def GCALTemporal(stimulus = None,
timestep=0.5,
ringParams = ('MAX','MAX'),
saveSnapshotTime = None,
plastic=True,
LGN_TC = None,
LGNFFStrength = 2.33,
GCStrength=0.6,
GCDelay=0.01,
LGNDistanceDelay=None,
# V1 Settings
V1_TC = None,
V1FFStrength=1.5,
ExcStrength=1.7,
InhStrength=-1.4,
LateralDelay=0.01,
V1DistanceDelay=None,
HomeoFactor=1.0,
HomeoPlastic = False ):
VSDSheetFlag = True; saveSnapshotFlag = False; LOAD = None
###################################################
# Modifying sheets #
####################
modelName = 'fit-LGN-V1.py'
(LGNRingNo,V1RingNo) = ringParams
if LOAD is not None:
typPath = '../models/snapshots/'
load_snapshot(typPath+LOAD)
else:
print "Modifying model loaded in memory"
########################
# Sheets Modifications #
########################
p = gcal_vanilla.makeParams()
gcal_vanilla.makeSheets(p)
# Switch out the LISSOM sheets for continuous ones
switchAllSheets(VSDSheetFlag)
# Setup the input generator
setupRetina(stimulus, timestep)
# Connecting with original GCAL connections
gcal_vanilla.connectGCAL(p,LGNRingNo,V1RingNo)
############################
# Connection Modifications #
############################
'Alternatively, could use topo.sheet[<name>].projection(<name>)'
conns = dict([(el.name,el) for el in topo.sim.connections()])
''' AfferentToLGNOn, AfferentToLGNOff, LGNOnAfferent, LGNOffAfferent,
GCLGNOn-*, GCLGNOff-*, LateralInhibitory-*, LateralExcitatory-*
'''
#######
# LGN #
#######
# FEEDFORWARD DELAY IS ABOUT 15MS FOR BOTH STAGES
# Afferent from Retina to LGNOn/Off. Delay and strength.
setConnDelay('AfferentToLGNOn',15.0,conns)
setConnDelay('AfferentToLGNOff',15.0,conns)
setConnStrength('AfferentToLGNOn', LGNFFStrength,conns)
setConnStrength('AfferentToLGNOff', LGNFFStrength,conns)
assert GCStrength > 0
modifyLateralConnections(conns, 'GCLGNOn', GCStrength, GCDelay, LGNDistanceDelay)
modifyLateralConnections(conns, 'GCLGNOff', GCStrength, GCDelay, LGNDistanceDelay)
######
# V1 #
######
setConnDelay('LGNOnAfferent',15.0,conns)
setConnDelay('LGNOffAfferent',15.0,conns)
setConnStrength('LGNOnAfferent', V1FFStrength,conns)
setConnStrength('LGNOffAfferent' ,V1FFStrength,conns)
assert ExcStrength > 0; assert InhStrength < 0
modifyLateralConnections(conns, 'LateralExcitatory', ExcStrength, LateralDelay, V1DistanceDelay)
modifyLateralConnections(conns, 'LateralInhibitory', InhStrength, LateralDelay, V1DistanceDelay)
##########################
# Sheet output functions #
##########################
setLGNOutputFns(LGN_TC)
setV1OutputFns(V1_TC, HomeoFactor, HomeoPlastic)
###############################
# Plasticity and snapshotting #
###############################
if saveSnapshotFlag:
setSheetPlasticity(plastic=True) # No point making snapshot if plasticity if off
saveSnapShot(saveSnapshotTime)
setSheetPlasticity(plastic) # Set as desired
V1outputList = []
if V1_TC is not None:
V1outputList += [Hysteresis(time_constant=V1_TC)]
# False for off and None for default
if HomeoFactor is not False:
learning_rate=0.001 * HomeoFactor
smoothing=0.999 # Thanks to Jan for this conversion.
smoothing = 1 - (1 - smoothing) / HomeoFactor
homeostaticFn = HomeostaticResponse(learning_rate=learning_rate)
homeostaticFn.plastic = HomeoPlastic
V1outputList += [homeostaticFn]
else:
print "\n*Warning*: Using half rectification instead of hysteresis!\n"
V1outputList += [HalfRectify()] # If homeostatis is off, this non-linearity to be used instead
OutputFns(V1outputList,['V1'])
# Calibrate with homeostasis OFF - ensure balance err towards inhibition
# Check that learning is off properly
if __name__ == '__main__':
# Should reproduce GCAL with delays and continuous sheets.
# Note that tsettle has been removed.
p = gcal_vanilla.makeParams()
stimulus = gcal_vanilla.GCALStimulusPattern(p,'Gaussian')
GCALTemporal(stimulus) # Using default (GCAL) parameters
