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Large-scale neural model of visual short-term memory (Ulloa, Horwitz 2016; Horwitz, et al. 2005,...)
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Model Information
Model File
Citations
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];
Download the displayed file
/
lsnm_in_python-master
visual_model
subject_10
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weightslist.txt
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