class SpikeSourcePoisson(cells.SpikeSourcePoisson):
"""Spike source, generating spikes according to a Poisson process."""
translations = common.build_translations(
('rate', 'rate'),
('start', 'start'),
('duration', 'duration'),
)
class rates(object):
"""
Acts as a function of time for the PoissonGroup, while storing the
parameters for later retrieval.
"""
def __init__(self, start, duration, rate):
self.start = start * ms
self.duration = duration * ms
self.rate = rate * Hz
def __call__(self, t):
#print t, self.start, self.duration, self.rate
return (self.start <= t <=
self.start + self.duration) and self.rate or 0.0 * Hz
def __init__(self, parameters):
cells.SpikeSourcePoisson.__init__(self, parameters)
start = self.parameters['start']
duration = self.parameters['duration']
rate = self.parameters['rate']
self.fct = SpikeSourcePoisson.rates(start, duration, rate)
class AdditivePotentiationMultiplicativeDepression(synapses.AdditivePotentiationMultiplicativeDepression):
"""
The amplitude of the weight change depends on the current weight for
depression (Dw propto w-w_min) and is fixed for potentiation.
"""
translations = common.build_translations(
('w_max', 'Wex', 1e-9), # unit conversion
('w_min', 'w_min_always_zero_in_PCSIM'),
('A_plus', 'Apos', 1e-9), # Apos has the same units as the weight
('A_minus', 'Aneg', -1), # Aneg is dimensionless
)
possible_models = stdp_synapse_models
scales_with_weight = ['Wex', 'Apos']
def __init__(self, w_min=0.0, w_max=1.0, A_plus=0.01, A_minus=0.01): # units?
if w_min != 0:
raise Exception("Non-zero minimum weight is not supported by PCSIM.")
#synapses.AdditivePotentiationMultiplicativeDepression.__init__(self, w_min, w_max, A_plus, A_minus)
parameters = dict(locals())
parameters.pop('self')
self.parameters = self.translate(parameters)
self.parameters['useFroemkeDanSTDP'] = False
self.parameters['mupos'] = 0.0
self.parameters['muneg'] = 1.0
self.parameters.pop('w_min_always_zero_in_PCSIM')
class AdditiveWeightDependence(synapses.AdditiveWeightDependence):
"""
The amplitude of the weight change is fixed for depression (`A_minus`)
and for potentiation (`A_plus`).
If the new weight would be less than `w_min` it is set to `w_min`. If it would
be greater than `w_max` it is set to `w_max`.
"""
translations = common.build_translations(
('w_max', 'Wmax', 1000.0), # unit conversion
('w_min', 'w_min_always_zero_in_NEST'),
('A_plus', 'lambda'),
('A_minus', 'alpha', 'A_minus/A_plus', 'alpha*lambda'),
)
possible_models = set(['stdp_synapse']) #,'stdp_synapse_hom'])
def __init__(self,
w_min=0.0,
w_max=1.0,
A_plus=0.01,
A_minus=0.01): # units?
if w_min != 0:
raise Exception(
"Non-zero minimum weight is not supported by NEST.")
#synapses.AdditiveWeightDependence.__init__(self, w_min, w_max, A_plus, A_minus)
parameters = dict(locals())
parameters.pop('self')
self.parameters = self.translate(parameters)
self.parameters['mu_plus'] = 0.0
self.parameters['mu_minus'] = 0.0
class IF_cond_alpha(cells.IF_cond_alpha):
translations = common.build_translations(
('v_rest', 'v_rest', mV),
('v_reset', 'v_reset'),
('cm', 'c_m', nF),
('tau_m', 'tau_m', mV),
('tau_refrac', 'tau_refrac'),
('tau_syn_E', 'tau_syn_E', ms),
('tau_syn_I', 'tau_syn_I', ms),
('v_thresh', 'v_thresh'),
('i_offset', 'i_offset', nA),
('e_rev_E', 'e_rev_E', mV),
('e_rev_I', 'e_rev_I', mV),
('v_init', 'v_init', mV),
)
eqs = brian.Equations('''
dv/dt = (v_rest-v)/tau_m + (ge*(e_rev_E-v) + gi*(e_rev_I-v) + i_offset + i_inj)/c_m : mV
dge/dt = (2.7182818284590451*ye-ge)/tau_syn_E : uS
dye/dt = -ye/tau_syn_E : uS
dgi/dt = (2.7182818284590451*yi-gi)/tau_syn_I : uS
dyi/dt = -yi/tau_syn_I : uS
tau_syn_E : ms
tau_syn_I : ms
tau_m : ms
v_rest : mV
e_rev_E : mV
e_rev_I : mV
c_m : nF
i_offset : nA
i_inj : nA
''')
synapses = {'excitatory': 'ye', 'inhibitory': 'yi'}
class GutigWeightDependence(synapses.GutigWeightDependence):
"""
The amplitude of the weight change depends on the current weight.
For depression, Dw propto w-w_min
For potentiation, Dw propto w_max-w
"""
translations = common.build_translations(
('w_max', 'Wex', 1e-9), # unit conversion
('w_min', 'w_min_always_zero_in_PCSIM'),
('A_plus', 'Apos'),
('A_minus', 'Aneg', -1),
('mu_plus', 'mupos'),
('mu_minus', 'muneg')
)
possible_models = stdp_synapse_models
def __init__(self, w_min=0.0, w_max=1.0, A_plus=0.01, A_minus=0.01, mu_plus=0.5, mu_minus=0.5): # units?
if w_min != 0:
raise Exception("Non-zero minimum weight is not supported by PCSIM.")
#synapses.AdditivePotentiationMultiplicativeDepression.__init__(self, w_min, w_max, A_plus, A_minus)
parameters = dict(locals())
parameters.pop('self')
self.parameters = self.translate(parameters)
self.parameters['useFroemkeDanSTDP'] = False
self.parameters.pop('w_min_always_zero_in_PCSIM')
class IF_cond_exp(cells.IF_cond_exp):
"""Leaky integrate and fire model with fixed threshold and
exponentially-decaying post-synaptic conductance."""
translations = common.build_translations(
('v_rest', 'v_rest', mV),
('v_reset', 'v_reset'),
('cm', 'cm', nF),
('tau_m', 'tau_m', ms),
('tau_refrac', 'tau_refrac'),
('tau_syn_E', 'tau_syn_E', ms),
('tau_syn_I', 'tau_syn_I', ms),
('v_thresh', 'v_thresh'),
('i_offset', 'i_offset', nA),
('e_rev_E', 'e_rev_E', mV),
('e_rev_I', 'e_rev_I', mV),
('v_init', 'v_init', mV),
)
eqs = brian.Equations('''
dv/dt = (v_rest-v)/tau_m + (ge*(e_rev_E-v) + gi*(e_rev_I-v) + i_offset + i_inj)/cm : mV
dge/dt = -ge/tau_syn_E : uS
dgi/dt = -gi/tau_syn_I : uS
tau_syn_E : ms
tau_syn_I : ms
tau_m : ms
cm : nF
v_rest : mV
e_rev_E : mV
e_rev_I : mV
i_offset : nA
i_inj : nA
''')
synapses = {'excitatory': 'ge', 'inhibitory': 'gi'}
class GutigWeightDependence(synapses.GutigWeightDependence):
"""
The amplitude of the weight change depends on the current weight.
For depression, Dw propto w-w_min
For potentiation, Dw propto w_max-w
"""
translations = common.build_translations(
('w_max', 'Wmax', 1000.0), # unit conversion
('w_min', 'w_min_always_zero_in_NEST'),
('A_plus', 'lambda'),
('A_minus', 'alpha', 'A_minus/A_plus', 'alpha*lambda'),
('mu_plus', 'mu_plus'),
('mu_minus', 'mu_minus'),
)
possible_models = set(['stdp_synapse']) #,'stdp_synapse_hom'])
def __init__(self,
w_min=0.0,
w_max=1.0,
A_plus=0.01,
A_minus=0.01,
mu_plus=0.5,
mu_minus=0.5):
if w_min != 0:
raise Exception(
"Non-zero minimum weight is not supported by NEST.")
#synapses.GutigWeightDependence.__init__(self, w_min, w_max, A_plus, A_minus)
parameters = dict(locals())
parameters.pop('self')
self.parameters = self.translate(parameters)
class IF_cond_exp_gsfa_grr(cells.IF_cond_exp_gsfa_grr):
"""
Linear leaky integrate and fire model with fixed threshold,
decaying-exponential post-synaptic conductance, conductance based spike-frequency adaptation,
and a conductance-based relative refractory mechanism.
See: Muller et al (2007) Spike-frequency adapting neural ensembles: Beyond mean-adaptation
and renewal theories. Neural Computation 19: 2958-3010.
See also: EIF_cond_alpha_isfa_ista
"""
translations = common.build_translations(
('v_rest', 'E_L'),
('v_reset', 'V_reset'),
('cm', 'C_m', 1000.0), # C_m is in pF, cm in nF
('tau_m', 'g_L', "cm/tau_m*1000.0", "C_m/g_L"),
('tau_refrac', 't_ref', "max(get_time_step(), tau_refrac)", "t_ref"),
('tau_syn_E', 'tau_syn_ex'),
('tau_syn_I', 'tau_syn_in'),
('v_thresh', 'V_th'),
('i_offset', 'I_e', 1000.0), # I_e is in pA, i_offset in nA
('e_rev_E', 'E_ex'),
('e_rev_I', 'E_in'),
('v_init', 'v_init'),
('tau_sfa', 'tau_sfa'),
('e_rev_sfa', 'E_sfa'),
('q_sfa', 'q_sfa'),
('tau_rr', 'tau_rr'),
('e_rev_rr', 'E_rr'),
('q_rr', 'q_rr'))
nest_name = "iaf_cond_exp_sfa_rr"
class TsodyksMarkramMechanism(synapses.TsodyksMarkramMechanism):
translations = common.build_translations(
('U', 'U'),
('tau_rec', 'tau_rec'),
('tau_facil', 'tau_facil'),
('u0', 'u0'),
('x0', 'x'), # } note that these two values
('y0', 'y') # } are not used
)
native_name = 'tsodkys-markram'
def __init__(self,
U=0.5,
tau_rec=100.0,
tau_facil=0.0,
u0=0.0,
x0=1.0,
y0=0.0):
assert (x0 == 1
and y0 == 0), "It is not currently possible to set x0 and y0"
#synapses.TsodyksMarkramMechanism.__init__(self, U, tau_rec, tau_facil, u0, x0, y0)
self.parameters = self.translate({
'U': U,
'tau_rec': tau_rec,
'tau_facil': tau_facil,
'u0': u0,
'x0': x0,
'y0': y0
})
class TsodyksMarkramMechanism(synapses.TsodyksMarkramMechanism):
translations = common.build_translations(
('U', 'U'),
('tau_rec', 'tau_rec'),
('tau_facil', 'tau_fac'),
('u0', 'u'), # this could cause problems for reverse translation
('x0', 'x'), # (as for V_m) in cell models, since the initial value
('y0', 'y') # is not stored, only set.
)
native_name = 'tsodyks_synapse'
def __init__(self,
U=0.5,
tau_rec=100.0,
tau_facil=0.0,
u0=0.0,
x0=1.0,
y0=0.0):
#synapses.TsodyksMarkramMechanism.__init__(self, U, tau_rec, tau_facil, u0, x0, y0)
parameters = dict(
locals()) # need the dict to get a copy of locals. When running
parameters.pop(
'self'
) # through coverage.py, for some reason, the pop() doesn't have any effect
self.parameters = self.translate(parameters)
class EIF_cond_alpha_isfa_ista(cells.EIF_cond_alpha_isfa_ista):
"""
Exponential integrate and fire neuron with spike triggered and sub-threshold
adaptation currents (isfa, ista reps.) according to:
Brette R and Gerstner W (2005) Adaptive Exponential Integrate-and-Fire Model as
an Effective Description of Neuronal Activity. J Neurophysiol 94:3637-3642
See also: IF_cond_exp_gsfa_grr
"""
translations = common.build_translations(
('v_init', 'v_init'),
('w_init', 'w', 1000.0), # nA -> pA
('cm', 'C_m', 1000.0), # nF -> pF
('tau_refrac', 't_ref'),
('v_spike', 'V_peak'),
('v_reset', 'V_reset'),
('v_rest', 'E_L'),
('tau_m', 'g_L', "cm/tau_m*1000.0", "C_m/g_L"),
('i_offset', 'I_e', 1000.0), # nA -> pA
('a', 'a'),
('b', 'b', 1000.0), # nA -> pA.
('delta_T', 'Delta_T'),
('tau_w', 'tau_w'),
('v_thresh', 'V_th'),
('e_rev_E', 'E_ex'),
('tau_syn_E', 'tau_syn_ex'),
('e_rev_I', 'E_in'),
('tau_syn_I', 'tau_syn_in'),
)
nest_name = "aeif_cond_alpha"
class IF_facets_hardware1(cells.IF_facets_hardware1):
"""
Leaky integrate and fire model with fixed threshold and conductance-based synpases,
describes the neuron model of the Spikey neuromorphic system.
"""
translations = common.build_translations(
('v_reset', 'v_reset'),
('v_rest', 'v_rest'),
('v_thresh', 'v_thresh'),
('e_rev_I', 'e_rev_I'),
('g_leak', 'g_leak'),
('tau_refrac', 'tau_refrac')
)
estimator_cm = 0.2
estimator_e_rev_E = 0.0
estimator_tau_syn_E = 5.0
estimator_tau_syn_I = 5.0
def __init__(self, parameters):
# extend with Spikey specific neuron parameters
cells.IF_facets_hardware1.__init__(self, parameters)
self.parameters['lowlevel_parameters'] = {}
self.parameters['estimator_cm'] = self.estimator_cm
self.parameters['estimator_e_rev_E'] = self.estimator_e_rev_E
self.parameters['estimator_tau_syn_E'] = self.estimator_tau_syn_E
self.parameters['estimator_tau_syn_I'] = self.estimator_tau_syn_I
class AdditiveWeightDependence(synapses.AdditiveWeightDependence):
"""
The amplitude of the weight change is fixed for depression (`A_minus`)
and for potentiation (`A_plus`).
If the new weight would be less than `w_min` it is set to `w_min`. If it would
be greater than `w_max` it is set to `w_max`.
"""
translations = common.build_translations(
('w_max', 'wmax'),
('w_min', 'wmin'),
('A_plus', 'aLTP'),
('A_minus', 'aLTD'),
)
possible_models = set([
'StdwaSA',
])
def __init__(self,
w_min=0.0,
w_max=1.0,
A_plus=0.01,
A_minus=0.01): # units?
#synapses.AdditiveWeightDependence.__init__(self, w_min, w_max, A_plus, A_minus)
self.parameters = self.translate({
'w_min': w_min,
'w_max': w_max,
'A_plus': A_plus,
'A_minus': A_minus
})
class IF_curr_alpha(cells.IF_curr_alpha):
"""Leaky integrate and fire model with fixed threshold and alpha-function-
shaped post-synaptic current."""
translations = common.build_translations(
('v_rest', 'v_rest', mV),
('v_reset', 'v_reset'),
('cm', 'c_m', nF),
('tau_m', 'tau_m', ms),
('tau_refrac', 'tau_refrac'),
('tau_syn_E', 'tau_syn_E', ms),
('tau_syn_I', 'tau_syn_I', ms),
('v_thresh', 'v_thresh'),
('i_offset', 'i_offset', nA),
('v_init', 'v_init', ms),
)
eqs = brian.Equations('''
dv/dt = (ge + gi + i_offset + i_inj)/c_m + (v_rest-v)/tau_m : mV
dge/dt = (2.7182818284590451*ye-ge)/tau_syn_E : nA
dye/dt = -ye/tau_syn_E : nA
dgi/dt = (2.7182818284590451*yi-gi)/tau_syn_I : nA
dyi/dt = -yi/tau_syn_I : nA
c_m : nF
tau_syn_E : ms
tau_syn_I : ms
tau_m : ms
v_rest : mV
i_offset : nA
i_inj : nA
''')
synapses = {'excitatory': 'ye', 'inhibitory': 'yi'}
class SpikeSourceArray(cells.SpikeSourceArray):
"""Spike source generating spikes at the times given in the spike_times array."""
translations = common.build_translations(
('spike_times', 'spike_times'),
)
model = VectorSpikeSource
class IF_facets_hardware1(cells.IF_facets_hardware1):
"""Leaky integrate and fire model with conductance-based synapses and fixed
threshold as it is resembled by the FACETS Hardware Stage 1. For further
details regarding the hardware model see the FACETS-internal Wiki:
https://facets.kip.uni-heidelberg.de/private/wiki/index.php/WP7_NNM
"""
translations = common.build_translations(
('v_rest', 'v_rest'),
('v_thresh', 'v_thresh'),
('v_reset', 'v_reset'),
('g_leak', 'tau_m', "0.2*1000.0/g_leak", "0.2*1000.0/tau_m"),
('tau_syn_E', 'tau_e'),
('tau_syn_I', 'tau_i'),
('e_rev_I', 'e_i')
)
model = StandardIF
def __init__(self, parameters):
cells.IF_facets_hardware1.__init__(self, parameters)
self.parameters['syn_type'] = 'conductance'
self.parameters['syn_shape'] = 'exp'
self.parameters['i_offset'] = 0.0
self.parameters['c_m'] = 0.2
self.parameters['t_refrac'] = 1.0
self.parameters['e_e'] = 0.0
class AdditiveWeightDependence(synapses.AdditiveWeightDependence):
"""
The amplitude of the weight change is fixed for depression (`A_minus`)
and for potentiation (`A_plus`).
If the new weight would be less than `w_min` it is set to `w_min`. If it would
be greater than `w_max` it is set to `w_max`.
"""
translations = common.build_translations(
('w_max', 'Wex', 1e-9), # unit conversion. This exposes a limitation of the current
# translation machinery, because this value depends on the
# type of the post-synaptic cell. We currently work around
# this using the "scales_with_weight" attribute, although
# this breaks reverse translation.
('w_min', 'w_min_always_zero_in_PCSIM'),
('A_plus', 'Apos', '1e-9*A_plus*w_max', '1e9*Apos/w_max'), # note that here Apos and Aneg
('A_minus', 'Aneg', '-1e-9*A_minus*w_max', '-1e9*Aneg/w_max'), # have the same units as the weight
)
possible_models = stdp_synapse_models
scales_with_weight = ['Wex', 'Apos', 'Aneg']
def __init__(self, w_min=0.0, w_max=1.0, A_plus=0.01, A_minus=0.01): # units?
if w_min != 0:
raise Exception("Non-zero minimum weight is not supported by PCSIM.")
#synapses.AdditiveWeightDependence.__init__(self, w_min, w_max, A_plus, A_minus)
parameters = dict(locals())
parameters.pop('self')
self.parameters = self.translate(parameters)
self.parameters['useFroemkeDanSTDP'] = False
self.parameters['mupos'] = 0.0
self.parameters['muneg'] = 0.0
self.parameters.pop('w_min_always_zero_in_PCSIM')
class HH_cond_exp(cells.HH_cond_exp):
translations = common.build_translations(
('gbar_Na', 'gbar_Na', 1e-6),
('gbar_K', 'gbar_K', 1e-6),
('g_leak', 'g_leak', 1e-6),
('cm', 'c_m'),
('v_offset', 'v_offset'),
('e_rev_Na', 'ena'),
('e_rev_K', 'ek'),
('e_rev_leak', 'e_leak'),
('e_rev_E', 'e_e'),
('e_rev_I', 'e_i'),
('tau_syn_E', 'tau_e'),
('tau_syn_I', 'tau_i'),
('i_offset', 'i_offset'),
('v_init', 'v_init'),
)
model = SingleCompartmentTraub
def __init__(self, parameters):
cells.HH_cond_exp.__init__(self, parameters) # checks supplied parameters and adds default
# values for not-specified parameters.
self.parameters['syn_type'] = 'conductance'
self.parameters['syn_shape'] = 'exp'
class IF_cond_exp(cells.IF_cond_exp):
"""Leaky integrate and fire model with fixed threshold and
exponentially-decaying post-synaptic conductance."""
translations = common.build_translations(
('tau_m', 'tau_m'),
('cm', 'c_m'),
('v_rest', 'v_rest'),
('v_thresh', 'v_thresh'),
('v_reset', 'v_reset'),
('tau_refrac', 't_refrac'),
('i_offset', 'i_offset'),
('tau_syn_E', 'tau_e'),
('tau_syn_I', 'tau_i'),
('v_init', 'v_init'),
('e_rev_E', 'e_e'),
('e_rev_I', 'e_i')
)
model = StandardIF
def __init__(self, parameters):
cells.IF_cond_exp.__init__(self, parameters) # checks supplied parameters and adds default
# values for not-specified parameters.
self.parameters['syn_type'] = 'conductance'
self.parameters['syn_shape'] = 'exp'
class IF_cond_exp(cells.IF_cond_exp):
"""Leaky integrate and fire model with fixed threshold and
exponentially-decaying post-synaptic conductance."""
translations = common.build_translations(
('tau_m', 'taum', 1e-3),
('cm', 'Cm', 1e-9),
('v_rest', 'Vresting', 1e-3),
('v_thresh', 'Vthresh', 1e-3),
('v_reset', 'Vreset', 1e-3),
('tau_refrac', 'Trefract', 1e-3),
('i_offset', 'Iinject', 1e-9),
('tau_syn_E', 'TauSynExc', 1e-3),
('tau_syn_I', 'TauSynInh', 1e-3),
('e_rev_E', 'ErevExc', 1e-3),
('e_rev_I', 'ErevInh', 1e-3),
('v_init', 'Vinit', 1e-3),
)
pcsim_name = "LIFCondExpNeuron"
simObjFactory = None
setterMethods = {}
recordable = ['spikes', 'v']
def __init__(self, parameters):
cells.IF_cond_exp.__init__(self, parameters)
self.parameters['Inoise'] = 0.0
self.simObjFactory = pypcsim.LIFCondExpNeuron(**self.parameters)
class IF_curr_exp(cells.IF_curr_exp):
"""Leaky integrate and fire model with fixed threshold and
decaying-exponential post-synaptic current. (Separate synaptic currents for
excitatory and inhibitory synapses."""
translations = common.build_translations(
('v_rest', 'v_rest', mV),
('v_reset', 'v_reset'),
('cm', 'c_m', nF),
('tau_m', 'tau_m', ms),
('tau_refrac', 'tau_refrac'),
('tau_syn_E', 'tau_syn_E', ms),
('tau_syn_I', 'tau_syn_I', ms),
('v_thresh', 'v_thresh'),
('i_offset', 'i_offset', nA),
('v_init', 'v_init', mV),
)
eqs = brian.Equations('''
dv/dt = (ie + ii + i_offset + i_inj)/c_m + (v_rest-v)/tau_m : mV
die/dt = -ie/tau_syn_E : nA
dii/dt = -ii/tau_syn_I : nA
tau_syn_E : ms
tau_syn_I : ms
tau_m : ms
c_m : nF
v_rest : mV
i_offset : nA
i_inj : nA
''')
synapses = {'excitatory': 'ie', 'inhibitory': 'ii'}
class SpikeSourcePoisson(cells.SpikeSourcePoisson):
"""Spike source, generating spikes according to a Poisson process."""
translations = common.build_translations(
('start', 'start'),
('rate', '_interval', "1000.0/rate", "1000.0/_interval"),
('duration', 'duration'),
)
model = RandomSpikeSource
class SpikeSourceArray(cells.SpikeSourceArray):
'''Spike source generating spikes at the times given in the spike_times array.'''
translations = common.build_translations(
('spike_times', 'spike_times')
)
def __init__(self, parameters):
cells.SpikeSourceArray.__init__(self, parameters)
self.index = None
class SpikeSourcePoisson(cells.SpikeSourcePoisson):
"""Spike source, generating spikes according to a Poisson process."""
translations = common.build_translations(('start', 'Tstart', 1e-3),
('rate', 'rate'),
('duration', 'duration', 1e-3))
pcsim_name = 'PoissonInputNeuron'
simObjFactory = None
setterMethods = {}
def __init__(self, parameters):
cells.SpikeSourcePoisson.__init__(self, parameters)
self.simObjFactory = pypcsim.PoissonInputNeuron(**self.parameters)
class SpikeSourcePoisson(cells.SpikeSourcePoisson):
'''Spike source, generating spikes according to a Poisson process.'''
translations = common.build_translations(
('rate', 'rate'),
('start', 'start'),
('duration', 'duration')
)
def __init__(self, parameters):
cells.SpikeSourcePoisson.__init__(self, parameters)
self.index = None
class SpikePairRule(synapses.SpikePairRule):
translations = common.build_translations(
('tau_plus', 'tau_plus'),
('tau_minus', 'tau_minus'), # defined in post-synaptic neuron
)
possible_models = set(['stdp_synapse']) #,'stdp_synapse_hom'])
def __init__(self, tau_plus=20.0, tau_minus=20.0):
#synapses.SpikePairRule.__init__(self, tau_plus, tau_minus)
parameters = dict(locals())
parameters.pop('self')
self.parameters = self.translate(parameters)
class SpikeSourcePoisson(cells.SpikeSourcePoisson):
"""Spike source, generating spikes according to a Poisson process."""
translations = common.build_translations(
('rate', 'rate'),
('start', 'start'),
('duration', 'stop', "start+duration", "stop-start"),
)
nest_name = 'poisson_generator'
always_local = True
def __init__(self, parameters):
cells.SpikeSourcePoisson.__init__(self, parameters)
self.parameters['origin'] = 1.0
class SpikePairRule(synapses.SpikePairRule):
translations = common.build_translations(
('tau_plus', 'tauLTP'),
('tau_minus', 'tauLTD'),
)
possible_models = set(['StdwaSA', 'StdwaSoft', 'StdwaGuetig'])
def __init__(self, tau_plus=20.0, tau_minus=20.0):
#synapses.SpikePairRule.__init__(self, tau_plus, tau_minus)
self.parameters = self.translate({
'tau_plus': tau_plus,
'tau_minus': tau_minus
})
class SpikePairRule(synapses.SpikePairRule):
translations = common.build_translations(
('tau_plus', 'taupos', 1e-3),
('tau_minus', 'tauneg', 1e-3),
)
possible_models = stdp_synapse_models
def __init__(self, tau_plus=20.0, tau_minus=20.0):
#synapses.SpikePairRule.__init__(self, tau_plus, tau_minus)
parameters = dict(locals())
parameters.pop('self')
self.parameters = self.translate(parameters)
self.parameters['STDPgap'] = 0.0
class SpikeSourceArray(cells.SpikeSourceArray):
"""Spike source generating spikes at the times given in the spike_times array."""
translations = common.build_translations(
('spike_times', 'spiketimes', ms), )
@classmethod
def translate(cls, parameters):
if 'spike_times' in parameters:
try:
parameters['spike_times'] = numpy.array(
parameters['spike_times'], float)
except ValueError:
raise common.InvalidParameterValueError(
"spike times must be floats")
return super(SpikeSourceArray, cls).translate(parameters)
