COREM: configurable retina simulator (Martínez-Cañada et al., 2016)

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Accession:225095
COREM is a configurable simulator for retina modeling that has been implemented within the framework of the Human Brain Project (HBP). The software platform can be interfaced with neural simulators (e.g., NEST) to connect with models of higher visual areas and with the Neurorobotics Platform of the HBP. The code is implemented in C++ and computations of spatiotemporal equations are optimized by means of recursive filtering techniques and multithreading. Most retina simulators are more focused on fitting specific retina functions. By contrast, the versatility of COREM allows the configuration of different retina models using a set of basic retina computational primitives. We implemented a series of retina models by combining these primitives to characterize some of the best-known phenomena observed in the retina: adaptation to the mean light intensity and temporal contrast, and differential motion sensitivity. The code has been extensively tested in Linux. The software can be also adapted to Mac OS. Installation instructions as well as the user manual can be found in the Github repository: https://github.com/pablomc88/COREM
Reference:
1 . Martínez-Cañada P, Morillas C, Pino B, Ros E, Pelayo F (2016) A Computational Framework for Realistic Retina Modeling. Int J Neural Syst 26:1650030 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Retina;
Cell Type(s): Retina bipolar GLU cell; Retina photoreceptor cone GLU cell; Retina ganglion GLU cell; Retina amacrine cell; Retina horizontal cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: C or C++ program;
Model Concept(s): Vision;
Implementer(s): Martínez-Cañada, Pablo [pablomc at ugr.es]; Carrillo, Richard R. [rcarrillo at atc.ugr.es];
Search NeuronDB for information about:  Retina ganglion GLU cell; Retina photoreceptor cone GLU cell; Retina bipolar GLU cell;
###################################################################################
##                                                                               ##
## With this retina model our aim is to show an overview of the main features    ##
## that can be configured in the retina script. The retina model implemented     ##
## in this script processes an input sequence of images based on a general       ##
## retina architecture that includes all neuron types. Temporal evolution and    ##
## spatial arrangement of some neurons' membrane potentials are recorded by      ##
## multimeters and displayed during and after simulation.                        ##
##                                                                               ##
## Author: Pablo Martinez                                                        ##
## email: pablomc@ugr.es                                                         ##
##                                                                               ##
###################################################################################


### Simulation parameters ###

retina.TempStep('1') # simulation step (in ms)
retina.SimTime('1200') # simulation time (in ms)
retina.NumTrials('1') # number of trials
retina.PixelsPerDegree({'5'}) # pixels per degree of visual angle
retina.DisplayDelay('0') # display delay
retina.DisplayZoom({'10.0'}) # display zoom
retina.DisplayWindows('3') # Display windows per row

### Visual input ###

# Folder that contains the input sequence
retina.Input('sequence','input_sequences/Weberlaw/0_255/',{'InputFramePeriod','100'})

### Creation of computational retinal microcircuits ###

# Temporal modules
retina.Create('LinearFilter','tmp_photoreceptors',{'type','Gamma','tau','30.0','n','10.0'})
retina.Create('LinearFilter','tmp_horizontal',{'type','Gamma','tau','20.0','n','1.0'})
retina.Create('SingleCompartment','tmp_bipolar',{'number_current_ports','1.0','number_conductance_ports','2.0','Rm','0.0','Cm','100.0','E',{'0.0','0.0'}})
retina.Create('LinearFilter','tmp_amacrine',{'type','Gamma','tau','10.0','n','1.0'})

# Spatial filters
retina.Create('GaussFilter','Gauss_horizontal',{'sigma','0.3','spaceVariantSigma','False'})
retina.Create('GaussFilter','Gauss_bipolar',{'sigma','0.1','spaceVariantSigma','False'})
retina.Create('GaussFilter','Gauss_amacrine',{'sigma','0.3','spaceVariantSigma','False'})
retina.Create('GaussFilter','Gauss_ganglion',{'sigma','0.2','spaceVariantSigma','False'})

# Nonlinearities
retina.Create('StaticNonLinearity','SNL_photoreceptors',{'slope','-0.1','offset','0.0','exponent','1.0'})
retina.Create('StaticNonLinearity','SNL_horizontal',{'slope','1.0','offset','0.0','exponent','1.0'})
retina.Create('StaticNonLinearity','SNL_amacrine',{'slope','0.2','offset','1.0','exponent','2.0'})
retina.Create('StaticNonLinearity','SNL_bipolar',{'slope','10.0','offset','0.0','exponent','1.0','threshold','0.0'})
retina.Create('StaticNonLinearity','SNL_ganglion',{'slope','5.0','offset','0.0','exponent','1.0'})

### Connections ###

# Phototransduction
retina.Connect('L_cones','tmp_photoreceptors','Current')
retina.Connect('tmp_photoreceptors','SNL_photoreceptors','Current')

# Horizontal cells
retina.Connect('SNL_photoreceptors','Gauss_horizontal','Current')
retina.Connect('Gauss_horizontal','tmp_horizontal','Current')
retina.Connect('tmp_horizontal','SNL_horizontal','Current')

# Subtraction at Outer Plexiform Layer
retina.Connect({'SNL_horizontal',-,'SNL_photoreceptors'},'Gauss_bipolar','Current')
retina.Connect('Gauss_bipolar','tmp_bipolar','Current')
retina.Connect('tmp_bipolar','SNL_bipolar','Current')

# Gain control at Inner Plexiform Layer
retina.Connect('SNL_bipolar','Gauss_amacrine','Current')
retina.Connect('Gauss_amacrine','tmp_amacrine','Current')
retina.Connect('tmp_amacrine','SNL_amacrine','Current')
retina.Connect('SNL_amacrine','tmp_bipolar','Conductance')

# Bipolar-ganglion synapse
retina.Connect('SNL_bipolar','Gauss_ganglion','Current')
retina.Connect('Gauss_ganglion','SNL_ganglion','Current')

# Connection with NEST
retina.Connect('SNL_ganglion','Output','Current')

### Displays and data analysis  ###

retina.Show('Input','True','margin','0')
retina.Show('SNL_photoreceptors','True','margin','0')
retina.Show('SNL_horizontal','True','margin','0')
retina.Show('SNL_bipolar','True','margin','0')
retina.Show('SNL_amacrine','True','margin','0')
retina.Show('SNL_ganglion','True','margin','0')

# Spatial multimeters of row/col 12th at 200 ms
# row selection
retina.multimeter('spatial','Horizontal cells','SNL_horizontal',{'timeStep','200','rowcol','True','value','12'},'Show','True')
# col selection
retina.multimeter('spatial','Horizontal cells','SNL_horizontal',{'timeStep','200','rowcol','False','value','12'},'Show','True')

# Temporal multimeter of ganglion cell at (5,5)
retina.multimeter('temporal','Ganglion cell','SNL_ganglion',{'x','5','y','5'},'Show','True')

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