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Large Scale Neural Modeling (LSNM) simulator in Python
NIDCD/lsnm_in_python
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================================================================= PUBLIC DOMAIN NOTICE National Institute on Deafness and Other Communication Disorders This software/database is a "United States Government Work" under the terms of the United States Copyright Act. It was written as part of the author's official duties as a United States Government employee and thus cannot be copyrighted. This software/database is freely available to the public for use. The NIDCD and the U.S. Government have not placed any restriction on its use or reproduction. Although all reasonable efforts have been taken to ensure the accuracy and reliability of the software and data, the NIDCD and the U.S. Government do not and cannot warrant the performance or results that may be obtained by using this software or data. The NIDCD and the U.S. Government disclaim all warranties, express or implied, including warranties of performance, merchantability or fitness for any particular purpose. Please cite the author in any work or product based on this material. ================================================================= Large-Scale Neural Modeling software (LSNM) Section on Brain Imaging and Modeling Voice, Speech and Language Branch National Institute on Deafness and Other Communication Disorders National Institutes of Health This README file was last modified on September 18, 2017. ================================================================ Python version of LSNM (Large-Scale Neural Modeling software). This repository contains the following directories: * stimuli_creation: Scripts to create stimuli (visual and auditory) for simulation * simulation: LSNM Simulator source code * analysis: Scripts to analyze simulated neuronal and neuroimaging timeseries * visualization: Scripts to visualize simulated neuronal and neuroimaging data * auditory_model: Husain et al (2004)'s auditory model implemented using LSNM in python * visual_model: New version of Tagamets and Horwitz (1998)'s visual model using Ulloa and Horwitz (2016) framework * visual_model_with_forgetting: Slightly different version of "visual_model" above to allow decay in short-term memory modules during a visual Delayed match-to-sample task To execute this software you will need to have the following installed on your local machine or server: * Python 2.7 for any platform (so far tested on Mac OS and RedHat Linux). Please note that python LSNM is NOT compatible with either Python 2.6 or Python 3.0. * Python modules matplotlib, re, random, math, numpy, sys, and PyQt4, pandas, among other scientific computation modules. My advice is to download Anaconda Python, freely available from continuum.io, which contains Python 2.7 and a full set of modules commonly used in scientific computation. * The Virtual Brain Python modules, located at the TVB github repository at https://github.com/the-virtual-brain. You will need to clone the directories 'tvb-library' and 'tvb-data' and install it locally so that your Python installation is able to see the location of such modules. Please refer to the instructions provided in the github repository on how to cleanly install TVB. Also, refer to the 'Alternate installation: the user scheme' at https://docs.python.org/2/install/, for instructions on how to install python modules locally when you don't have 'write' permission to global-site packages (e.g., you are only a user in a Unix system an do not have a 'root' or 'su' password. If the ‘Alternate installation’ does not work, please refer to the “Installing Python modules” in the lsnm_in_python wiki page, at https://github.com/NIDCD/lsnm_in_python/wiki/Installing-Python-modules.
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