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

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Accession:206337
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.
References:
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):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: Python;
Model Concept(s): Working memory;
Implementer(s): Ulloa, Antonio [antonio.ulloa at alum.bu.edu];
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lsnm_in_python-master
auditory_model
subject_1_OLD
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weightslist.txt *
                            
% Mon Aug  3 15:42:52 2015

% Input layer: (1, 1)
% Output layer: (1, 81)
% Fanout size: (9, 9)
% Fanout spacing: (1, 1)
% Specified fanout weights

Connect(ena1, ea1d)  {
  From:  (1, 1)  {
    ([ 1,78]  0.033734)     ([ 1,79]  0.036930)     ([ 1,80]  0.032719)     ([ 1,81]  0.033100)     ([ 1, 1]  0.032062)     ([ 1, 2]  0.042564)     ([ 1, 3]  0.045676)     ([ 1, 4]  0.039318)     ([ 1, 5]  0.038908) 
    ([ 1,78]  0.043865)     ([ 1,79]  0.044590)     ([ 1,80]  0.043358)     ([ 1,81]  0.040139)     ([ 1, 1]  0.031264)     ([ 1, 2]  0.039014)     ([ 1, 3]  0.042821)     ([ 1, 4]  0.045815)     ([ 1, 5]  0.048129) 
    ([ 1,78]  0.049921)     ([ 1,79]  0.038433)     ([ 1,80]  0.040518)     ([ 1,81]  0.040351)     ([ 1, 1]  0.049450)     ([ 1, 2]  0.047724)     ([ 1, 3]  0.045793)     ([ 1, 4]  0.039280)     ([ 1, 5]  0.046434) 
    ([ 1,78]  0.043341)     ([ 1,79]  0.034066)     ([ 1,80]  0.036893)     ([ 1,81]  0.032432)     ([ 1, 1]  0.036931)     ([ 1, 2]  0.048059)     ([ 1, 3]  0.046322)     ([ 1, 4]  0.048034)     ([ 1, 5]  0.046583) 
    ([ 1,78]  0.040085)     ([ 1,79]  0.045724)     ([ 1,80]  0.049823)     ([ 1,81]  0.043459)     ([ 1, 1]  0.031537)     ([ 1, 2]  0.038982)     ([ 1, 3]  0.034661)     ([ 1, 4]  0.031605)     ([ 1, 5]  0.044491) 
    ([ 1,78]  0.032816)     ([ 1,79]  0.049865)     ([ 1,80]  0.033782)     ([ 1,81]  0.034539)     ([ 1, 1]  0.046644)     ([ 1, 2]  0.045340)     ([ 1, 3]  0.035791)     ([ 1, 4]  0.036798)     ([ 1, 5]  0.038867) 
    ([ 1,78]  0.046916)     ([ 1,79]  0.041867)     ([ 1,80]  0.045501)     ([ 1,81]  0.031286)     ([ 1, 1]  0.048855)     ([ 1, 2]  0.034568)     ([ 1, 3]  0.041656)     ([ 1, 4]  0.041914)     ([ 1, 5]  0.037937) 
    ([ 1,78]  0.033990)     ([ 1,79]  0.042978)     ([ 1,80]  0.035739)     ([ 1,81]  0.031526)     ([ 1, 1]  0.033190)     ([ 1, 2]  0.035606)     ([ 1, 3]  0.032819)     ([ 1, 4]  0.037664)     ([ 1, 5]  0.049389) 
    ([ 1,78]  0.038103)     ([ 1,79]  0.033597)     ([ 1,80]  0.046522)     ([ 1,81]  0.033794)     ([ 1, 1]  0.044791)     ([ 1, 2]  0.032436)     ([ 1, 3]  0.039425)     ([ 1, 4]  0.046861)     ([ 1, 5]  0.037710) 
  }
}

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