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pubscript_attractorEffectsOnSTDP.py
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pubscript_attractorEffectsOnSTDP.py
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import dotmap
import numpy as np
# switch off the need for an X-windows backend:
import matplotlib as mpl
mpl.use('Agg')
#import matplotlib.pyplot as plt
import figures
import helpers
def define_extended_simulation_parameters(metaparams,baseParams):
"""
The helpers of baseParams that should be re-run with a list of settings each.
Node paths of extendedParams have to match those in baseParams.
"""
extendedParams = dotmap.DotMap()
#extendedParams.neurongroups.outputs.userecovery = [True,False]
#extendedParams.neurongroups.inputs.rate = [ 10 , 15 ] # Hz
#extendedParams.neurongroups.outputs.projMult = [ 1.0 , 1.5 , 1.8 ]
#extendedParams.neurongroups.outputs.projMult = np.r_[0.2:4.2:0.2]
#extendedParams.connectionsets.con1.stdprule.learningrate = 1/32.0 * np.array([0.5 , 1.0 , 2.0]) # eta in Auryn
#extendedParams.connectionsets.con1.stdprule.learningrate = 1/32.0 * np.r_[0.2:4.2:0.2]
#extendedParams.connectionsets.con1.maximumweight = np.array([0.5 , 1.0 , 2.0]) # eta in Auryn
#extendedParams.connectionsets.con1.maximumweight = np.array([0.5 , 0.75 , 1.0 , 1.25 , 1.5 , 1.75 , 2.0]) # eta in Auryn
#extendedParams.connectionsets.con1.maximumweight = np.r_[0.2:4.2:0.2]
#extendedParams.connectionsets.con1.maximumweight = np.r_[0.2:8.2:0.2]
#extendedParams.connectionsets.con1.stdprule.weightdependence.attractorStrengthIndicator = [ 0.0 , 0.025 ]
extendedParams.connectionsets.con1.stdprule.weightdependence.attractorStrengthIndicator = [ 0.0 , 0.025 , 0.05 , 0.075 , 0.1 , 0.2 , 0.3 , 0.4 , 0.5]
#extendedParams.connectionsets.con1.stdprule.weightdependence.attractorStrengthIndicator = np.r_[0.0:0.52:0.025]
#extendedParams.connectionsets.con1.stdprule.weightdependence.attractorStrengthIndicator = np.linspace(0,1,num=21)
#extendedParams.connectionsets.con1.stdprule.weightdependence.attractorStrengthIndicator = np.linspace(0,1,num=21)
#extendedParams.connectionsets.con1.stdprule.weightdependence.attractorLocationIndicator = [-0.2 , 0.2]
#extendedParams.connectionsets.con1.stdprule.weightdependence.attractorLocationIndicator = np.linspace(-1,1,num=21)
#extendedParams.connectionsets.con1.stdprule.weightdependence.attractorLocationIndicator = np.linspace(-1,1,num=41)
extendedParams.connectionsets.con1.stdprule.weightdependence.attractorLocationIndicator = np.r_[-0.2:0.4:0.1] # 7 values
#extendedParams.connectionsets.con1.stdprule.weightdependence.attractorLocationIndicator = np.round(np.r_[-0.2:0.4:0.05],3) # 13 values, rounded to 3 digits behind the dot
#extendedParams.connectionsets.con1.stdprule.weightdependence.theMeanSlope = np.linspace(0,0.6,num=4)
#extendedParams.connectionsets.con1.stdprule.weightdependence.theMeanSlope = np.linspace(0,1.0,num=21)
#extendedParams.connectionsets.con1.driftcompensation.stride = np.linspace(0.01,0.07,num=4)
extendedParams.connectionsets.con1.driftcompensation.stride = np.round(np.logspace(np.log10(0.0000001), np.log10(0.0001), num=11 ),8)
#extendedParams.connectionsets.con1.driftcompensation.stride = np.array([ 0.0 , 0.0001 ])
#extendedParams.connectionsets.con1.driftcompensation.stride = np.array([ 0.0 , 0.000001 , 0.00001 , 0.0001 ])
#extendedParams.connectionsets.con1.driftcompensation.stride = np.round(np.logspace(np.log10(0.0000001), np.log10(25), num=21 ),8)
#extendedParams.connectionsets.con1.stdprule.A_plus = [ 0.588 , 0.8 , 0.95 , 1.0 ]
#extendedParams.connectionsets.con1.stdprule.A_plus = np.linspace( 0.2, 1.2, num=11)
return extendedParams
def define_dependent_simulation_parameters():
dependentParams = {}
# key: path of dependent param. value: list of operations to compute the dependent param by:
# value: ( source param path , math operation , value to apply )
#dependentParams['connectionsets.con1.maximumweight'] = [ ('mul','connectionsets.con1.stdprule.learningrate' ,'*', 32.0) ]
#dependentParams['connectionsets.con1.initialweight'] = [ ('mul','connectionsets.con1.stdprule.learningrate' ,'*', 32.0) ]
#dependentParams['connectionsets.con1.initialweight'] = [ ('mul', 'connectionsets.con1.maximumweight' ,'*', 1.0) , ('mul', 'connectionsets.con1.stdprule.learningrate' ,'*', 32.0)]
#dependentParams['connectionsets.con1.stdprule.learningrate'] = [ ('mul','connectionsets.con1.maximumweight' ,'*', 1.0) ]
#dependentParams['connectionsets.con1.initialweight'] = [ ('mul','connectionsets.con1.maximumweight' ,'*', 1.0) ]
return dependentParams
def define_base_simulation_parameters(metaparams):
ms = 1e-3 # a millisecond.
simparams = dotmap.DotMap()
simparams.general.outfileprefix = metaparams.data_basename # dont need this: #+ "_repetition"+str(repetitionID+1)
#simparams.general.testingProtocol.durations = [400] #1500 #116 # seconds
#simparams.general.testingProtocol.intervals = [0.2] # patterns interval in seconds
#simparams.general.testingProtocol.durations = [400,1000] # in seconds
#simparams.general.testingProtocol.intervals = [0.2,10] # patterns interval in seconds
simparams.general.testingProtocol.durations = [300,300,1500] # in seconds
simparams.general.testingProtocol.intervals = [300,0.2,10] # patterns interval in seconds
simparams.general.simtime = sum(simparams.general.testingProtocol.durations)
simparams.recordings.detailedtracking = False
simparams.neurongroups.inputs.N = 2000
#simparams.neurongroups.inputs.type = "PoissonGroup"
#simparams.neurongroups.inputs.type = "FileInputGroup"
#simparams.neurongroups.inputs.type = "StructuredPoissonGroup"
simparams.neurongroups.inputs.type = "PolychronousPoissonGroup"
# only needed by PoissonGroup and StructuredPoissonGroup:
simparams.neurongroups.inputs.rate = 10 # Hz
simparams.neurongroups.inputs.randomseed = -1 # will be redefined in run_simulation()
# only needed by FileInputGroup:
simparams.neurongroups.inputs.rasfilename = '../sim_simon1.data/simon1_fileinputs.ras'
# only needed by StructuredPoissonGroup and PolychronousPoissonGroup:
simparams.neurongroups.inputs.patternduration = 100*ms
simparams.neurongroups.inputs.patterninterval = 200*ms
simparams.neurongroups.inputs.numberofstimuli = 1 # how many different patterns
simparams.neurongroups.inputs.patternOccurrencesFilename = metaparams.data_basename+'_patterntimes.tiser'
# only needed by PolychronousPoissonGroup:
simparams.neurongroups.inputs.N_presenting = 600
simparams.neurongroups.inputs.N_subpresenting = 600
simparams.neurongroups.outputs.type = "Izhikevich2003Group" # not used yet
simparams.neurongroups.outputs.N = 1
simparams.neurongroups.outputs.projMult = 1.0
simparams.neurongroups.outputs.userecovery = False
# Things to test:
# projMult ; maxW ; learningrate ; input rate ; (STDP shape) ; (use_recovery) ; (dt) ; ...
# input rate ; (STDP shape) ; (dt) ; ...
# General connection settings:
simparams.connectionsets.con1.presynaptic = "inputs"
simparams.connectionsets.con1.postsynaptic = "outputs"
simparams.connectionsets.con1.initialweight = 0.85
simparams.connectionsets.con1.maximumweight = 1.0
# Connection type:
#simparams.connectionsets.con1.type = "STDPConnection"
#simparams.connectionsets.con1.type = "GeneralAlltoallSTDPConnection"
#simparams.connectionsets.con1.type = "WDHomeostaticSTDPConnection"
simparams.connectionsets.con1.type = "STDPwdGrowthConnection"
# Linearised STDP parameters:
# simparams.connectionsets.con1.stdprule.A_plus = 0.588 # 0.8
# simparams.connectionsets.con1.stdprule.A_minus = -1
# simparams.connectionsets.con1.stdprule.tau_plus = 28.6 *ms
# simparams.connectionsets.con1.stdprule.tau_minus = 28.6 *ms #22e-3
# simparams.connectionsets.con1.stdprule.learningrate = 0.0325 *1 # eta in Auryn
# Masquelier STDP parameters:
simparams.connectionsets.con1.stdprule.A_plus = 1
simparams.connectionsets.con1.stdprule.A_minus = -0.85
simparams.connectionsets.con1.stdprule.tau_plus = 16.8 *ms
simparams.connectionsets.con1.stdprule.tau_minus = 33.7 *ms #22e-3
simparams.connectionsets.con1.stdprule.learningrate = 1/32.0/1.0 # eta in Auryn
# Song2000 STDP (including learning rate):
#simparams.connectionsets.con1.stdprule.A_plus = 1 # so that the maximum is 1
#simparams.connectionsets.con1.stdprule.A_minus = -1.05
#simparams.connectionsets.con1.stdprule.tau_plus = 20 *ms
#simparams.connectionsets.con1.stdprule.tau_minus = 20 *ms #22e-3
#simparams.connectionsets.con1.stdprule.learningrate = 0.005 # eta in Auryn
# FroemkeDan2002 STDP (unknown learning rate):
#simparams.connectionsets.con1.stdprule.A_plus = 1.01 # =101%
#simparams.connectionsets.con1.stdprule.A_minus = -0.52 # = 52%
#simparams.connectionsets.con1.stdprule.tau_plus = 14.8 *ms
#simparams.connectionsets.con1.stdprule.tau_minus = 33.8 *ms #22e-3
#simparams.connectionsets.con1.stdprule.learningrate = 0.0325 # eta in Auryn
# Weight dependence:
simparams.connectionsets.con1.stdprule.weightdependence.type = "LinearAttractorWeightDependence" # options: AdditiveWeightDependence, LinearAttractorWeightDependence, ...
simparams.connectionsets.con1.stdprule.weightdependence.attractorStrengthIndicator = 0.0
simparams.connectionsets.con1.stdprule.weightdependence.attractorLocationIndicator = 0.5
simparams.connectionsets.con1.stdprule.weightdependence.theMeanSlope = 0.0
# Growth:
simparams.connectionsets.con1.driftcompensation.type = "ConstantGrowth" # options: None, ConstantGrowth, RandomGrowth, RandomJitter, RandomShrinkage, ConstantShrinkage
simparams.connectionsets.con1.driftcompensation.stride = 0.0001 # rough step size in fraction-of-weightrange; take care to keep this fitting to updateinterval_weights !
simparams.connectionsets.con1.driftcompensation.scaleByWeight = False # use or don't use STDP weight dependence for growing weights
simparams.connectionsets.con1.driftcompensation.trainednessMethod = "SumOfLargeWeights" # options: Entropy, Kurtosis, SumOfExponentials, SumOfLargeWeights
simparams.connectionsets.con1.driftcompensation.updateinterval_trainedness = 0.1 # seconds
simparams.connectionsets.con1.driftcompensation.updateinterval_weights = 0.1 # seconds
recordingparams = simparams.recordings
recordingparams.inputs.samplinginterval_poprate = 0.1 # seconds
recordingparams.outputs.samplinginterval_rate = 0.1 # seconds
recordingparams.outputs.samplinginterval_membranes = 'dt' # seconds
recordingparams.outputs.samplinginterval_ampa = 'dt' # seconds
recordingparams.outputs.samplinginterval_nmda = 'dt' # seconds
recordingparams.con1.samplinginterval_weightsum = 1 # seconds
recordingparams.con1.samplinginterval_weightstats = 0.1 # seconds
if simparams.general.simtime > 1000:
recordingparams.con1.samplinginterval_weightmatrix = 10 # seconds
elif simparams.general.simtime > 50:
recordingparams.con1.samplinginterval_weightmatrix = 1 # seconds
else:
recordingparams.con1.samplinginterval_weightmatrix = 0.1 # seconds
# recording times for TimespanMonitor:
duration = 0.5
membraneViews = [ 0 , 4 , 40 , simparams.general.simtime-1 ]
recordingparams.dtintervalsAsFloats.starttimes = membraneViews
recordingparams.dtintervalsAsFloats.stoptimes = (np.asarray(membraneViews) + duration).tolist()
recordingparams.dtintervalsAsStrings.starttimes = ''
recordingparams.dtintervalsAsStrings.stoptimes = ''
for mi in xrange(len(membraneViews)):
#recordingparams.dtintervalslist[mi].start = float(membraneViews[mi])
#recordingparams.dtintervalslist[mi].stop = float(membraneViews[mi])+duration
recordingparams.dtintervalsAsStrings.starttimes += str(float(membraneViews[mi])) + " "
recordingparams.dtintervalsAsStrings.stoptimes += str(float(membraneViews[mi] + duration)) + " "
return simparams
def define_meta_parameters(existingSimfoldername=None):
#### Define meta settings: executable, etc... ####
basefolder = './datafig/'
metaparams = dotmap.DotMap()
#metaparams.executable_path = '../build/debug/examples/'
#metaparams.executable_path = '../auryn_v0.8.0/build/release/examples/'
#metaparams.executable_path = '../auryn_migration_to_v8.0/build/release/examples/'
#metaparams.executable_file = 'sim_json'
metaparams.executable_path = '' # use a symlink in the current folder:
metaparams.executable_file = 'sim_json_symlink'
if existingSimfoldername:
metaparams.datafig_basename = existingSimfoldername
else:
metaparams.datafig_basename = helpers.simulation.find_unique_foldername(basefolder)
metaparams.data_path = basefolder+metaparams.datafig_basename+'/data/'
metaparams.data_basename = metaparams.datafig_basename
metaparams.cache_path = basefolder+metaparams.datafig_basename+'/cache/'
metaparams.cache_basename = metaparams.datafig_basename
metaparams.figures_path = basefolder+metaparams.datafig_basename+'/figures/'
metaparams.figures_basename = metaparams.data_basename
metaparams.numRepetitions = 10
for repetitionID in xrange(metaparams.numRepetitions):
metaparams.repetitionFoldernames[repetitionID] = 'repetition_'+str(repetitionID+1)
return metaparams
def make_figures(params):
try:
#figures.all_paramsets.figuretype_TwoParams2D_Accuracy.makeFig(params, paramdotpathX='connectionsets.con1.maximumweight', paramdotpathY='neurongroups.outputs.projMult')
#figures.all_paramsets.figuretype_TwoParams2D_Accuracy.makeFig(params, paramdotpathX='connectionsets.con1.stdprule.weightdependence.attractorLocationIndicator', paramdotpathY='connectionsets.con1.stdprule.weightdependence.attractorStrengthIndicator')
figures.all_paramsets.figuretype_TwoParams2D_GrowthImprovements.makeFig(params, paramdotpathX='connectionsets.con1.stdprule.weightdependence.attractorLocationIndicator', paramdotpathY='connectionsets.con1.stdprule.weightdependence.attractorStrengthIndicator')
if params.baseParams.recordings.detailedtracking:
# old: makeFigs(params.allsimparams,params.metaparams)
figures.per_paramset.figuretype_FinalWeightsWithRepetitions.makeFigs(params)
# if metaparams.numRepetitions < 5:
# old: makeFigs(params.allsimparams,params.metaparams)
if params.baseParams.general.simtime < 150 and params.metaparams.numRepetitions < 2:
figures.per_repetition.figuretype_DevelopmentOfResponses.makeFigs(params,plotDevelopmentOfWeights=True)
else:
figures.per_repetition.figuretype_DevelopmentOfResponses.makeFigs(params,plotDevelopmentOfWeights=False)
return True
except IOError as e:
print e.message
print e
print type(e)
return False
def main():
params = dotmap.DotMap()
#### Define meta settings: executable, etc... ####
existingSimfoldername = None
#existingSimfoldername = 'sim2016-09-07_trial15'
#existingSimfoldername = 'sim2016-09-07_trial17'
#existingSimfoldername = 'sim2016-09-07_trial20'
#existingSimfoldername = 'sim2016-09-08_trial1'
#existingSimfoldername = 'sim2016-09-12_trial1'
#existingSimfoldername = 'sim2017-01-31_trial3'
#existingSimfoldername = 'from_nemo/sim2017-01-21_trial3'
params.metaparams = define_meta_parameters(existingSimfoldername)
##### Define simulation settings: ####
params.baseParams = define_base_simulation_parameters(params.metaparams)
params.extendedParams = define_extended_simulation_parameters(params.metaparams,params.baseParams)
params.dependentParams = define_dependent_simulation_parameters()
##### Rearrange them: #####
params.flatParamLists = helpers.parameters.flattenExtendedParamsets(params.metaparams, params.baseParams, params.extendedParams)
params.allsimparams = helpers.parameters.crossAllParamsets(params.baseParams, params.flatParamLists.copy())
#nestedPrint(params.allsimparams)
##### Update Dependent parameters: #####
helpers.parameters.adjustDependentParameters(params)
#nestedPrint(params.allsimparams)
##### Run simulation(s) #####
helpers.simulation.run_simulation(params, (existingSimfoldername==None) )
helpers.simulation.rerun_missing_simulations(params)
##### Plot results #####
make_figures(params)
if __name__ == "__main__":
main()