Large-scale neural model of visual short-term memory (Ulloa, Horwitz 2016; Horwitz, et al. 2005,...)

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Large-scale neural model of visual short term memory embedded into a 998-node connectome. The model simulates electrical activity across neuronal populations of a number of brain regions and converts that activity into fMRI and MEG time-series. The model uses a neural simulator developed at the Brain Imaging and Modeling Section of the National Institutes of Health.
1 . Tagamets MA, Horwitz B (1998) Integrating electrophysiological and anatomical experimental data to create a large-scale model that simulates a delayed match-to-sample human brain imaging study. Cereb Cortex 8:310-20 [PubMed]
2 . Ulloa A, Horwitz B (2016) Embedding Task-Based Neural Models into a Connectome-Based Model of the Cerebral Cortex. Front Neuroinform 10:32 [PubMed]
3 . Horwitz B, Warner B, Fitzer J, Tagamets MA, Husain FT, Long TW (2005) Investigating the neural basis for functional and effective connectivity. Application to fMRI. Philos Trans R Soc Lond B Biol Sci 360:1093-108 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Prefrontal cortex (PFC);
Cell Type(s):
Gap Junctions:
Simulation Environment: Python;
Model Concept(s): Working memory;
Implementer(s): Ulloa, Antonio [antonio.ulloa at];
# ============================================================================
#                            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 file ( was created on June 7, 2015.
#   Author: Antonio Ulloa
#   Last updated by Antonio Ulloa on March 15 2016 
# **************************************************************************/

# Calculate and plot MEG signal at source locations based on data from auditory
# delay-match-to-sample simulation

import numpy as np
import matplotlib.pyplot as plt

# define the name of the output file where the MEG source activity timeseries will be stored
MEG_source_file = 'meg_source_activity.npy'

# Load A1 synaptic activity data files into a numpy array
ea1u = np.loadtxt('ea1u_signed_syn.out')
ea1d = np.loadtxt('ea1d_signed_syn.out')

# Load A2 synaptic activity data files into a numpy array
ea2u = np.loadtxt('ea2u_signed_syn.out')
ea2c = np.loadtxt('ea2c_signed_syn.out')
ea2d = np.loadtxt('ea2d_signed_syn.out')

# Load ST synaptic activity data files into a numpy array
estg = np.loadtxt('estg_signed_syn.out')

# Load PFC synaptic activity data files into a numpy array
efd1 = np.loadtxt('efd1_signed_syn.out')
efd2 = np.loadtxt('efd2_signed_syn.out')
exfs = np.loadtxt('exfs_signed_syn.out')
exfr = np.loadtxt('exfr_signed_syn.out')

# Extract number of timesteps from one of the synaptic activity arrays
synaptic_timesteps = ea1u.shape[0]

# add all units within each region together across space to calculate
# MEG source dynamics in each brain region
a1 = np.sum(ea1u + ea1d, axis = 1)
a2 = np.sum(ea2u + ea2c + ea2d, axis=1)
st = np.sum(estg, axis = 1)
pf = np.sum(efd1 + efd2 + exfs + exfr, axis = 1)

# create a numpy array of MEG source activity timeseries
meg_source = np.array([a1, a2, st, pf])

print 'Size of each MEG source activity time-series: ', a1.size

# now, save all MEG source activity timeseries to a single file, meg_source)

# Set up figure to plot MEG source dynamics


# Plot MEG signal
a1_plot=plt.plot(a1, label='A1')
a2_plot=plt.plot(a2, label='A2')
st_plot=plt.plot(st, label='ST')
pf_plot=plt.plot(pf, label='PFC')


# Show the plot on the screen

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