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_6
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weightslist.txt
                            
% Fri Aug 21 04:52:55 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]  0.194494) 
  }
  From:  (1, 2)  {
    ([ 1, 2]  0.195391) 
  }
  From:  (1, 3)  {
    ([ 1, 3]  0.217608) 
  }
  From:  (1, 4)  {
    ([ 1, 4]  0.210141) 
  }
  From:  (1, 5)  {
    ([ 1, 5]  0.214741) 
  }
  From:  (1, 6)  {
    ([ 1, 6]  0.197317) 
  }
  From:  (1, 7)  {
    ([ 1, 7]  0.212303) 
  }
  From:  (1, 8)  {
    ([ 1, 8]  0.200140) 
  }
  From:  (1, 9)  {
    ([ 1, 9]  0.188223) 
  }
  From:  (2, 1)  {
    ([ 2, 1]  0.218236) 
  }
  From:  (2, 2)  {
    ([ 2, 2]  0.192047) 
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  From:  (2, 3)  {
    ([ 2, 3]  0.202691) 
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  From:  (2, 4)  {
    ([ 2, 4]  0.187616) 
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  From:  (2, 5)  {
    ([ 2, 5]  0.219408) 
  }
  From:  (2, 6)  {
    ([ 2, 6]  0.212409) 
  }
  From:  (2, 7)  {
    ([ 2, 7]  0.200747) 
  }
  From:  (2, 8)  {
    ([ 2, 8]  0.199646) 
  }
  From:  (2, 9)  {
    ([ 2, 9]  0.196665) 
  }
  From:  (3, 1)  {
    ([ 3, 1]  0.180430) 
  }
  From:  (3, 2)  {
    ([ 3, 2]  0.183939) 
  }
  From:  (3, 3)  {
    ([ 3, 3]  0.199164) 
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  From:  (3, 4)  {
    ([ 3, 4]  0.210526) 
  }
  From:  (3, 5)  {
    ([ 3, 5]  0.202540) 
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  From:  (3, 6)  {
    ([ 3, 6]  0.201858) 
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  From:  (3, 7)  {
    ([ 3, 7]  0.197483) 
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  From:  (3, 8)  {
    ([ 3, 8]  0.193004) 
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  From:  (3, 9)  {
    ([ 3, 9]  0.214051) 
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  From:  (4, 1)  {
    ([ 4, 1]  0.216624) 
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  From:  (4, 2)  {
    ([ 4, 2]  0.183641) 
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  From:  (4, 3)  {
    ([ 4, 3]  0.208879) 
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  From:  (4, 4)  {
    ([ 4, 4]  0.199668) 
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  From:  (4, 5)  {
    ([ 4, 5]  0.196593) 
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  From:  (4, 6)  {
    ([ 4, 6]  0.186678) 
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  From:  (4, 7)  {
    ([ 4, 7]  0.197691) 
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  From:  (4, 8)  {
    ([ 4, 8]  0.204531) 
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  From:  (4, 9)  {
    ([ 4, 9]  0.207168) 
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  From:  (5, 1)  {
    ([ 5, 1]  0.207652) 
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  From:  (5, 2)  {
    ([ 5, 2]  0.192421) 
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  From:  (5, 3)  {
    ([ 5, 3]  0.216574) 
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  From:  (5, 4)  {
    ([ 5, 4]  0.210016) 
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  From:  (5, 5)  {
    ([ 5, 5]  0.182061) 
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  From:  (5, 6)  {
    ([ 5, 6]  0.201826) 
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  From:  (5, 7)  {
    ([ 5, 7]  0.193539) 
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  From:  (5, 8)  {
    ([ 5, 8]  0.198604) 
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  From:  (5, 9)  {
    ([ 5, 9]  0.196489) 
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  From:  (6, 1)  {
    ([ 6, 1]  0.195605) 
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  From:  (6, 2)  {
    ([ 6, 2]  0.211758) 
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  From:  (6, 3)  {
    ([ 6, 3]  0.215823) 
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  From:  (6, 4)  {
    ([ 6, 4]  0.217704) 
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  From:  (6, 5)  {
    ([ 6, 5]  0.185046) 
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  From:  (6, 6)  {
    ([ 6, 6]  0.208874) 
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  From:  (6, 7)  {
    ([ 6, 7]  0.198559) 
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  From:  (6, 8)  {
    ([ 6, 8]  0.198116) 
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  From:  (6, 9)  {
    ([ 6, 9]  0.206551) 
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  From:  (7, 1)  {
    ([ 7, 1]  0.198927) 
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  From:  (7, 2)  {
    ([ 7, 2]  0.194421) 
  }
  From:  (7, 3)  {
    ([ 7, 3]  0.198395) 
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  From:  (7, 4)  {
    ([ 7, 4]  0.211391) 
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  From:  (7, 5)  {
    ([ 7, 5]  0.195829) 
  }
  From:  (7, 6)  {
    ([ 7, 6]  0.217463) 
  }
  From:  (7, 7)  {
    ([ 7, 7]  0.196232) 
  }
  From:  (7, 8)  {
    ([ 7, 8]  0.202281) 
  }
  From:  (7, 9)  {
    ([ 7, 9]  0.189958) 
  }
  From:  (8, 1)  {
    ([ 8, 1]  0.212544) 
  }
  From:  (8, 2)  {
    ([ 8, 2]  0.209632) 
  }
  From:  (8, 3)  {
    ([ 8, 3]  0.195830) 
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  From:  (8, 4)  {
    ([ 8, 4]  0.182734) 
  }
  From:  (8, 5)  {
    ([ 8, 5]  0.202121) 
  }
  From:  (8, 6)  {
    ([ 8, 6]  0.213793) 
  }
  From:  (8, 7)  {
    ([ 8, 7]  0.209679) 
  }
  From:  (8, 8)  {
    ([ 8, 8]  0.215245) 
  }
  From:  (8, 9)  {
    ([ 8, 9]  0.214577) 
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  From:  (9, 1)  {
    ([ 9, 1]  0.192536) 
  }
  From:  (9, 2)  {
    ([ 9, 2]  0.194069) 
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  From:  (9, 3)  {
    ([ 9, 3]  0.209587) 
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  From:  (9, 4)  {
    ([ 9, 4]  0.199822) 
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  From:  (9, 5)  {
    ([ 9, 5]  0.218421) 
  }
  From:  (9, 6)  {
    ([ 9, 6]  0.209838) 
  }
  From:  (9, 7)  {
    ([ 9, 7]  0.195345) 
  }
  From:  (9, 8)  {
    ([ 9, 8]  0.185738) 
  }
  From:  (9, 9)  {
    ([ 9, 9]  0.180096) 
  }
}

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