64 bit) or a newer version of MATLAB should be fine.
//FILE DESCRIPTIONS//
runTask.m
Main function for choosing the task whose (behavioral and physiological) data will be simulated and supplying key model lateral intraparietal area parameters
runRTtask.m
Function, which is called by runTask.m, to run the reaction time task (Roitman & Shadlen, 2002)
run2002FDtask.m
Function, which is called by runTask.m, to run the fixed duration task (Roitman & Shadlen, 2002)
run2001FDtask.m
Function, which is called by runTask.m, to run the fixed duration task (Shadlen & Newsome, 2001)
Motion0405.mat
Data file containing precomputed dynamics of average model middle temporal and medial superior temporal responses for six coherence levels and 100 trials per coherence
analyzeMTMST.m
Function that computes statistics (across trials) of average model middle temporal and medial superior temporal responses as a function of motion coherence
analyzeRT.m
Function that computes statistics of reaction time as a function of motion coherence for correct and error trials in the reaction time task
anisot.m
Function to generate a spatial oriented, anisotropic kernel
computeLIP_lhs.m
Function to generate the left portion in Figures 5 and 8 of Grossberg and Pilly (2008) of average lateral intraparietal responses during reaction time task trials for various coherences
computeLIP_rhs.m
Function to generate the right portion in Figures 5 and 8 of Grossberg and Pilly (2008) of average lateral intraparietal responses during reaction time task trials for various coherences
fsigm.m
Function that implements the self-excitatory signal function in model lateral intraparietal area cells
hsigm.m
Function that implements the recurrent inhibitory signal function in model lateral intraparietal circuit
Illust7.m
Script that can be run to show the dependence of performance on viewing duration for various coherences in the fixed duration task
isot.m
Function to generate a spatial isotropic kernel
KernelVisual.m
Script to visualize each of the various spatial kernels that are employed in the MODE model
left_part_lipFD.m
Function to generate the left portion in Figures 5 and 9 of Grossberg and Pilly (2008) of average lateral intraparietal responses during fixed duration task trials for various coherences
right_part_lipFD.m
Function to generate the right portion in Figures 5 and 9 of Grossberg and Pilly (2008) of average lateral intraparietal responses during fixed duration task trials for various coherences
LongShort.m
Script that illustrates the proportional relationship between lateral intraparietal response and reaction time as in Figure 10 of Grossberg and Pilly (2008)
ml_e.m (written by Myung, 2003)
Function for maximum likelihood estimation of parameters of the cumulative Weibull distribution function fit to the psychometric (accuracy vs. motion coherence) data
MODE_lip_FD01.m
Function that computes the lateral intraparietal responses in each trial of the fixed duration task (Shadlen & Newsome, 2001)
MODE_lip_FD.m
Function that computes the lateral intraparietal responses in each trial of the fixed duration task (Roitman & Shadlen, 2002)
MODE_lip_RT.m
Function that computes the lateral intraparietal responses in each trial of the reaction time task (Roitman & Shadlen, 2002)
MODE_motion.m
Function that performs the computations in the motion pathway from Retina to medial superior temporal area in each trial
polyfitw.m
Function to compute weighted least sqaures linear fits
rdm_stimulus.m
Function that generates the random dot motion stimuli
rect.m
Function that simply implements half-wave rectification
shift.m
Function that computes a spatial pattern that is shifted by one unit in a given direction of a given pattern
weib_mle.m (written by Myung, 2003)
Function that defines the log-likelihood of the cumulative Weibull distribution function
//CONTACT//
Praveen K. Pilly (advaitp@gmail.com)
Please contact for any questions, suggestions (code improvements, model enhancements, etc.), bug reports, etc.
//LICENSE POLICY//
Written by Praveen K. Pilly, Department of Cognitive and Neural Systems, Boston University
Copyright 2009, Trustees of Boston University
Permission to use, copy, modify, distribute, and sell this software and its documentation for any purpose is hereby granted without fee, provided that the above copyright notice and this permission notice appear in all copies, derivative works and associated documentation, and that neither the name of Boston University nor that of the author(s) be used in advertising or publicity pertaining to the distribution or sale of the software without specific, prior written permission. Neither Boston University nor its agents make any representations about the suitability of this software for any purpose. It is provided "as is" without warranty of any kind, either express or implied. Neither Boston University nor the author indemnify any infringement of copyright, patent, trademark, or trade secret resulting from the use, modification, distribution or sale of this software.