Orientation selectivity in inhibition-dominated recurrent networks (Sadeh and Rotter, 2015)

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Accession:182759
Emergence of contrast-invariant orientation selectivity in large-scale networks of excitatory and inhibitory neurons using integrate-and-fire neuron models.
Reference:
1 . Sadeh S, Rotter S (2015) Orientation selectivity in inhibition-dominated networks of spiking neurons: effect of single neuron properties and network dynamics. PLoS Comput Biol 11:e1004045 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Abstract integrate-and-fire leaky neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEST; Python;
Model Concept(s): Oscillations; Synchronization; Simplified Models; Synaptic Integration; Sensory processing; Orientation selectivity;
Implementer(s): Sadeh, Sadra [s.sadeh at ucl.ac.uk];
Search NeuronDB for information about:  Gaba; Glutamate;
# general description

Simulation, analysis and plotting codes for:
[1] Sadeh and Rotter. Orientation selectivity in inhibition-dominated
networks of spiking neurons: effect of single neuron properties and
network dynamics. PLOS Computational Biology 2015.

This code was contributed by Sadra Sadeh.

Needs: NEST, Python

[The current codes are written compatible with NEST 2.6.0 and
Python 3; efforts have been made, however, to be backward compatible]

# list of files
(1) OS_source.py
Source file comprising the main Class for NEST simulations

(2) OS_params.py
Default parameters of simulations in [1]

(3) OS_run.py
Uses OS_source to run network simulations with parameters read from OS_params

(4) OS_functions.py
Functions needed for analysis of the results

(5) OS_results.py
Pre-processing and analysis of the results 
[Note: OS_results only works if no file called "results" exists in the
simulation folder, so by default the results is not overwritten to
avoid analysing an already processed data. Delete "results" from the
folder if you want to re-analyse the data.]

(6) OS_Figure1.py
Analysis and plotting of the simulations in Figure 1 of [1]

# to run the simulation and plot the figure, follow these steps:
(i) run (3): it saves spike trains and membrane potential out of
simulations
(ii) run (5): it pre-processes results of (3) and generates tuning
curves, etc
(iii) run (6): it analyses the results of (5) and plots Figure 1