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_4
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weightslist.txt
                            
% Wed Aug 19 21:41:30 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]  0.196883) 
  }
  From:  (1, 2)  {
    ([ 1, 2]  0.195255) 
  }
  From:  (1, 3)  {
    ([ 1, 3]  0.180489) 
  }
  From:  (1, 4)  {
    ([ 1, 4]  0.211340) 
  }
  From:  (1, 5)  {
    ([ 1, 5]  0.213448) 
  }
  From:  (1, 6)  {
    ([ 1, 6]  0.209966) 
  }
  From:  (1, 7)  {
    ([ 1, 7]  0.216091) 
  }
  From:  (1, 8)  {
    ([ 1, 8]  0.214671) 
  }
  From:  (1, 9)  {
    ([ 1, 9]  0.219465) 
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  From:  (2, 1)  {
    ([ 2, 1]  0.183176) 
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  From:  (2, 2)  {
    ([ 2, 2]  0.203349) 
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  From:  (2, 3)  {
    ([ 2, 3]  0.219044) 
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  From:  (2, 4)  {
    ([ 2, 4]  0.196074) 
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  From:  (2, 5)  {
    ([ 2, 5]  0.209588) 
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  From:  (2, 6)  {
    ([ 2, 6]  0.217718) 
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  From:  (2, 7)  {
    ([ 2, 7]  0.197547) 
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  From:  (2, 8)  {
    ([ 2, 8]  0.200289) 
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  From:  (2, 9)  {
    ([ 2, 9]  0.205222) 
  }
  From:  (3, 1)  {
    ([ 3, 1]  0.189664) 
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  From:  (3, 2)  {
    ([ 3, 2]  0.217163) 
  }
  From:  (3, 3)  {
    ([ 3, 3]  0.190014) 
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  From:  (3, 4)  {
    ([ 3, 4]  0.185971) 
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  From:  (3, 5)  {
    ([ 3, 5]  0.218530) 
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  From:  (3, 6)  {
    ([ 3, 6]  0.195490) 
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  From:  (3, 7)  {
    ([ 3, 7]  0.213315) 
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  From:  (3, 8)  {
    ([ 3, 8]  0.192845) 
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  From:  (3, 9)  {
    ([ 3, 9]  0.197958) 
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  From:  (4, 1)  {
    ([ 4, 1]  0.206743) 
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  From:  (4, 2)  {
    ([ 4, 2]  0.200624) 
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  From:  (4, 3)  {
    ([ 4, 3]  0.181684) 
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  From:  (4, 4)  {
    ([ 4, 4]  0.182344) 
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  From:  (4, 5)  {
    ([ 4, 5]  0.218909) 
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  From:  (4, 6)  {
    ([ 4, 6]  0.187949) 
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  From:  (4, 7)  {
    ([ 4, 7]  0.183583) 
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  From:  (4, 8)  {
    ([ 4, 8]  0.213808) 
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  From:  (4, 9)  {
    ([ 4, 9]  0.217983) 
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  From:  (5, 1)  {
    ([ 5, 1]  0.199831) 
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  From:  (5, 2)  {
    ([ 5, 2]  0.212014) 
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  From:  (5, 3)  {
    ([ 5, 3]  0.210501) 
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  From:  (5, 4)  {
    ([ 5, 4]  0.190631) 
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  From:  (5, 5)  {
    ([ 5, 5]  0.192700) 
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  From:  (5, 6)  {
    ([ 5, 6]  0.183600) 
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  From:  (5, 7)  {
    ([ 5, 7]  0.189552) 
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  From:  (5, 8)  {
    ([ 5, 8]  0.193161) 
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  From:  (5, 9)  {
    ([ 5, 9]  0.202463) 
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  From:  (6, 1)  {
    ([ 6, 1]  0.211714) 
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  From:  (6, 2)  {
    ([ 6, 2]  0.201785) 
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  From:  (6, 3)  {
    ([ 6, 3]  0.212583) 
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  From:  (6, 4)  {
    ([ 6, 4]  0.194537) 
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  From:  (6, 5)  {
    ([ 6, 5]  0.185568) 
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  From:  (6, 6)  {
    ([ 6, 6]  0.210380) 
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  From:  (6, 7)  {
    ([ 6, 7]  0.204619) 
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  From:  (6, 8)  {
    ([ 6, 8]  0.200243) 
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  From:  (6, 9)  {
    ([ 6, 9]  0.202172) 
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  From:  (7, 1)  {
    ([ 7, 1]  0.191900) 
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  From:  (7, 2)  {
    ([ 7, 2]  0.213869) 
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  From:  (7, 3)  {
    ([ 7, 3]  0.180365) 
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  From:  (7, 4)  {
    ([ 7, 4]  0.214531) 
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  From:  (7, 5)  {
    ([ 7, 5]  0.215695) 
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  From:  (7, 6)  {
    ([ 7, 6]  0.202969) 
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  From:  (7, 7)  {
    ([ 7, 7]  0.204846) 
  }
  From:  (7, 8)  {
    ([ 7, 8]  0.196650) 
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  From:  (7, 9)  {
    ([ 7, 9]  0.206442) 
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  From:  (8, 1)  {
    ([ 8, 1]  0.180391) 
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  From:  (8, 2)  {
    ([ 8, 2]  0.217019) 
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  From:  (8, 3)  {
    ([ 8, 3]  0.204430) 
  }
  From:  (8, 4)  {
    ([ 8, 4]  0.194653) 
  }
  From:  (8, 5)  {
    ([ 8, 5]  0.180669) 
  }
  From:  (8, 6)  {
    ([ 8, 6]  0.181422) 
  }
  From:  (8, 7)  {
    ([ 8, 7]  0.211238) 
  }
  From:  (8, 8)  {
    ([ 8, 8]  0.191242) 
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  From:  (8, 9)  {
    ([ 8, 9]  0.216170) 
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  From:  (9, 1)  {
    ([ 9, 1]  0.196171) 
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  From:  (9, 2)  {
    ([ 9, 2]  0.197911) 
  }
  From:  (9, 3)  {
    ([ 9, 3]  0.184800) 
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  From:  (9, 4)  {
    ([ 9, 4]  0.206130) 
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  From:  (9, 5)  {
    ([ 9, 5]  0.184007) 
  }
  From:  (9, 6)  {
    ([ 9, 6]  0.199201) 
  }
  From:  (9, 7)  {
    ([ 9, 7]  0.194013) 
  }
  From:  (9, 8)  {
    ([ 9, 8]  0.216531) 
  }
  From:  (9, 9)  {
    ([ 9, 9]  0.180587) 
  }
}

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