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_2_OLD
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weightslist.txt *
                            
% Mon Aug  3 15:42:52 2015

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

Connect(ia1d, ea1d)  {
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  From:  (1, 2)  {
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  From:  (1, 3)  {
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  From:  (1, 4)  {
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  From:  (1, 5)  {
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  From:  (1, 6)  {
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  From:  (1, 7)  {
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  From:  (1, 8)  {
    ([ 1, 8] -0.150000) 
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  From:  (1, 9)  {
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  From:  (1, 10)  {
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  From:  (1, 11)  {
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  From:  (1, 12)  {
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  From:  (1, 13)  {
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  From:  (1, 14)  {
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  From:  (1, 15)  {
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  From:  (1, 16)  {
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  From:  (1, 17)  {
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  From:  (1, 18)  {
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  From:  (1, 19)  {
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  From:  (1, 20)  {
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  From:  (1, 22)  {
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  From:  (1, 23)  {
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  From:  (1, 24)  {
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  From:  (1, 26)  {
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  From:  (1, 27)  {
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  From:  (1, 28)  {
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  From:  (1, 29)  {
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  From:  (1, 30)  {
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  From:  (1, 31)  {
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  From:  (1, 32)  {
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  From:  (1, 34)  {
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  From:  (1, 35)  {
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  From:  (1, 36)  {
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  From:  (1, 37)  {
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  From:  (1, 38)  {
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  From:  (1, 39)  {
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  From:  (1, 41)  {
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  From:  (1, 42)  {
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  From:  (1, 43)  {
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  From:  (1, 44)  {
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  From:  (1, 47)  {
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  From:  (1, 48)  {
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  From:  (1, 49)  {
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  From:  (1, 50)  {
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  From:  (1, 51)  {
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  From:  (1, 52)  {
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  From:  (1, 53)  {
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  From:  (1, 54)  {
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  From:  (1, 55)  {
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  From:  (1, 56)  {
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  From:  (1, 59)  {
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  From:  (1, 60)  {
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  From:  (1, 61)  {
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  From:  (1, 62)  {
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  From:  (1, 63)  {
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  From:  (1, 64)  {
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  From:  (1, 65)  {
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  From:  (1, 66)  {
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  From:  (1, 67)  {
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  From:  (1, 68)  {
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  From:  (1, 69)  {
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  From:  (1, 70)  {
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  From:  (1, 71)  {
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  From:  (1, 72)  {
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  From:  (1, 73)  {
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  From:  (1, 74)  {
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  From:  (1, 75)  {
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  From:  (1, 76)  {
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  From:  (1, 77)  {
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  From:  (1, 78)  {
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  From:  (1, 79)  {
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  From:  (1, 80)  {
    ([ 1,80] -0.150000) 
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  From:  (1, 81)  {
    ([ 1,81] -0.150000) 
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}

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