Potjans-Diesmann cortical microcircuit model in NetPyNE (Romaro et al 2021)

 Download zip file 
Help downloading and running models
Accession:266872
The Potjans-Diesmann cortical microcircuit model is a widely used model originally implemented in NEST. Here, we re-implemented the model using NetPyNE, a high-level Python interface to the NEURON simulator, and reproduced the findings of the original publication. We also implemented a method for rescaling the network size which preserves first and second order statistics, building on existing work on network theory. The new implementation enables using more detailed neuron models with multicompartment morphologies and multiple biophysically realistic channels. This opens the model to new research, including the study of dendritic processing, the influence of individual channel parameters, and generally multiscale interactions in the network. The rescaling method provides flexibility to increase or decrease the network size if required when running these more realistic simulations. Finally, NetPyNE facilitates modifying or extending the model using its declarative language; optimizing model parameters; running efficient large-scale parallelized simulations; and analyzing the model through built-in methods, including local field potential calculation and information flow measures.
Reference:
1 . Romaro C, Najman FA, Lytton WW, Roque AC, Dura-Bernal S (2021) NetPyNE Implementation and Scaling of the Potjans-Diesmann Cortical Microcircuit Model Neural Computation 33(7):1993-2032 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Connectionist Network;
Brain Region(s)/Organism: Auditory cortex; Olfactory cortex;
Cell Type(s): Abstract integrate-and-fire leaky neuron; Abstract integrate-and-fire neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: Python; NEURON; NetPyNE;
Model Concept(s): Detailed Neuronal Models; Simplified Models;
Implementer(s): Dura-Bernal, Salvador [salvadordura at gmail.com]; Romaro, Cecilia [ceciliaromaro at gmail.com]; 
'''
NetPyNE version of Potjans and Diesmann thalamocortical network

cfg.py -- contains the simulation configuration (cfg object)

'''

from netpyne import specs


############################################################
#
#                    SIMULATION CONFIGURATION
#
############################################################

cfg = specs.SimConfig() # object of class SimConfig to store simulation configuration

############################################################
# Run options
############################################################

cfg.seeds['stim']=3
cfg.duration = 1*1e3 #6*1e2   # Duration of the simulation, in ms
cfg.dt = 0.025          # Internal integration timestep to use
cfg.verbose = 0     # Show detailed messages
cfg.seeds['m'] = 123
cfg.printPopAvgRates = True
cfg.printRunTime = 1

### Options to save memory in large-scale ismulations
cfg.gatherOnlySimData = False  #Original

# set the following 3 options to False when running large-scale versions of the model (>50% scale) to save memory
cfg.saveCellSecs = True 
cfg.saveCellConns = True
cfg.createPyStruct = True     


###########################################################
# Network Options
###########################################################

# DC=True ;  TH=False; Balanced=True   => Reproduce Figure 7 A1 and A2
# DC=False;  TH=False; Balanced=False  => Reproduce Figure 7 B1 and B2
# DC=False ; TH=False; Balanced=True   => Reproduce Figure 8 A, B, C and D
# DC=False ; TH=False; Balanced=True   and run to 60 s to => Table 6 
# DC=False ; TH=True;  Balanced=True   => Figure 10A. But I want a partial reproduce so I guess Figure 10C is not necessary

# Size of Network. Adjust this constants, please!
cfg.ScaleFactor = 0.10  # 1.0 = 80.000 

# External input DC or Poisson
cfg.DC = False #True = DC // False = Poisson

# Thalamic input in 4th and 6th layer on or off
cfg.TH = False #True = on // False = off

# Balanced and Unbalanced external input as PD article
cfg.Balanced = False #True=Balanced // False=Unbalanced

cfg.simLabel = 'pd_scale-%s_DC-%d_TH-%d_Balanced-%d_dur-%d'%(str(cfg.ScaleFactor), int(cfg.DC), int(cfg.TH), int(cfg.Balanced), int(cfg.duration/1e3))

###########################################################
# Recording and plotting options
###########################################################

cfg.recordStep = 0.1         # Step size in ms to save data (e.g. V traces, LFP, etc)
cfg.filename = cfg.simLabel  # Set file output name
cfg.saveFolder = 'data/'
cfg.savePickle = True         # Save params, network and sim output to pickle file
cfg.saveJson = False
cfg.recordStim = False
cfg.printSynsAfterRule = False
cfg.recordCellsSpikes = ['L2e', 'L2i', 'L4e', 'L4i', 'L5e', 'L5i','L6e', 'L6i'] # record only spikes of cells (not ext stims)

# raster plot (include update in netParams.py)
cfg.analysis['plotRaster']={'include': [], 'timeRange': [100,600], 'popRates' : False, 'figSize' : (6,7),  
	'labels':'overlay', 'orderInverse': True, 'fontSize':16, 'showFig':False, 'saveFig': True}

# statistics plot (include update in netParams.py)
cfg.analysis['plotSpikeStats'] = {'include' : [], 'stats' : ['rate'], 'legendLabels':cfg.recordCellsSpikes,
	'timeRange' : [100,600], 'fontSize': 16, 'figSize': (6,9),'showFig':False, 'saveFig': True}

## Additional NetPyNE analysis
# plot traces
#cfg.recordTraces = {'m': {'var': 'm', 'conds':{'pop': ['L2e', 'L2i']}}}
#cfg.analysis['plotTraces'] = {'include':[('L2e', [0, 1, 2, 3]),('L2i', [0, 1])], 'timeRange': [0,100],'overlay': True,'oneFigPer': 'trace', 'showFig':False, 'saveFig': 'traceEscala3'+str(ScaleFactor)+'.png'}

# plot 2D net structure
# cfg.analysis['plot2Dnet'] = {'include': cfg.recordCellsSpikes, 'saveFig': True,  'figSize': (10,15)}

# plot convergence connectivity as 2D 
# cfg.analysis['plotConn'] = {'includePre': cfg.recordCellsSpikes, 'includePost': cfg.recordCellsSpikes, 'feature': 'convergence', \
#    'synOrConn': 'conn', 'graphType': 'bar', 'saveFig': True, 'figSize': (15, 9)}

# plot firing rate spectrogram  (run for 4 sec)
# cfg.analysis['plotRateSpectrogram'] = {'include': ['allCells'], 'saveFig': True, 'figSize': (15, 7)}

# plot granger causality (run for 4 sec)
# cfg.analysis.granger = {'cells1': ['L2i'], 'cells2': ['L4e'], 'label1': 'L2i', 'label2': 'L4e', 'timeRange': [500,4000], 'saveFig': True, 'binSize': 4}

Loading data, please wait...