LICENSE ======= These files are copyright (c) 20010-2014 by Svenja Marx, Gina Gruenhage and Ueli Rutishauser, Philipps-University Marburg & California Institute of Technology This software is provided AS IS AND WITHOUT ANY WARRANTY WHATSOEVER. We welcome comments and questions but can not guarantee any support. Re-Distribution is NOT permited unless permission is obtained from the authors. If you use this code or publish based on it, please cite our paper: S. Marx, G. Gruenhage, D. Walper, U. Rutishauser, W. Einhauser. Competition with and without priority control: linking rivalry to attention through winner-take-all networks with memory. Ann NY Acad Sci (in press, 2015) This code is based on U. Rutishauser, R.J. Douglas. State dependent computation using coupled recurrent networks. Neural computation, 21(2):478-509, 2009 The authors: svenja.marx@physik.uni-marburg.de gina.gruenhage@campus.tu-berlin.de urut@caltech.edu Introduction ============ This set of mat-files is created to simulate rivalry behavior. The results in Marx et al, 2015 (ANYAS) are based on these functions. The basic functions are provided here, for testing Levelt's propositions, they have to be run several times using different input strengths. Functions ========= MainDoubleWTARivalry.m Main function, arguments are noise amplitude for the two input units, noise offset for the two input units, number of integration steps, noisetype (1 = gaussian, else poisson), and blanking table (if necessary). runDoubleWTA.m, plotDoubleWTAMatrices.m and plotDoubleWTARun_rivalry.m are executed in the main file. test_WTA_blank.m Function for testing blanking behavior, blanking and presentation duration can be adjusted as well as input strengths