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MOtion DEcision (MODE) model

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MOtion DEcision (MODE) model

by Massimiliano Versace

 

09 Jul 2009 (Updated 22 Jul 2009)

MOtion DEcision (MODE) model is a neural model of perceptual decision-making.

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Description

This software has been realized at the Technology Lab, Department of Cognitive and Neural Systems, Boston University - http://techlab.bu.edu. The main author of the software is Praveen Pilly ( http://techlab.bu.edu/members/advait/ ).

How does the brain make perceptual decisions? Speed and accuracy of saccadic decisions regarding motion direction depend on the inherent ambiguity in the motion stimulus and correlate with the temporal dynamics of firing rates in parietal and frontal cortical neurons of macaque monkeys. The MOtion DEcision (MODE) model incorporates interactions within and between Retina/lateral geniculate nucleus, primary visual cortex, middle temporal area, medial superior temporal area, and lateral intraparietal area, gated by basal ganglia, to provide a functional and mechanistic understanding of motion-based decision-making behavior in response to the experimental motion stimuli. The model demonstrate how motion capture circuits in middle temporal and medial superior temporal areas gradually solve the informational aperture problem, while interacting with a noisy recurrent competitive field in lateral intraparietal area whose self-normalizing choice properties make probabilistic directional decisions in real time. Quantitative model simulations include the time course of lateral intraparietal neuronal dynamics, as well as behavioral accuracy and reaction time properties, during both correct and error trials at different levels of input ambiguity in both fixed duration and reaction time tasks.
Demo

[ http://techlab.bu.edu/resources/data_view/mode_model_demos/ ] The demos show precomputed temporal dynamics at each MODE model stage with a brief description for three distinct input ambiguity (low, medium, and high) cases to provide an insight into the functional roles of the various stages.
Tutorial

[ http://techlab.bu.edu/MODE/Tutorial.pps ] The tutorial is a power point presentation on the science behind the MODE model.
Code Description

To use the software for the MODE model, download the package (MODE_GUI_070109.zip) from the Download(s) below and unzip the contents into a local folder. Open MATLAB (see Programming Language requirement below) and change the current directory to the folder. Also, set the path to include all the subfolders. At the command prompt, type MODEgui to begin using the software via a GUI (highly recommended). Also, the GUI provides one-stop easy access to model equations and descriptions, relevant articles, tutorial, animated demos, and source code. If you like a more hands-on approach, you can run the MODE model directly by using the main function runTask(.m). The folder includes a detailed READ ME file in \SRC, which is also available in Download(s) below.
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.
Programming Language(s)

The software has been tested using MATLAB 7 (32 bit) and MATLAB R2008a (64 bit). Newer versions of MATLAB should work fine.
License

IMPORTANT NOTICE
Some of the GUI functionality requires to download a directory with pre-computed data. Due to size limitations, this cannot be posted here. Please refer to this URL for the download: http://techlab.bu.edu/resources/software_view/motion_decision_mode_model/#download

- Contributors
Gail Carpenter
Ben Chandler
Robert Kozma
Praveen K. Pilly
Chaitanya Sai
Doug Sondak
Kadin Tseng

MATLAB release MATLAB 7.5 (R2007b)
Other requirements Please refer to http://techlab.bu.edu/resources/software_view/motion_decision_mode_model/#download for additional info
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Updates
15 Jul 2009

changed the introductory text (first paragraph)

21 Jul 2009

edited beginning and credits

22 Jul 2009

updated authorship/credits info

Tag Activity for this File
Tag Applied By Date/Time
neural networks Massimiliano Versace 10 Jul 2009 09:50:55
signal processing Massimiliano Versace 10 Jul 2009 09:50:56
mathematics Massimiliano Versace 10 Jul 2009 09:50:56
simulation Massimiliano Versace 10 Jul 2009 09:50:56

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