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
visual_model
subject_7
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weightslist.txt
                            
% Fri Aug 21 17:13:46 2015

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

Connect(exss, exfs)  {
  From:  (1, 1)  {
    ([ 1, 1]  0.184523) 
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  From:  (1, 2)  {
    ([ 1, 2]  0.189999) 
  }
  From:  (1, 3)  {
    ([ 1, 3]  0.216734) 
  }
  From:  (1, 4)  {
    ([ 1, 4]  0.200374) 
  }
  From:  (1, 5)  {
    ([ 1, 5]  0.215031) 
  }
  From:  (1, 6)  {
    ([ 1, 6]  0.204952) 
  }
  From:  (1, 7)  {
    ([ 1, 7]  0.217736) 
  }
  From:  (1, 8)  {
    ([ 1, 8]  0.192545) 
  }
  From:  (1, 9)  {
    ([ 1, 9]  0.196112) 
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  From:  (2, 1)  {
    ([ 2, 1]  0.199938) 
  }
  From:  (2, 2)  {
    ([ 2, 2]  0.198006) 
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  From:  (2, 3)  {
    ([ 2, 3]  0.214175) 
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  From:  (2, 4)  {
    ([ 2, 4]  0.203296) 
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  From:  (2, 5)  {
    ([ 2, 5]  0.191086) 
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  From:  (2, 6)  {
    ([ 2, 6]  0.192707) 
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  From:  (2, 7)  {
    ([ 2, 7]  0.217592) 
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  From:  (2, 8)  {
    ([ 2, 8]  0.203620) 
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  From:  (2, 9)  {
    ([ 2, 9]  0.216465) 
  }
  From:  (3, 1)  {
    ([ 3, 1]  0.197637) 
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  From:  (3, 2)  {
    ([ 3, 2]  0.219643) 
  }
  From:  (3, 3)  {
    ([ 3, 3]  0.180631) 
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  From:  (3, 4)  {
    ([ 3, 4]  0.194646) 
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  From:  (3, 5)  {
    ([ 3, 5]  0.183096) 
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  From:  (3, 6)  {
    ([ 3, 6]  0.198411) 
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  From:  (3, 7)  {
    ([ 3, 7]  0.183665) 
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  From:  (3, 8)  {
    ([ 3, 8]  0.189940) 
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  From:  (3, 9)  {
    ([ 3, 9]  0.219309) 
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  From:  (4, 1)  {
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  From:  (4, 2)  {
    ([ 4, 2]  0.180049) 
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  From:  (4, 3)  {
    ([ 4, 3]  0.202111) 
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  From:  (4, 4)  {
    ([ 4, 4]  0.182876) 
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  From:  (4, 5)  {
    ([ 4, 5]  0.191819) 
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  From:  (4, 6)  {
    ([ 4, 6]  0.218310) 
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  From:  (4, 7)  {
    ([ 4, 7]  0.186805) 
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  From:  (4, 8)  {
    ([ 4, 8]  0.203753) 
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  From:  (4, 9)  {
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  From:  (5, 1)  {
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  From:  (5, 2)  {
    ([ 5, 2]  0.182670) 
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  From:  (5, 3)  {
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  From:  (5, 4)  {
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  From:  (5, 5)  {
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  From:  (5, 6)  {
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  From:  (5, 7)  {
    ([ 5, 7]  0.188464) 
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  From:  (5, 8)  {
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  From:  (5, 9)  {
    ([ 5, 9]  0.216365) 
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  From:  (6, 1)  {
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  From:  (6, 2)  {
    ([ 6, 2]  0.184042) 
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  From:  (6, 3)  {
    ([ 6, 3]  0.202431) 
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  From:  (6, 4)  {
    ([ 6, 4]  0.204514) 
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  From:  (6, 5)  {
    ([ 6, 5]  0.187496) 
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  From:  (6, 6)  {
    ([ 6, 6]  0.193790) 
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  From:  (6, 7)  {
    ([ 6, 7]  0.187141) 
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  From:  (6, 8)  {
    ([ 6, 8]  0.205128) 
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  From:  (6, 9)  {
    ([ 6, 9]  0.194351) 
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  From:  (7, 1)  {
    ([ 7, 1]  0.190229) 
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  From:  (7, 2)  {
    ([ 7, 2]  0.193479) 
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  From:  (7, 3)  {
    ([ 7, 3]  0.185262) 
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  From:  (7, 4)  {
    ([ 7, 4]  0.196852) 
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  From:  (7, 5)  {
    ([ 7, 5]  0.184289) 
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  From:  (7, 6)  {
    ([ 7, 6]  0.180195) 
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  From:  (7, 7)  {
    ([ 7, 7]  0.216751) 
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  From:  (7, 8)  {
    ([ 7, 8]  0.217932) 
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  From:  (7, 9)  {
    ([ 7, 9]  0.207856) 
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  From:  (8, 1)  {
    ([ 8, 1]  0.182499) 
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  From:  (8, 2)  {
    ([ 8, 2]  0.180450) 
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  From:  (8, 3)  {
    ([ 8, 3]  0.203044) 
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  From:  (8, 4)  {
    ([ 8, 4]  0.184273) 
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  From:  (8, 5)  {
    ([ 8, 5]  0.192290) 
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  From:  (8, 6)  {
    ([ 8, 6]  0.214042) 
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  From:  (8, 7)  {
    ([ 8, 7]  0.195728) 
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  From:  (8, 8)  {
    ([ 8, 8]  0.182912) 
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  From:  (8, 9)  {
    ([ 8, 9]  0.209921) 
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  From:  (9, 1)  {
    ([ 9, 1]  0.204410) 
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  From:  (9, 2)  {
    ([ 9, 2]  0.207625) 
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  From:  (9, 3)  {
    ([ 9, 3]  0.196444) 
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  From:  (9, 4)  {
    ([ 9, 4]  0.193241) 
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  From:  (9, 5)  {
    ([ 9, 5]  0.218691) 
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  From:  (9, 6)  {
    ([ 9, 6]  0.204911) 
  }
  From:  (9, 7)  {
    ([ 9, 7]  0.213265) 
  }
  From:  (9, 8)  {
    ([ 9, 8]  0.197472) 
  }
  From:  (9, 9)  {
    ([ 9, 9]  0.212561) 
  }
}

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