Large-scale neural model of visual short-term memory (Ulloa, Horwitz 2016; Horwitz, et al. 2005,...)

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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.
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):
Gap Junctions:
Simulation Environment: Python;
Model Concept(s): Working memory;
Implementer(s): Ulloa, Antonio [antonio.ulloa at];
# The following script recreates a Resting State Experiment.
# The total number of timesteps is 39600 = 198 seconds
# We assume 1 timestep = 5 ms, as in Horwitz et al (2005)
# To maintain consistency with Husain et al (2004) and Tagamets and Horwitz (1998),
# we are assuming that each simulation timestep is equivalent to 5 milliseconds
# of real time. 
# define the simulation time in total number of timesteps
# Each timestep is roughly equivalent to 5ms
LSNM_simulation_time = 39600

# define a dictionary of simulation events functions, each associated with
# a specific simulation timestep
simulation_events = {        


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