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
#   September 20, 2015.
#   Author: Antonio Ulloa
#   Last updated by Antonio Ulloa on September 20 2015
# **************************************************************************/
# Reads the correlation coefficients from several python (*.npy) data files, each
# corresponding to a single subject, and calculates:
# (1) the average functional connectivity across all subjects, including TVB subject
# (2) means, standard deviations and variances for each data point.
# For the calculations, It uses
# previously calculated correlation coefficients between IT and all other areas in both,
# synaptic activity time-series and fMRI bold time-series.

import numpy as np
import matplotlib.pyplot as plt

import plotly.plotly as py
import as tls

import matplotlib as mpl

import pandas as pd

from scipy.stats import t

# set matplot lib parameters to produce visually appealing plots'ggplot')

# construct array of indices of modules contained in an LSNM model, minus 1
modules = np.arange(7)

# construct array of subjects to be considered
subjects = np.arange(10)

# define the names of the output files where the correlation coefficients were stored
func_conn_syn_dms_subj1 = '../visual_model/subject_1/output.36trials/corr_syn_IT_vs_all_dms.npy'
func_conn_syn_dms_subj2 = '../visual_model/subject_2/output.36trials/corr_syn_IT_vs_all_dms.npy'
func_conn_syn_dms_subj3 = '../visual_model/subject_3/output.36trials/corr_syn_IT_vs_all_dms.npy'
func_conn_syn_dms_subj4 = '../visual_model/subject_4/output.36trials/corr_syn_IT_vs_all_dms.npy'
func_conn_syn_dms_subj5 = '../visual_model/subject_5/output.36trials/corr_syn_IT_vs_all_dms.npy'
func_conn_syn_dms_subj6 = '../visual_model/subject_6/output.36trials/corr_syn_IT_vs_all_dms.npy'
func_conn_syn_dms_subj7 = '../visual_model/subject_7/output.36trials/corr_syn_IT_vs_all_dms.npy'
func_conn_syn_dms_subj8 = '../visual_model/subject_8/output.36trials/corr_syn_IT_vs_all_dms.npy'
func_conn_syn_dms_subj9 = '../visual_model/subject_9/output.36trials/corr_syn_IT_vs_all_dms.npy'
func_conn_syn_dms_subj10= '../visual_model/subject_10/output.36trials/corr_syn_IT_vs_all_dms.npy'
func_conn_syn_dms_tvb   = '../visual_model/subject_tvb/output.36trials/corr_syn_IT_vs_all_tvb.npy'

func_conn_fmri_dms_subj1 = '../visual_model/subject_1/output.36trials/corr_fmri_IT_vs_all_dms_poisson.npy'
func_conn_fmri_dms_subj2 = '../visual_model/subject_2/output.36trials/corr_fmri_IT_vs_all_dms_poisson.npy'
func_conn_fmri_dms_subj3 = '../visual_model/subject_3/output.36trials/corr_fmri_IT_vs_all_dms_poisson.npy'
func_conn_fmri_dms_subj4 = '../visual_model/subject_4/output.36trials/corr_fmri_IT_vs_all_dms_poisson.npy'
func_conn_fmri_dms_subj5 = '../visual_model/subject_5/output.36trials/corr_fmri_IT_vs_all_dms_poisson.npy'
func_conn_fmri_dms_subj6 = '../visual_model/subject_6/output.36trials/corr_fmri_IT_vs_all_dms_poisson.npy'
func_conn_fmri_dms_subj7 = '../visual_model/subject_7/output.36trials/corr_fmri_IT_vs_all_dms_poisson.npy'
func_conn_fmri_dms_subj8 = '../visual_model/subject_8/output.36trials/corr_fmri_IT_vs_all_dms_poisson.npy'
func_conn_fmri_dms_subj9 = '../visual_model/subject_9/output.36trials/corr_fmri_IT_vs_all_dms_poisson.npy'
func_conn_fmri_dms_subj10= '../visual_model/subject_10/output.36trials/corr_fmri_IT_vs_all_dms_poisson.npy'
func_conn_fmri_dms_tvb   = '../visual_model/subject_tvb/output.36trials/corr_fmri_IT_vs_all_poisson_tvb.npy'

# open files that contain correlation coefficients
fc_syn_dms_subj1 = np.load(func_conn_syn_dms_subj1)
fc_syn_dms_subj2 = np.load(func_conn_syn_dms_subj2)
fc_syn_dms_subj3 = np.load(func_conn_syn_dms_subj3)
fc_syn_dms_subj4 = np.load(func_conn_syn_dms_subj4)
fc_syn_dms_subj5 = np.load(func_conn_syn_dms_subj5)
fc_syn_dms_subj6 = np.load(func_conn_syn_dms_subj6)
fc_syn_dms_subj7 = np.load(func_conn_syn_dms_subj7)
fc_syn_dms_subj8 = np.load(func_conn_syn_dms_subj8)
fc_syn_dms_subj9 = np.load(func_conn_syn_dms_subj9)
fc_syn_dms_subj10 = np.load(func_conn_syn_dms_subj10)
fc_syn_dms_tvb = np.load(func_conn_syn_dms_tvb)
fc_fmri_dms_subj1 = np.load(func_conn_fmri_dms_subj1)
fc_fmri_dms_subj2 = np.load(func_conn_fmri_dms_subj2)
fc_fmri_dms_subj3 = np.load(func_conn_fmri_dms_subj3)
fc_fmri_dms_subj4 = np.load(func_conn_fmri_dms_subj4)
fc_fmri_dms_subj5 = np.load(func_conn_fmri_dms_subj5)
fc_fmri_dms_subj6 = np.load(func_conn_fmri_dms_subj6)
fc_fmri_dms_subj7 = np.load(func_conn_fmri_dms_subj7)
fc_fmri_dms_subj8 = np.load(func_conn_fmri_dms_subj8)
fc_fmri_dms_subj9 = np.load(func_conn_fmri_dms_subj9)
fc_fmri_dms_subj10 = np.load(func_conn_fmri_dms_subj10)
fc_fmri_dms_tvb = np.load(func_conn_fmri_dms_tvb)

# construct numpy arrays that contain correlation coefficients for all subjects
# (the functional connectivity of IT versus the other 6 modules of LSNM model and contralateral IT)
fc_syn_dms = np.array([fc_syn_dms_subj1, fc_syn_dms_subj2, fc_syn_dms_subj3,
                       fc_syn_dms_subj4, fc_syn_dms_subj5, fc_syn_dms_subj6,
                       fc_syn_dms_subj7, fc_syn_dms_subj8, fc_syn_dms_subj9,
                       fc_syn_dms_subj10, fc_syn_dms_tvb ]) 
fc_fmri_dms = np.array([fc_fmri_dms_subj1, fc_fmri_dms_subj2, fc_fmri_dms_subj3,
                        fc_fmri_dms_subj4, fc_fmri_dms_subj5, fc_fmri_dms_subj6,
                        fc_fmri_dms_subj7, fc_fmri_dms_subj8, fc_fmri_dms_subj9,
                        fc_fmri_dms_subj10, fc_fmri_dms_tvb ]) 

# now, we need to apply a Fisher Z transformation to the correlation coefficients,
fc_syn_dms  = np.arctanh(fc_syn_dms)
fc_fmri_dms = np.arctanh(fc_fmri_dms)

# concatenate both datasets together prior to generating boxplot
fc_dms = np.concatenate((fc_syn_dms, fc_fmri_dms), axis=1)

ax1.set_ylim([-.5, 1.5])
plt.setp(bp['boxes'], color='black')
plt.setp(bp['whiskers'], color='black')
plt.setp(bp['fliers'], color='red', marker='+')


# Show the plots on the screen

# send figure to website for showing others:
#plotly_fig = tls.mpl_to_plotly(mpl_fig)

#unique_url = py.plot(plotly_fig)

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