def __init__(self):
super(Sim, self).__init__()
self.Net = leabra.Network()
self.SetTags("Net", 'view:"no-inline" desc:"the network -- click to view / edit parameters for layers, prjns, etc"')
self.Pats = etable.Table()
self.SetTags("Pats", 'view:"no-inline" desc:"click to see the full face testing input patterns to use"')
self.PartPats = etable.Table()
self.SetTags("PartPats", 'view:"no-inline" desc:"click to see the partial face testing input patterns to use"')
self.TstTrlLog = etable.Table()
self.SetTags("TstTrlLog", 'view:"no-inline" desc:"testing trial-level log data -- click to see record of network\'s response to each input"')
self.PrjnTable = etable.Table()
self.SetTags("PrjnTable", 'view:"no-inline" desc:"projection of testing data"')
self.Params = params.Sets()
self.SetTags("Params", 'view:"no-inline" desc:"full collection of param sets -- not really interesting for this model"')
self.ParamSet = str()
self.SetTags("ParamSet", 'view:"-" desc:"which set of *additional* parameters to use -- always applies Base and optionaly this next if set -- can use multiple names separated by spaces (don\'t put spaces in ParamSet names!)"')
self.TestEnv = env.FixedTable()
self.SetTags("TestEnv", 'desc:"Testing environment -- manages iterating over testing"')
self.Time = leabra.Time()
self.SetTags("Time", 'desc:"leabra timing parameters and state"')
self.ViewUpdt = leabra.TimeScales.Cycle
self.SetTags("ViewUpdt", 'desc:"at what time scale to update the display during testing? Change to AlphaCyc to make display updating go faster"')
self.TstRecLays = go.Slice_string(["Input", "Emotion", "Gender", "Identity"])
self.SetTags("TstRecLays", 'desc:"names of layers to record activations etc of during testing"')
self.ClustFaces = eplot.Plot2D()
self.SetTags("ClustFaces", 'view:"no-inline" desc:"cluster plot of faces"')
self.ClustEmote = eplot.Plot2D()
self.SetTags("ClustEmote", 'view:"no-inline" desc:"cluster plot of emotions"')
self.ClustGend = eplot.Plot2D()
self.SetTags("ClustGend", 'view:"no-inline" desc:"cluster plot of genders"')
self.ClustIdent = eplot.Plot2D()
self.SetTags("ClustIdent", 'view:"no-inline" desc:"cluster plot of identity"')
self.PrjnRandom = eplot.Plot2D()
self.SetTags("PrjnRandom", 'view:"no-inline" desc:"random projection plot"')
self.PrjnEmoteGend = eplot.Plot2D()
self.SetTags("PrjnEmoteGend", 'view:"no-inline" desc:"projection plot of emotions & gender"')
# internal state - view:"-"
self.Win = 0
self.SetTags("Win", 'view:"-" desc:"main GUI window"')
self.NetView = 0
self.SetTags("NetView", 'view:"-" desc:"the network viewer"')
self.ToolBar = 0
self.SetTags("ToolBar", 'view:"-" desc:"the master toolbar"')
self.TstTrlPlot = 0
self.SetTags("TstTrlPlot", 'view:"-" desc:"the test-trial plot"')
self.ValsTsrs = {}
self.SetTags("ValsTsrs", 'view:"-" desc:"for holding layer values"')
self.IsRunning = False
self.SetTags("IsRunning", 'view:"-" desc:"true if sim is running"')
self.StopNow = False
self.SetTags("StopNow", 'view:"-" desc:"flag to stop running"')
self.vp = 0
self.SetTags("vp", 'view:"-" desc:"viewport"')
def __init__(self):
super(Sim, self).__init__()
self.SpatToObj = float(2)
self.SetTags("SpatToObj", 'def:"2" desc:"spatial to object projection WtScale.Rel strength -- reduce to 1.5, 1 to test"')
self.V1ToSpat1 = float(0.6)
self.SetTags("V1ToSpat1", 'def:"0.6" desc:"V1 to Spat1 projection WtScale.Rel strength -- reduce to .55, .5 to test"')
self.KNaAdapt = False
self.SetTags("KNaAdapt", 'def:"false" desc:"sodium (Na) gated potassium (K) channels that cause neurons to fatigue over time"')
self.CueDur = int(100)
self.SetTags("CueDur", 'def:"100" desc:"number of cycles to present the cue -- 100 by default, 50 to 300 for KNa adapt testing"')
self.Net = leabra.Network()
self.SetTags("Net", 'view:"no-inline" desc:"the network -- click to view / edit parameters for layers, prjns, etc"')
self.Test = TestType.MultiObjs
self.SetTags("Test", 'desc:"select which type of test (input patterns) to use"')
self.MultiObjs = etable.Table()
self.SetTags("MultiObjs", 'view:"no-inline" desc:"click to see these testing input patterns"')
self.StdPosner = etable.Table()
self.SetTags("StdPosner", 'view:"no-inline" desc:"click to see these testing input patterns"')
self.ClosePosner = etable.Table()
self.SetTags("ClosePosner", 'view:"no-inline" desc:"click to see these testing input patterns"')
self.ReversePosner = etable.Table()
self.SetTags("ReversePosner", 'view:"no-inline" desc:"click to see these testing input patterns"')
self.ObjAttn = etable.Table()
self.SetTags("ObjAttn", 'view:"no-inline" desc:"click to see these testing input patterns"')
self.TstTrlLog = etable.Table()
self.SetTags("TstTrlLog", 'view:"no-inline" desc:"testing trial-level log data -- click to see record of network\'s response to each input"')
self.TstStats = etable.Table()
self.SetTags("TstStats", 'view:"no-inline" desc:"aggregate stats on testing data"')
self.Params = params.Sets()
self.SetTags("Params", 'view:"no-inline" desc:"full collection of param sets -- not really interesting for this model"')
self.ParamSet = str()
self.SetTags("ParamSet", 'view:"-" desc:"which set of *additional* parameters to use -- always applies Base and optionaly this next if set -- can use multiple names separated by spaces (don\'t put spaces in ParamSet names!)"')
self.TestEnv = env.FixedTable()
self.SetTags("TestEnv", 'desc:"Testing environment -- manages iterating over testing"')
self.Time = leabra.Time()
self.SetTags("Time", 'desc:"leabra timing parameters and state"')
self.ViewUpdt = leabra.TimeScales.FastSpike
self.SetTags("ViewUpdt", 'desc:"at what time scale to update the display during testing? Change to AlphaCyc to make display updating go faster"')
self.TstRecLays = go.Slice_string(["Input", "V1", "Spat1", "Spat2", "Obj1", "Obj2", "Output"])
self.SetTags("TstRecLays", 'desc:"names of layers to record activations etc of during testing"')
# internal state - view:"-"
self.Win = 0
self.SetTags("Win", 'view:"-" desc:"main GUI window"')
self.NetView = 0
self.SetTags("NetView", 'view:"-" desc:"the network viewer"')
self.ToolBar = 0
self.SetTags("ToolBar", 'view:"-" desc:"the master toolbar"')
self.TstTrlPlot = 0
self.SetTags("TstTrlPlot", 'view:"-" desc:"the test-trial plot"')
self.ValsTsrs = {}
self.SetTags("ValsTsrs", 'view:"-" desc:"for holding layer values"')
self.IsRunning = False
self.SetTags("IsRunning", 'view:"-" desc:"true if sim is running"')
self.StopNow = False
self.SetTags("StopNow", 'view:"-" desc:"flag to stop running"')
self.vp = 0
self.SetTags("vp", 'view:"-" desc:"viewport"')
def __init__(self):
super(Sim, self).__init__()
self.GbarL = float(2.0)
self.SetTags("GbarL", 'def:"2" min:"0" max:"4" step:"0.05" desc:"the leak conductance, which pulls against the excitatory input conductance to determine how hard it is to activate the receiving unit"')
self.Net = leabra.Network()
self.SetTags("Net", 'view:"no-inline" desc:"the network -- click to view / edit parameters for layers, prjns, etc"')
self.Pats = etable.Table()
self.SetTags("Pats", 'view:"no-inline" desc:"click to see the testing input patterns to use (digits)"')
self.TstTrlLog = etable.Table()
self.SetTags("TstTrlLog", 'view:"no-inline" desc:"testing trial-level log data -- click to see record of network\'s response to each input"')
self.Params = params.Sets()
self.ParamSet = str()
self.SetTags("ParamSet", 'view:"-" desc:"which set of *additional* parameters to use -- always applies Base and optionaly this next if set -- can use multiple names separated by spaces (don\'t put spaces in ParamSet names!)"')
self.SetTags("Params", 'view:"no-inline" desc:"full collection of param sets -- not really interesting for this model"')
self.TestEnv = env.FixedTable()
self.SetTags("TestEnv", 'desc:"Testing environment -- manages iterating over testing"')
self.Time = leabra.Time()
self.SetTags("Time", 'desc:"leabra timing parameters and state"')
self.ViewUpdt = leabra.TimeScales.Cycle
self.SetTags("ViewUpdt", 'desc:"at what time scale to update the display during testing? Change to AlphaCyc to make display updating go faster"')
# internal state - view:"-"
self.Win = 0
self.SetTags("Win", 'view:"-" desc:"main GUI window"')
self.NetView = 0
self.SetTags("NetView", 'view:"-" desc:"the network viewer"')
self.ToolBar = 0
self.SetTags("ToolBar", 'view:"-" desc:"the master toolbar"')
self.TstTrlPlot = 0
self.SetTags("TstTrlPlot", 'view:"-" desc:"the test-trial plot"')
self.ValsTsrs = {}
self.SetTags("ValsTsrs", 'view:"-" desc:"for holding layer values"')
self.IsRunning = False
self.SetTags("IsRunning", 'view:"-" desc:"true if sim is running"')
self.StopNow = False
self.SetTags("StopNow", 'view:"-" desc:"flag to stop running"')
self.vp = 0
self.SetTags("vp", 'view:"-" desc:"viewport"')
def py_to_etable(pt):
"""
returns an etable.Table version of given pyet.eTable.
"""
et = etable.Table()
et.Rows = pt.Rows
nc = len(pt.Cols)
for ci in range(nc):
pc = pt.Cols[ci]
cn = pt.ColNames[ci]
tsr = numpy_to_etensor(pc)
et.AddCol(tsr, cn)
for md in pt.MetaData:
et.SetMetaData(md, pt.MetaData[md])
return et
def __init__(self):
super(Sim, self).__init__()
self.Pats = etable.Table()
self.SetTags("Pats", 'view:"no-inline" desc:"test patterns"')
self.PatsTable = 0
self.SetTags("PatsTable", 'view:"-" desc:"view"')
# statistics: note use float64 as that is best for etable.Table
self.Win = 0
self.SetTags("Win", 'view:"-" desc:"main GUI window"')
self.ToolBar = 0
self.SetTags("ToolBar", 'view:"-" desc:"the master toolbar"')
self.vp = 0
self.SetTags("vp", 'view:"-" desc:"viewport"')
def ClustPlot(ss, plt, dt, colNm):
"""
ClustPlot does one cluster plot on given table column
"""
ix = etable.NewIdxView(dt)
smat = simat.SimMat()
smat.TableColStd(ix, colNm, "Name", False, metric.Euclidean)
pt = etable.Table()
clust.Plot(pt, clust.GlomStd(smat, clust.Min), smat)
plt.InitName(plt, colNm)
plt.Params.Title = "Cluster Plot of Faces " + colNm
plt.Params.XAxisCol = "X"
plt.SetTable(pt)
plt.SetColParams("X", eplot.Off, eplot.FixMin, 0, eplot.FloatMax, 0)
plt.SetColParams("Y", eplot.On, eplot.FixMin, 0, eplot.FloatMax, 0)
plt.SetColParams("Label", eplot.On, eplot.FixMin, 0, eplot.FloatMax, 0)
def __init__(self):
super(Sim, self).__init__()
self.ExcitLateralScale = float(0.2)
self.SetTags(
"ExcitLateralScale",
'def:"0.2" desc:"excitatory lateral (recurrent) WtScale.Rel value"'
)
self.InhibLateralScale = float(0.2)
self.SetTags(
"InhibLateralScale",
'def:"0.2" desc:"inhibitory lateral (recurrent) WtScale.Abs value"'
)
self.ExcitLateralLearn = True
self.SetTags(
"ExcitLateralLearn",
'def:"true" desc:"do excitatory lateral (recurrent) connections learn?"'
)
self.Net = leabra.Network()
self.SetTags(
"Net",
'view:"no-inline" desc:"the network -- click to view / edit parameters for layers, prjns, etc"'
)
self.Probes = etable.Table()
self.SetTags("Probes", 'view:"no-inline" desc:"probe inputs"')
self.TrnEpcLog = etable.Table()
self.SetTags("TrnEpcLog",
'view:"no-inline" desc:"training epoch-level log data"')
self.TstEpcLog = etable.Table()
self.SetTags("TstEpcLog",
'view:"no-inline" desc:"testing epoch-level log data"')
self.TstTrlLog = etable.Table()
self.SetTags("TstTrlLog",
'view:"no-inline" desc:"testing trial-level log data"')
self.RunLog = etable.Table()
self.SetTags("RunLog",
'view:"no-inline" desc:"summary log of each run"')
self.RunStats = etable.Table()
self.SetTags("RunStats",
'view:"no-inline" desc:"aggregate stats on all runs"')
self.Params = params.Sets()
self.SetTags("Params",
'view:"no-inline" desc:"full collection of param sets"')
self.ParamSet = str()
self.SetTags(
"ParamSet",
'view:"-" desc:"which set of *additional* parameters to use -- always applies Base and optionaly this next if set -- can use multiple names separated by spaces (don\'t put spaces in ParamSet names!)"'
)
self.V1onWts = etensor.Float32()
self.SetTags("V1onWts",
'view:"-" desc:"weights from input to V1 layer"')
self.V1offWts = etensor.Float32()
self.SetTags("V1offWts",
'view:"-" desc:"weights from input to V1 layer"')
self.V1Wts = etensor.Float32()
self.SetTags(
"V1Wts",
'view:"no-inline" desc:"net on - off weights from input to V1 layer"'
)
self.MaxRuns = int(1)
self.SetTags("MaxRuns",
'desc:"maximum number of model runs to perform"')
self.MaxEpcs = int(100)
self.SetTags("MaxEpcs",
'desc:"maximum number of epochs to run per model run"')
self.MaxTrls = int(100)
self.SetTags("MaxTrls",
'desc:"maximum number of training trials per epoch"')
self.NZeroStop = int(-1)
self.SetTags(
"NZeroStop",
'desc:"if a positive number, training will stop after this many epochs with zero SSE"'
)
self.TrainEnv = ImgEnv()
self.SetTags("TrainEnv",
'desc:"Training environment -- visual images"')
self.TestEnv = env.FixedTable()
self.SetTags(
"TestEnv",
'desc:"Testing environment -- manages iterating over testing"')
self.Time = leabra.Time()
self.SetTags("Time", 'desc:"leabra timing parameters and state"')
self.ViewOn = True
self.SetTags(
"ViewOn",
'desc:"whether to update the network view while running"')
self.TrainUpdt = leabra.TimeScales.AlphaCycle
self.SetTags(
"TrainUpdt",
'desc:"at what time scale to update the display during training? Anything longer than Epoch updates at Epoch in this model"'
)
self.TestUpdt = leabra.TimeScales.Cycle
self.SetTags(
"TestUpdt",
'desc:"at what time scale to update the display during testing? Anything longer than Epoch updates at Epoch in this model"'
)
self.LayStatNms = go.Slice_string(["V1"])
self.SetTags(
"LayStatNms",
'desc:"names of layers to collect more detailed stats on (avg act, etc)"'
)
# statistics: note use float64 as that is best for etable.Table
self.Win = 0
self.SetTags("Win", 'view:"-" desc:"main GUI window"')
self.NetView = 0
self.SetTags("NetView", 'view:"-" desc:"the network viewer"')
self.ToolBar = 0
self.SetTags("ToolBar", 'view:"-" desc:"the master toolbar"')
self.CurImgGrid = 0
self.SetTags("CurImgGrid",
'view:"-" desc:"the current image grid view"')
self.WtsGrid = 0
self.SetTags("WtsGrid", 'view:"-" desc:"the weights grid view"')
self.TrnEpcPlot = 0
self.SetTags("TrnEpcPlot", 'view:"-" desc:"the training epoch plot"')
self.TstEpcPlot = 0
self.SetTags("TstEpcPlot", 'view:"-" desc:"the testing epoch plot"')
self.TstTrlPlot = 0
self.SetTags("TstTrlPlot", 'view:"-" desc:"the test-trial plot"')
self.RunPlot = 0
self.SetTags("RunPlot", 'view:"-" desc:"the run plot"')
self.TrnEpcFile = 0
self.SetTags("TrnEpcFile", 'view:"-" desc:"log file"')
self.RunFile = 0
self.SetTags("RunFile", 'view:"-" desc:"log file"')
self.ValsTsrs = {}
self.SetTags("ValsTsrs", 'view:"-" desc:"for holding layer values"')
self.IsRunning = False
self.SetTags("IsRunning", 'view:"-" desc:"true if sim is running"')
self.StopNow = False
self.SetTags("StopNow", 'view:"-" desc:"flag to stop running"')
self.NeedsNewRun = False
self.SetTags(
"NeedsNewRun",
'view:"-" desc:"flag to initialize NewRun if last one finished"')
self.RndSeed = int(1)
self.SetTags("RndSeed", 'view:"-" desc:"the current random seed"')
self.vp = 0
self.SetTags("vp", 'view:"-" desc:"viewport"')
def __init__(self):
super(Sim, self).__init__()
self.Net = leabra.Network()
self.SetTags(
"Net",
'view:"no-inline" desc:"the network -- click to view / edit parameters for layers, prjns, etc"'
)
self.Learn = LearnType.Hebbian
self.SetTags("Learn", 'desc:"select which type of learning to use"')
self.Pats = PatsType.Easy
self.SetTags("Pats", 'desc:"select which type of patterns to use"')
self.Easy = etable.Table()
self.SetTags(
"Easy",
'view:"no-inline" desc:"easy training patterns -- can be learned with Hebbian"'
)
self.Hard = etable.Table()
self.SetTags(
"Hard",
'view:"no-inline" desc:"hard training patterns -- require error-driven"'
)
self.Impossible = etable.Table()
self.SetTags(
"Impossible",
'view:"no-inline" desc:"impossible training patterns -- require error-driven + hidden layer"'
)
self.TrnEpcLog = etable.Table()
self.SetTags("TrnEpcLog",
'view:"no-inline" desc:"training epoch-level log data"')
self.TstEpcLog = etable.Table()
self.SetTags("TstEpcLog",
'view:"no-inline" desc:"testing epoch-level log data"')
self.TstTrlLog = etable.Table()
self.SetTags("TstTrlLog",
'view:"no-inline" desc:"testing trial-level log data"')
self.RunLog = etable.Table()
self.SetTags("RunLog",
'view:"no-inline" desc:"summary log of each run"')
self.RunStats = etable.Table()
self.SetTags("RunStats",
'view:"no-inline" desc:"aggregate stats on all runs"')
self.Params = params.Sets()
self.SetTags("Params",
'view:"no-inline" desc:"full collection of param sets"')
self.ParamSet = str()
self.SetTags(
"ParamSet",
'view:"-" desc:"which set of *additional* parameters to use -- always applies Base and optionaly this next if set"'
)
self.MaxRuns = int(10)
self.SetTags("MaxRuns",
'desc:"maximum number of model runs to perform"')
self.MaxEpcs = int(40)
self.SetTags("MaxEpcs",
'desc:"maximum number of epochs to run per model run"')
self.NZeroStop = int(5)
self.SetTags(
"NZeroStop",
'desc:"if a positive number, training will stop after this many epochs with zero SSE"'
)
self.TrainEnv = env.FixedTable()
self.SetTags(
"TrainEnv",
'desc:"Training environment -- contains everything about iterating over input / output patterns over training"'
)
self.TestEnv = env.FixedTable()
self.SetTags(
"TestEnv",
'desc:"Testing environment -- manages iterating over testing"')
self.Time = leabra.Time()
self.SetTags("Time", 'desc:"leabra timing parameters and state"')
self.ViewOn = True
self.SetTags(
"ViewOn",
'desc:"whether to update the network view while running"')
self.TrainUpdt = leabra.TimeScales.AlphaCycle
self.SetTags(
"TrainUpdt",
'desc:"at what time scale to update the display during training? Anything longer than Epoch updates at Epoch in this model"'
)
self.TestUpdt = leabra.TimeScales.Cycle
self.SetTags(
"TestUpdt",
'desc:"at what time scale to update the display during testing? Anything longer than Epoch updates at Epoch in this model"'
)
self.TestInterval = int(5)
self.SetTags(
"TestInterval",
'desc:"how often to run through all the test patterns, in terms of training epochs -- can use 0 or -1 for no testing"'
)
self.LayStatNms = go.Slice_string(["Input", "Output"])
self.SetTags(
"LayStatNms",
'desc:"names of layers to collect more detailed stats on (avg act, etc)"'
)
# statistics: note use float64 as that is best for etable.Table
self.TrlErr = float(0)
self.SetTags(
"TrlErr",
'inactive:"+" desc:"1 if trial was error, 0 if correct -- based on SSE = 0 (subject to .5 unit-wise tolerance)"'
)
self.TrlSSE = float(0)
self.SetTags("TrlSSE",
'inactive:"+" desc:"current trial\'s sum squared error"')
self.TrlAvgSSE = float(0)
self.SetTags(
"TrlAvgSSE",
'inactive:"+" desc:"current trial\'s average sum squared error"')
self.TrlCosDiff = float(0)
self.SetTags("TrlCosDiff",
'inactive:"+" desc:"current trial\'s cosine difference"')
self.EpcSSE = float(0)
self.SetTags(
"EpcSSE",
'inactive:"+" desc:"last epoch\'s total sum squared error"')
self.EpcAvgSSE = float(0)
self.SetTags(
"EpcAvgSSE",
'inactive:"+" desc:"last epoch\'s average sum squared error (average over trials, and over units within layer)"'
)
self.EpcPctErr = float(0)
self.SetTags("EpcPctErr",
'inactive:"+" desc:"last epoch\'s average TrlErr"')
self.EpcPctCor = float(0)
self.SetTags("EpcPctCor",
'inactive:"+" desc:"1 - last epoch\'s average TrlErr"')
self.EpcCosDiff = float(0)
self.SetTags(
"EpcCosDiff",
'inactive:"+" desc:"last epoch\'s average cosine difference for output layer (a normalized error measure, maximum of 1 when the minus phase exactly matches the plus)"'
)
self.FirstZero = int(-1)
self.SetTags(
"FirstZero",
'inactive:"+" desc:"epoch at when SSE first went to zero"')
self.NZero = int(0)
self.SetTags(
"NZero",
'inactive:"+" desc:"number of epochs in a row with zero SSE"')
# internal state - view:"-"
self.SumErr = float(0)
self.SetTags(
"SumErr",
'view:"-" inactive:"+" desc:"sum to increment as we go through epoch"'
)
self.SumSSE = float(0)
self.SetTags(
"SumSSE",
'view:"-" inactive:"+" desc:"sum to increment as we go through epoch"'
)
self.SumAvgSSE = float(0)
self.SetTags(
"SumAvgSSE",
'view:"-" inactive:"+" desc:"sum to increment as we go through epoch"'
)
self.SumCosDiff = float(0)
self.SetTags(
"SumCosDiff",
'view:"-" inactive:"+" desc:"sum to increment as we go through epoch"'
)
self.Win = 0
self.SetTags("Win", 'view:"-" desc:"main GUI window"')
self.NetView = 0
self.SetTags("NetView", 'view:"-" desc:"the network viewer"')
self.ToolBar = 0
self.SetTags("ToolBar", 'view:"-" desc:"the master toolbar"')
self.TrnEpcPlot = 0
self.SetTags("TrnEpcPlot", 'view:"-" desc:"the training epoch plot"')
self.TstEpcPlot = 0
self.SetTags("TstEpcPlot", 'view:"-" desc:"the testing epoch plot"')
self.TstTrlPlot = 0
self.SetTags("TstTrlPlot", 'view:"-" desc:"the test-trial plot"')
self.RunPlot = 0
self.SetTags("RunPlot", 'view:"-" desc:"the run plot"')
self.TrnEpcFile = 0
self.SetTags("TrnEpcFile", 'view:"-" desc:"log file"')
self.RunFile = 0
self.SetTags("RunFile", 'view:"-" desc:"log file"')
self.ValsTsrs = {}
self.SetTags("ValsTsrs", 'view:"-" desc:"for holding layer values"')
self.IsRunning = False
self.SetTags("IsRunning", 'view:"-" desc:"true if sim is running"')
self.StopNow = False
self.SetTags("StopNow", 'view:"-" desc:"flag to stop running"')
self.NeedsNewRun = False
self.SetTags(
"NeedsNewRun",
'view:"-" desc:"flag to initialize NewRun if last one finished"')
self.RndSeed = int(1)
self.SetTags("RndSeed", 'view:"-" desc:"the current random seed"')
self.vp = 0
self.SetTags("vp", 'view:"-" desc:"viewport"')
def __init__(self):
super(Sim, self).__init__()
self.Noise = float(0.01)
self.SetTags(
"Noise",
'min:"0" step:"0.01" desc:"the variance parameter for Gaussian noise added to unit activations on every cycle"'
)
self.KNaAdapt = False
self.SetTags(
"KNaAdapt",
'desc:"apply sodium-gated potassium adaptation mechanisms that cause the neuron to reduce spiking over time"'
)
self.CycPerQtr = int(25)
self.SetTags(
"CycPerQtr",
'def:"25,250" desc:"total number of cycles per quarter to run -- increase to 250 when testing adaptation"'
)
self.Net = leabra.Network()
self.SetTags(
"Net",
'view:"no-inline" desc:"the network -- click to view / edit parameters for layers, prjns, etc"'
)
self.TstCycLog = etable.Table()
self.SetTags(
"TstCycLog",
'view:"no-inline" desc:"testing trial-level log data -- click to see record of network\'s response to each input"'
)
self.Params = params.Sets()
self.SetTags(
"Params",
'view:"no-inline" desc:"full collection of param sets -- not really interesting for this model"'
)
self.ParamSet = str()
self.SetTags(
"ParamSet",
'view:"-" desc:"which set of *additional* parameters to use -- always applies Base and optionaly this next if set -- can use multiple names separated by spaces (don\'t put spaces in ParamSet names!)"'
)
self.Time = leabra.Time()
self.SetTags("Time", 'desc:"leabra timing parameters and state"')
self.ViewUpdt = leabra.TimeScales.Cycle
self.SetTags(
"ViewUpdt",
'desc:"at what time scale to update the display during testing? Change to AlphaCyc to make display updating go faster"'
)
self.TstRecLays = go.Slice_string(["NeckerCube"])
self.SetTags(
"TstRecLays",
'desc:"names of layers to record activations etc of during testing"'
)
# internal state - view:"-"
self.Win = 0
self.SetTags("Win", 'view:"-" desc:"main GUI window"')
self.NetView = 0
self.SetTags("NetView", 'view:"-" desc:"the network viewer"')
self.ToolBar = 0
self.SetTags("ToolBar", 'view:"-" desc:"the master toolbar"')
self.TstCycPlot = 0
self.SetTags("TstCycPlot", 'view:"-" desc:"the test-trial plot"')
self.ValsTsrs = {}
self.SetTags("ValsTsrs", 'view:"-" desc:"for holding layer values"')
self.IsRunning = False
self.SetTags("IsRunning", 'view:"-" desc:"true if sim is running"')
self.StopNow = False
self.SetTags("StopNow", 'view:"-" desc:"flag to stop running"')
self.vp = 0
self.SetTags("vp", 'view:"-" desc:"viewport"')
def __init__(self):
super(Sim, self).__init__()
self.BurstDaGain = float(1)
self.SetTags(
"BurstDaGain",
'min:"0" step:"0.1" desc:"strength of dopamine bursts: 1 default -- reduce for PD OFF, increase for PD ON"'
)
self.DipDaGain = float(1)
self.SetTags(
"DipDaGain",
'min:"0" step:"0.1" desc:"strength of dopamine dips: 1 default -- reduce to siulate D2 agonists"'
)
self.Net = pbwm.Network()
self.SetTags(
"Net",
'view:"no-inline" desc:"the network -- click to view / edit parameters for layers, prjns, etc"'
)
self.TrnEpcLog = etable.Table()
self.SetTags("TrnEpcLog",
'view:"no-inline" desc:"training epoch-level log data"')
self.TstEpcLog = etable.Table()
self.SetTags("TstEpcLog",
'view:"no-inline" desc:"testing epoch-level log data"')
self.TstTrlLog = etable.Table()
self.SetTags("TstTrlLog",
'view:"no-inline" desc:"testing trial-level log data"')
self.MtxInputWts = etensor.Float32()
self.SetTags(
"MtxInputWts",
'view:"no-inline" desc:"weights from input to hidden layer"')
self.RunLog = etable.Table()
self.SetTags("RunLog",
'view:"no-inline" desc:"summary log of each run"')
self.RunStats = etable.Table()
self.SetTags("RunStats",
'view:"no-inline" desc:"aggregate stats on all runs"')
self.Params = params.Sets()
self.SetTags("Params",
'view:"no-inline" desc:"full collection of param sets"')
self.ParamSet = str()
self.SetTags(
"ParamSet",
'view:"-" desc:"which set of *additional* parameters to use -- always applies Base and optionaly this next if set -- can use multiple names separated by spaces (don\'t put spaces in ParamSet names!)"'
)
self.MaxRuns = int(1)
self.SetTags("MaxRuns",
'desc:"maximum number of model runs to perform"')
self.MaxEpcs = int(30)
self.SetTags("MaxEpcs",
'desc:"maximum number of epochs to run per model run"')
self.MaxTrls = int(100)
self.SetTags("MaxTrls",
'desc:"maximum number of training trials per epoch"')
self.TrainEnv = BanditEnv()
self.SetTags("TrainEnv",
'desc:"Training environment -- bandit environment"')
self.Time = leabra.Time()
self.SetTags("Time", 'desc:"leabra timing parameters and state"')
self.ViewOn = True
self.SetTags(
"ViewOn",
'desc:"whether to update the network view while running"')
self.TrainUpdt = leabra.TimeScales.AlphaCycle
self.SetTags(
"TrainUpdt",
'desc:"at what time scale to update the display during training? Anything longer than Epoch updates at Epoch in this model"'
)
self.TestUpdt = leabra.TimeScales.AlphaCycle
self.SetTags(
"TestUpdt",
'desc:"at what time scale to update the display during testing? Anything longer than Epoch updates at Epoch in this model"'
)
self.TstRecLays = go.Slice_string(["MatrixGo", "MatrixNoGo"])
self.SetTags(
"TstRecLays",
'desc:"names of layers to record activations etc of during testing"'
)
# internal state - view:"-"
self.Win = 0
self.SetTags("Win", 'view:"-" desc:"main GUI window"')
self.NetView = 0
self.SetTags("NetView", 'view:"-" desc:"the network viewer"')
self.ToolBar = 0
self.SetTags("ToolBar", 'view:"-" desc:"the master toolbar"')
self.WtsGrid = 0
self.SetTags("WtsGrid", 'view:"-" desc:"the weights grid view"')
self.TrnEpcPlot = 0
self.SetTags("TrnEpcPlot", 'view:"-" desc:"the training epoch plot"')
self.TstEpcPlot = 0
self.SetTags("TstEpcPlot", 'view:"-" desc:"the testing epoch plot"')
self.TstTrlPlot = 0
self.SetTags("TstTrlPlot", 'view:"-" desc:"the test-trial plot"')
self.RunPlot = 0
self.SetTags("RunPlot", 'view:"-" desc:"the run plot"')
self.TrnEpcFile = 0
self.SetTags("TrnEpcFile", 'view:"-" desc:"log file"')
self.RunFile = 0
self.SetTags("RunFile", 'view:"-" desc:"log file"')
self.ValsTsrs = {}
self.SetTags("ValsTsrs", 'view:"-" desc:"for holding layer values"')
self.IsRunning = False
self.SetTags("IsRunning", 'view:"-" desc:"true if sim is running"')
self.StopNow = False
self.SetTags("StopNow", 'view:"-" desc:"flag to stop running"')
self.NeedsNewRun = False
self.SetTags(
"NeedsNewRun",
'view:"-" desc:"flag to initialize NewRun if last one finished"')
self.RndSeed = int(1)
self.SetTags("RndSeed", 'view:"-" desc:"the current random seed"')
self.vp = 0
self.SetTags("vp", 'view:"-" desc:"viewport"')
def __init__(self):
super(Sim, self).__init__()
self.Words1 = "attention"
self.SetTags("Words1", 'desc:"space-separated words to test the network with"')
self.Words2 = "binding"
self.SetTags("Words2", 'desc:"space-separated words to test the network with"')
self.ExcitLateralScale = float(0.05)
self.SetTags("ExcitLateralScale", 'def:"0.05" desc:"excitatory lateral (recurrent) WtScale.Rel value"')
self.InhibLateralScale = float(0.05)
self.SetTags("InhibLateralScale", 'def:"0.05" desc:"inhibitory lateral (recurrent) WtScale.Abs value"')
self.ExcitLateralLearn = True
self.SetTags("ExcitLateralLearn", 'def:"true" desc:"do excitatory lateral (recurrent) connections learn?"')
self.WtWordsThr = float(0.75)
self.SetTags("WtWordsThr", 'def:"0.75" desc:"threshold for weight strength for including in WtWords"')
self.Net = leabra.Network()
self.SetTags("Net", 'view:"no-inline" desc:"the network -- click to view / edit parameters for layers, prjns, etc"')
self.TrnEpcLog = etable.Table()
self.SetTags("TrnEpcLog", 'view:"no-inline" desc:"training epoch-level log data"')
self.TstEpcLog = etable.Table()
self.SetTags("TstEpcLog", 'view:"no-inline" desc:"testing epoch-level log data"')
self.TstQuizLog = etable.Table()
self.SetTags("TstQuizLog", 'view:"no-inline" desc:"testing quiz epoch-level log data"')
self.TstTrlLog = etable.Table()
self.SetTags("TstTrlLog", 'view:"no-inline" desc:"testing trial-level log data"')
self.RunLog = etable.Table()
self.SetTags("RunLog", 'view:"no-inline" desc:"summary log of each run"')
self.RunStats = etable.Table()
self.SetTags("RunStats", 'view:"no-inline" desc:"aggregate stats on all runs"')
self.Params = params.Sets()
self.SetTags("Params", 'view:"no-inline" desc:"full collection of param sets"')
self.ParamSet = str()
self.SetTags("ParamSet", 'view:"-" desc:"which set of *additional* parameters to use -- always applies Base and optionaly this next if set -- can use multiple names separated by spaces (don\'t put spaces in ParamSet names!)"')
self.Tag = str()
self.SetTags("Tag", 'desc:"extra tag string to add to any file names output from sim (e.g., weights files, log files, params for run)"')
self.MaxRuns = int(1)
self.SetTags("MaxRuns", 'desc:"maximum number of model runs to perform"')
self.MaxEpcs = int(50)
self.SetTags("MaxEpcs", 'desc:"maximum number of epochs to run per model run"')
self.NZeroStop = int(-1)
self.SetTags("NZeroStop", 'desc:"if a positive number, training will stop after this many epochs with zero SSE"')
self.TrainEnv = SemEnv()
self.SetTags("TrainEnv", 'desc:"Training environment -- training paragraphs"')
self.TestEnv = SemEnv()
self.SetTags("TestEnv", 'desc:"Testing environment -- manages iterating over testing"')
self.QuizEnv = SemEnv()
self.SetTags("QuizEnv", 'desc:"Quiz environment -- manages iterating over testing"')
self.Time = leabra.Time()
self.SetTags("Time", 'desc:"leabra timing parameters and state"')
self.ViewOn = True
self.SetTags("ViewOn", 'desc:"whether to update the network view while running"')
self.TrainUpdt = leabra.TimeScales.AlphaCycle
self.SetTags("TrainUpdt", 'desc:"at what time scale to update the display during training? Anything longer than Epoch updates at Epoch in this model"')
self.TestUpdt = leabra.TimeScales.AlphaCycle
self.SetTags("TestUpdt", 'desc:"at what time scale to update the display during testing? Anything longer than Epoch updates at Epoch in this model"')
self.LayStatNms = go.Slice_string(["Hidden"])
self.SetTags("LayStatNms", 'desc:"names of layers to collect more detailed stats on (avg act, etc)"')
# statistics: note use float64 as that is best for etable.Table
self.TstWords = str()
self.SetTags("TstWords", 'inactive:"+" desc:"words that were tested (short form)"')
self.TstWordsCorrel = float()
self.SetTags("TstWordsCorrel", 'inactive:"+" desc:"correlation between hidden pattern for Words1 vs. Words2"')
self.TstQuizPctCor = float()
self.SetTags("TstQuizPctCor", 'inactive:"+" desc:"proportion correct for the quiz"')
self.EpcPerTrlMSec = float()
self.SetTags("EpcPerTrlMSec", 'view:"-" desc:"how long did the epoch take per trial in wall-clock milliseconds"')
self.Win = 0
self.SetTags("Win", 'view:"-" desc:"main GUI window"')
self.NetView = 0
self.SetTags("NetView", 'view:"-" desc:"the network viewer"')
self.ToolBar = 0
self.SetTags("ToolBar", 'view:"-" desc:"the master toolbar"')
self.TrnEpcPlot = 0
self.SetTags("TrnEpcPlot", 'view:"-" desc:"the training epoch plot"')
self.TstEpcPlot = 0
self.SetTags("TstEpcPlot", 'view:"-" desc:"the testing epoch plot"')
self.TstQuizPlot = 0
self.SetTags("TstQuizPlot", 'view:"-" desc:"the testing quiz epoch plot"')
self.TstTrlPlot = 0
self.SetTags("TstTrlPlot", 'view:"-" desc:"the test-trial plot"')
self.RunPlot = 0
self.SetTags("RunPlot", 'view:"-" desc:"the run plot"')
self.TrnEpcFile = 0
self.SetTags("TrnEpcFile", 'view:"-" desc:"log file"')
self.RunFile = 0
self.SetTags("RunFile", 'view:"-" desc:"log file"')
self.ValsTsrs = {}
self.SetTags("ValsTsrs", 'view:"-" desc:"for holding layer values"')
self.SaveWts = False
self.SetTags("SaveWts", 'view:"-" desc:"for command-line run only, auto-save final weights after each run"')
self.NoGui = False
self.SetTags("NoGui", 'view:"-" desc:"if true, runing in no GUI mode"')
self.LogSetParams = False
self.SetTags("LogSetParams", 'view:"-" desc:"if true, print message for all params that are set"')
self.IsRunning = False
self.SetTags("IsRunning", 'view:"-" desc:"true if sim is running"')
self.InQuiz = False
self.SetTags("InQuiz", 'view:"-" desc:"true if in quiz"')
self.StopNow = False
self.SetTags("StopNow", 'view:"-" desc:"flag to stop running"')
self.NeedsNewRun = False
self.SetTags("NeedsNewRun", 'view:"-" desc:"flag to initialize NewRun if last one finished"')
self.RndSeed = int(1)
self.SetTags("RndSeed", 'view:"-" desc:"the current random seed"')
self.LastEpcTime = 0
self.SetTags("LastEpcTime", 'view:"-" desc:"timer for last epoch"')
self.vp = 0
self.SetTags("vp", 'view:"-" desc:"viewport"')
def __init__(self):
super(Sim, self).__init__()
self.BidirNet = False
self.SetTags(
"BidirNet",
'desc:"if true, use the bidirectionally-connected network -- otherwise use the simpler feedforward network"'
)
self.TrainedWts = False
self.SetTags(
"TrainedWts",
'desc:"simulate trained weights by having higher variance and Gaussian distributed weight values -- otherwise lower variance, uniform"'
)
self.InputPct = float(20)
self.SetTags(
"InputPct",
'def:"20" min:"5" max:"50" step:"1" desc:"percent of active units in input layer (literally number of active units, because input has 100 units total)"'
)
self.FFFBInhib = False
self.SetTags(
"FFFBInhib",
'def:"false" desc:"use feedforward, feedback (FFFB) computed inhibition instead of unit-level inhibition"'
)
self.HiddenGbarI = float(0.4)
self.SetTags(
"HiddenGbarI",
'def:"0.4" min:"0" step:"0.05" desc:"inhibitory conductance strength for inhibition into Hidden layer"'
)
self.InhibGbarI = float(0.75)
self.SetTags(
"InhibGbarI",
'def:"0.75" min:"0" step:"0.05" desc:"inhibitory conductance strength for inhibition into Inhib layer (self-inhibition -- tricky!)"'
)
self.FFinhibWtScale = float(1.0)
self.SetTags(
"FFinhibWtScale",
'def:"1" min:"0" step:"0.1" desc:"feedforward (FF) inhibition relative strength: for FF projections into Inhib neurons"'
)
self.FBinhibWtScale = float(1.0)
self.SetTags(
"FBinhibWtScale",
'def:"1" min:"0" step:"0.1" desc:"feedback (FB) inhibition relative strength: for projections into Inhib neurons"'
)
self.HiddenGTau = float(40)
self.SetTags(
"HiddenGTau",
'def:"40" min:"1" step:"1" desc:"time constant (tau) for updating G conductances into Hidden neurons -- much slower than std default of 1.4"'
)
self.InhibGTau = float(20)
self.SetTags(
"InhibGTau",
'def:"20" min:"1" step:"1" desc:"time constant (tau) for updating G conductances into Inhib neurons -- much slower than std default of 1.4, but 2x faster than Hidden"'
)
self.FmInhibWtScaleAbs = float(1)
self.SetTags(
"FmInhibWtScaleAbs",
'def:"1" desc:"absolute weight scaling of projections from inhibition onto hidden and inhib layers -- this must be set to 0 to turn off the connection-based inhibition when using the FFFBInhib computed inbhition"'
)
self.NetFF = leabra.Network()
self.SetTags(
"NetFF",
'view:"no-inline" desc:"the feedforward network -- click to view / edit parameters for layers, prjns, etc"'
)
self.NetBidir = leabra.Network()
self.SetTags(
"NetBidir",
'view:"no-inline" desc:"the bidirectional network -- click to view / edit parameters for layers, prjns, etc"'
)
self.TstCycLog = etable.Table()
self.SetTags(
"TstCycLog",
'view:"no-inline" desc:"testing trial-level log data -- click to see record of network\'s response to each input"'
)
self.Params = params.Sets()
self.SetTags(
"Params",
'view:"no-inline" desc:"full collection of param sets -- not really interesting for this model"'
)
self.ParamSet = str()
self.SetTags(
"ParamSet",
'view:"-" desc:"which set of *additional* parameters to use -- always applies Base and optionaly this next if set -- can use multiple names separated by spaces (don\'t put spaces in ParamSet names!)"'
)
self.Time = leabra.Time()
self.SetTags("Time", 'desc:"leabra timing parameters and state"')
self.ViewUpdt = leabra.TimeScales.Cycle
self.SetTags(
"ViewUpdt",
'desc:"at what time scale to update the display during testing? Change to AlphaCyc to make display updating go faster"'
)
self.TstRecLays = go.Slice_string(["Hidden", "Inhib"])
self.SetTags(
"TstRecLays",
'desc:"names of layers to record activations etc of during testing"'
)
self.Pats = etable.Table()
self.SetTags(
"Pats",
'view:"no-inline" desc:"the input patterns to use -- randomly generated"'
)
# internal state - view:"-"
self.Win = 0
self.SetTags("Win", 'view:"-" desc:"main GUI window"')
self.NetViewFF = 0
self.SetTags("NetViewFF", 'view:"-" desc:"the network viewer"')
self.NetViewBidir = 0
self.SetTags("NetViewBidir", 'view:"-" desc:"the network viewer"')
self.ToolBar = 0
self.SetTags("ToolBar", 'view:"-" desc:"the master toolbar"')
self.TstCycPlot = 0
self.SetTags("TstCycPlot", 'view:"-" desc:"the test-trial plot"')
self.ValsTsrs = {}
self.SetTags("ValsTsrs", 'view:"-" desc:"for holding layer values"')
self.IsRunning = False
self.SetTags("IsRunning", 'view:"-" desc:"true if sim is running"')
self.StopNow = False
self.SetTags("StopNow", 'view:"-" desc:"flag to stop running"')
self.vp = 0
self.SetTags("vp", 'view:"-" desc:"viewport"')
def __init__(self):
super(Sim, self).__init__()
self.Delay = Delays.Delay3
self.SetTags("Delay",
'desc:"which delay to use -- pres Init when changing"')
self.RecurrentWt = float(0.4)
self.SetTags(
"RecurrentWt",
'def:"0.4" step:"0.01" desc:"strength of recurrent weight in Hidden layer from each unit back to self"'
)
self.Net = leabra.Network()
self.SetTags(
"Net",
'view:"no-inline" desc:"the network -- click to view / edit parameters for layers, prjns, etc"'
)
self.Delay3Pats = etable.Table()
self.SetTags("Delay3Pats", 'view:"no-inline" desc:"delay 3 patterns"')
self.Delay5Pats = etable.Table()
self.SetTags("Delay5Pats", 'view:"no-inline" desc:"delay 5 patterns"')
self.Delay1Pats = etable.Table()
self.SetTags("Delay1Pats", 'view:"no-inline" desc:"delay 1 patterns"')
self.TrnTrlLog = etable.Table()
self.SetTags("TrnTrlLog",
'view:"no-inline" desc:"testing trial-level log data"')
self.Params = params.Sets()
self.SetTags("Params",
'view:"no-inline" desc:"full collection of param sets"')
self.ParamSet = str()
self.SetTags(
"ParamSet",
'view:"-" desc:"which set of *additional* parameters to use -- always applies Base and optionaly this next if set -- can use multiple names separated by spaces (don\'t put spaces in ParamSet names!)"'
)
self.MaxRuns = int(1)
self.SetTags("MaxRuns",
'desc:"maximum number of model runs to perform"')
self.MaxEpcs = int(1)
self.SetTags("MaxEpcs",
'desc:"maximum number of epochs to run per model run"')
self.TrainEnv = env.FixedTable()
self.SetTags(
"TrainEnv",
'desc:"Training environment -- contains everything about iterating over input / output patterns over training"'
)
self.Time = leabra.Time()
self.Time.CycPerQtr = 4 # key!
self.SetTags("Time", 'desc:"leabra timing parameters and state"')
self.ViewOn = True
self.SetTags(
"ViewOn",
'desc:"whether to update the network view while running"')
self.TrainUpdt = leabra.TimeScales.Quarter
self.SetTags(
"TrainUpdt",
'desc:"at what time scale to update the display during training? Anything longer than Epoch updates at Epoch in this model"'
)
self.TstRecLays = go.Slice_string(
["Location", "Cover", "Toy", "Hidden", "GazeExpect", "Reach"])
self.SetTags(
"TstRecLays",
'desc:"names of layers to record activations etc of during testing"'
)
# statistics: note use float64 as that is best for etable.Table
self.PrvGpName = str()
self.SetTags("PrvGpName", 'view:"-" desc:"previous group name"')
self.Win = 0
self.SetTags("Win", 'view:"-" desc:"main GUI window"')
self.NetView = 0
self.SetTags("NetView", 'view:"-" desc:"the network viewer"')
self.ToolBar = 0
self.SetTags("ToolBar", 'view:"-" desc:"the master toolbar"')
self.TrnTrlTable = 0
self.SetTags("TrnTrlTable",
'view:"-" desc:"the train trial table view"')
self.TrnTrlPlot = 0
self.SetTags("TrnTrlPlot", 'view:"-" desc:"the train trial plot"')
self.RunPlot = 0
self.SetTags("RunPlot", 'view:"-" desc:"the run plot"')
self.TrnEpcFile = 0
self.SetTags("TrnEpcFile", 'view:"-" desc:"log file"')
self.RunFile = 0
self.SetTags("RunFile", 'view:"-" desc:"log file"')
self.ValsTsrs = {}
self.SetTags("ValsTsrs", 'view:"-" desc:"for holding layer values"')
self.IsRunning = False
self.SetTags("IsRunning", 'view:"-" desc:"true if sim is running"')
self.StopNow = False
self.SetTags("StopNow", 'view:"-" desc:"flag to stop running"')
self.NeedsNewRun = False
self.SetTags(
"NeedsNewRun",
'view:"-" desc:"flag to initialize NewRun if last one finished"')
self.RndSeed = int(1)
self.SetTags("RndSeed", 'view:"-" desc:"the current random seed"')
self.vp = 0
self.SetTags("vp", 'view:"-" desc:"viewport"')
def __init__(self):
super(Sim, self).__init__()
self.AvgLGain = float(2.5)
self.SetTags("AvgLGain", 'min:"0.1" step:"0.5" def:"2.5" desc:"key BCM hebbian learning parameter, that determines how high the floating threshold goes -- higher = more homeostatic pressure against rich-get-richer feedback loops"')
self.InputNoise = float(0)
self.SetTags("InputNoise", 'min:"0" def:"0" desc:"variance on gaussian noise to add to inputs"')
self.TrainGi = float(1.8)
self.SetTags("TrainGi", 'min:"0" step:"0.1" def:"1.8" desc:"strength of inhibition during training with two lines present in input"')
self.TestGi = float(2.5)
self.SetTags("TestGi", 'min:"0" step:"0.1" def:"2.5" desc:"strength of inhibition during testing with one line present in input -- higher because fewer neurons should be active"')
self.Net = leabra.Network()
self.SetTags("Net", 'view:"no-inline" desc:"the network -- click to view / edit parameters for layers, prjns, etc"')
self.Lines2 = etable.Table()
self.SetTags("Lines2", 'view:"no-inline" desc:"easy training patterns -- can be learned with Hebbian"')
self.Lines1 = etable.Table()
self.SetTags("Lines1", 'view:"no-inline" desc:"hard training patterns -- require error-driven"')
self.TrnEpcLog = etable.Table()
self.SetTags("TrnEpcLog", 'view:"no-inline" desc:"training epoch-level log data"')
self.TstEpcLog = etable.Table()
self.SetTags("TstEpcLog", 'view:"no-inline" desc:"testing epoch-level log data"')
self.TstTrlLog = etable.Table()
self.SetTags("TstTrlLog", 'view:"no-inline" desc:"testing trial-level log data"')
self.HidFmInputWts = etensor.Float32()
self.SetTags("HidFmInputWts", 'view:"no-inline" desc:"weights from input to hidden layer"')
self.RunLog = etable.Table()
self.SetTags("RunLog", 'view:"no-inline" desc:"summary log of each run"')
self.RunStats = etable.Table()
self.SetTags("RunStats", 'view:"no-inline" desc:"aggregate stats on all runs"')
self.SimMat = simat.SimMat()
self.SetTags("SimMat", 'view:"no-inline" desc:"similarity matrix"')
self.Params = params.Sets()
self.SetTags("Params", 'view:"no-inline" desc:"full collection of param sets"')
self.ParamSet = str()
self.SetTags("ParamSet", 'view:"-" desc:"which set of *additional* parameters to use -- always applies Base and optionaly this next if set -- can use multiple names separated by spaces (don\'t put spaces in ParamSet names!)"')
self.MaxRuns = int(8)
self.SetTags("MaxRuns", 'desc:"maximum number of model runs to perform"')
self.MaxEpcs = int(30)
self.SetTags("MaxEpcs", 'desc:"maximum number of epochs to run per model run"')
self.TrainEnv = env.FixedTable()
self.SetTags("TrainEnv", 'desc:"Training environment -- contains everything about iterating over input / output patterns over training"')
self.TestEnv = env.FixedTable()
self.SetTags("TestEnv", 'desc:"Testing environment -- manages iterating over testing"')
self.Time = leabra.Time()
self.SetTags("Time", 'desc:"leabra timing parameters and state"')
self.ViewOn = True
self.SetTags("ViewOn", 'desc:"whether to update the network view while running"')
self.TrainUpdt = leabra.TimeScales.AlphaCycle
self.SetTags("TrainUpdt", 'desc:"at what time scale to update the display during training? Anything longer than Epoch updates at Epoch in this model"')
self.TestUpdt = leabra.TimeScales.Cycle
self.SetTags("TestUpdt", 'desc:"at what time scale to update the display during testing? Anything longer than Epoch updates at Epoch in this model"')
self.TestInterval = int(1)
self.SetTags("TestInterval", 'desc:"how often to run through all the test patterns, in terms of training epochs"')
self.TstRecLays = go.Slice_string(["Input", "Hidden"])
self.SetTags("TstRecLays", 'desc:"names of layers to record activations etc of during testing"')
self.UniqPats = float(0)
self.SetTags("UniqPats", 'inactive:"+" desc:"number of uniquely-coded line patterns, computed during testing -- maximum 10, higher is better"')
# internal state - view:"-"
self.Win = 0
self.SetTags("Win", 'view:"-" desc:"main GUI window"')
self.NetView = 0
self.SetTags("NetView", 'view:"-" desc:"the network viewer"')
self.ToolBar = 0
self.SetTags("ToolBar", 'view:"-" desc:"the master toolbar"')
self.WtsGrid = 0
self.SetTags("WtsGrid", 'view:"-" desc:"the weights grid view"')
self.TrnEpcPlot = 0
self.SetTags("TrnEpcPlot", 'view:"-" desc:"the training epoch plot"')
self.TstEpcPlot = 0
self.SetTags("TstEpcPlot", 'view:"-" desc:"the testing epoch plot"')
self.TstTrlPlot = 0
self.SetTags("TstTrlPlot", 'view:"-" desc:"the test-trial plot"')
self.RunPlot = 0
self.SetTags("RunPlot", 'view:"-" desc:"the run plot"')
self.TrnEpcFile = 0
self.SetTags("TrnEpcFile", 'view:"-" desc:"log file"')
self.RunFile = 0
self.SetTags("RunFile", 'view:"-" desc:"log file"')
self.ValsTsrs = {}
self.SetTags("ValsTsrs", 'view:"-" desc:"for holding layer values"')
self.IsRunning = False
self.SetTags("IsRunning", 'view:"-" desc:"true if sim is running"')
self.StopNow = False
self.SetTags("StopNow", 'view:"-" desc:"flag to stop running"')
self.NeedsNewRun = False
self.SetTags("NeedsNewRun", 'view:"-" desc:"flag to initialize NewRun if last one finished"')
self.RndSeed = int(1)
self.SetTags("RndSeed", 'view:"-" desc:"the current random seed"')
self.vp = 0
self.SetTags("vp", 'view:"-" desc:"viewport"')
def __init__(self):
super(Sim, self).__init__()
self.Spike = True
self.SetTags(
"Spike",
'desc:"use discrete spiking equations -- otherwise use Noisy X-over-X-plus-1 rate code activation function"'
)
self.GbarE = float(0.3)
self.SetTags(
"GbarE",
'min:"0" step:"0.01" def:"0.3" desc:"excitatory conductance multiplier -- determines overall value of Ge which drives neuron to be more excited -- pushes up over threshold to fire if strong enough"'
)
self.GbarL = float(0.3)
self.SetTags(
"GbarL",
'min:"0" step:"0.01" def:"0.3" desc:"leak conductance -- determines overall value of Gl which drives neuron to be less excited (inhibited) -- pushes back to resting membrane potential"'
)
self.ErevE = float(1)
self.SetTags(
"ErevE",
'min:"0" max:"1" step:"0.01" def:"1" desc:"excitatory reversal (driving) potential -- determines where excitation pushes Vm up to"'
)
self.ErevL = float(0.3)
self.SetTags(
"ErevL",
'min:"0" max:"1" step:"0.01" def:"0.3" desc:"leak reversal (driving) potential -- determines where excitation pulls Vm down to"'
)
self.Noise = float(0.0)
self.SetTags(
"Noise",
'min:"0" step:"0.01" desc:"the variance parameter for Gaussian noise added to unit activations on every cycle"'
)
self.KNaAdapt = True
self.SetTags(
"KNaAdapt",
'desc:"apply sodium-gated potassium adaptation mechanisms that cause the neuron to reduce spiking over time"'
)
self.NCycles = int(200)
self.SetTags(
"NCycles",
'min:"10" def:"200" desc:"total number of cycles to run"')
self.OnCycle = int(10)
self.SetTags(
"OnCycle",
'min:"0" def:"10" desc:"when does excitatory input into neuron come on?"'
)
self.OffCycle = int(160)
self.SetTags(
"OffCycle",
'min:"0" def:"160" desc:"when does excitatory input into neuron go off?"'
)
self.UpdtInterval = int(10)
self.SetTags(
"UpdtInterval",
'min:"1" def:"10" desc:"how often to update display (in cycles)"')
self.Net = leabra.Network()
self.SetTags(
"Net",
'view:"no-inline" desc:"the network -- click to view / edit parameters for layers, prjns, etc"'
)
self.SpikeParams = spike.ActParams()
self.SetTags("SpikeParams",
'view:"no-inline" desc:"parameters for spiking funcion"')
self.TstCycLog = etable.Table()
self.SetTags(
"TstCycLog",
'view:"no-inline" desc:"testing trial-level log data -- click to see record of network\'s response to each input"'
)
self.SpikeVsRateLog = etable.Table()
self.SetTags(
"SpikeVsRateLog",
'view:"no-inline" desc:"plot of measured spike rate vs. noisy X/X+1 rate function"'
)
self.Params = params.Sets()
self.SetTags(
"Params",
'view:"no-inline" desc:"full collection of param sets -- not really interesting for this model"'
)
self.ParamSet = str()
self.SetTags(
"ParamSet",
'view:"-" desc:"which set of *additional* parameters to use -- always applies Base and optionaly this next if set -- can use multiple names separated by spaces (don\'t put spaces in ParamSet names!)"'
)
self.Cycle = int()
self.SetTags("Cycle", 'inactive:"+" desc:"current cycle of updating"')
# internal state - view:"-"
self.Win = 0
self.SetTags("Win", 'view:"-" desc:"main GUI window"')
self.NetView = 0
self.SetTags("NetView", 'view:"-" desc:"the network viewer"')
self.ToolBar = 0
self.SetTags("ToolBar", 'view:"-" desc:"the master toolbar"')
self.TstCycPlot = 0
self.SetTags("TstCycPlot", 'view:"-" desc:"the test-trial plot"')
self.SpikeVsRatePlot = 0
self.SetTags("SpikeVsRatePlot",
'view:"-" desc:"the spike vs. rate plot"')
self.IsRunning = False
self.SetTags("IsRunning", 'view:"-" desc:"true if sim is running"')
self.StopNow = False
self.SetTags("StopNow", 'view:"-" desc:"flag to stop running"')
self.vp = 0
self.SetTags("vp", 'view:"-" desc:"viewport"')
