/usr/share/pyshared/pyNN/neuron/cells.py is in python-pynn 0.7.4-1.
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"""
Standard cells for the neuron module.
:copyright: Copyright 2006-2011 by the PyNN team, see AUTHORS.
:license: CeCILL, see LICENSE for details.
$Id: cells.py 957 2011-05-03 13:44:15Z apdavison $
"""
from pyNN.standardmodels import cells, build_translations
from pyNN.models import BaseCellType
from pyNN import errors
from neuron import h, nrn, hclass
from simulator import state
from math import pi
import logging
logger = logging.getLogger("PyNN")
def _new_property(obj_hierarchy, attr_name):
"""
Returns a new property, mapping attr_name to obj_hierarchy.attr_name.
For example, suppose that an object of class A has an attribute b which
itself has an attribute c which itself has an attribute d. Then placing
e = _new_property('b.c', 'd')
in the class definition of A makes A.e an alias for A.b.c.d
"""
def set(self, value):
obj = reduce(getattr, [self] + obj_hierarchy.split('.'))
setattr(obj, attr_name, value)
def get(self):
obj = reduce(getattr, [self] + obj_hierarchy.split('.'))
return getattr(obj, attr_name)
return property(fset=set, fget=get)
class NativeCellType(BaseCellType):
pass
class SingleCompartmentNeuron(nrn.Section):
"""docstring"""
synapse_models = {
'current': { 'exp': h.ExpISyn, 'alpha': h.AlphaISyn },
'conductance' : { 'exp': h.ExpSyn, 'alpha': h.AlphaSyn },
}
def __init__(self, syn_type, syn_shape, c_m, i_offset,
tau_e, tau_i, e_e, e_i):
# initialise Section object with 'pas' mechanism
nrn.Section.__init__(self)
self.seg = self(0.5)
self.L = 100
self.seg.diam = 1000/pi # gives area = 1e-3 cm2
self.source_section = self
self.syn_type = syn_type
self.syn_shape = syn_shape
# insert synapses
assert syn_type in ('current', 'conductance'), "syn_type must be either 'current' or 'conductance'. Actual value is %s" % syn_type
assert syn_shape in ('alpha', 'exp'), "syn_type must be either 'alpha' or 'exp'"
synapse_model = StandardIF.synapse_models[syn_type][syn_shape]
self.esyn = synapse_model(0.5, sec=self)
self.isyn = synapse_model(0.5, sec=self)
if self.syn_shape == 'exp':
if self.syn_type == 'conductance':
self.esyn_TM = h.tmgsyn(0.5, sec=self)
self.isyn_TM = h.tmgsyn(0.5, sec=self)
else:
self.esyn_TM = h.tmisyn(0.5, sec=self)
self.isyn_TM = h.tmisyn(0.5, sec=self)
# insert current source
self.stim = h.IClamp(0.5, sec=self)
self.stim.delay = 0
self.stim.dur = 1e12
self.stim.amp = i_offset
# for recording
self.spike_times = h.Vector(0)
self.traces = {}
self.gsyn_trace = {}
self.recording_time = 0
self.v_init = None
@property
def excitatory(self):
return self.esyn
@property
def inhibitory(self):
return self.isyn
@property
def excitatory_TM(self):
if hasattr(self, 'esyn_TM'):
return self.esyn_TM
else:
return None
@property
def inhibitory_TM(self):
if hasattr(self, 'isyn_TM'):
return self.isyn_TM
else:
return None
def area(self):
"""Membrane area in µm²"""
return pi*self.L*self.seg.diam
c_m = _new_property('seg', 'cm')
i_offset = _new_property('stim', 'amp')
def _get_tau_e(self):
return self.esyn.tau
def _set_tau_e(self, value):
self.esyn.tau = value
if hasattr(self, 'esyn_TM'):
self.esyn_TM.tau = value
tau_e = property(fget=_get_tau_e, fset=_set_tau_e)
def _get_tau_i(self):
return self.isyn.tau
def _set_tau_i(self, value):
self.isyn.tau = value
if hasattr(self, 'isyn_TM'):
self.isyn_TM.tau = value
tau_i = property(fget=_get_tau_i, fset=_set_tau_i)
def _get_e_e(self):
return self.esyn.e
def _set_e_e(self, value):
self.esyn.e = value
if hasattr(self, 'esyn_TM'):
self.esyn_TM.e = value
e_e = property(fget=_get_e_e, fset=_set_e_e)
def _get_e_i(self):
return self.isyn.e
def _set_e_i(self, value):
self.isyn.e = value
if hasattr(self, 'isyn_TM'):
self.isyn_TM.e = value
e_i = property(fget=_get_e_i, fset=_set_e_i)
def record(self, active):
if active:
rec = h.NetCon(self.source, None)
rec.record(self.spike_times)
else:
self.spike_times = h.Vector(0)
def record_v(self, active):
if active:
self.vtrace = h.Vector()
self.vtrace.record(self(0.5)._ref_v)
if not self.recording_time:
self.record_times = h.Vector()
self.record_times.record(h._ref_t)
self.recording_time += 1
else:
self.vtrace = None
self.recording_time -= 1
if self.recording_time == 0:
self.record_times = None
def record_gsyn(self, syn_name, active):
# how to deal with static and T-M synapses?
# record both and sum?
if active:
self.gsyn_trace[syn_name] = h.Vector()
self.gsyn_trace[syn_name].record(getattr(self, syn_name)._ref_g)
if not self.recording_time:
self.record_times = h.Vector()
self.record_times.record(h._ref_t)
self.recording_time += 1
else:
self.gsyn_trace[syn_name] = None
self.recording_time -= 1
if self.recording_time == 0:
self.record_times = None
def memb_init(self):
assert self.v_init is not None, "cell is a %s" % self.__class__.__name__
for seg in self:
seg.v = self.v_init
#self.seg.v = self.v_init
def set_Tsodyks_Markram_synapses(self, ei, U, tau_rec, tau_facil, u0):
if self.syn_shape == 'alpha':
raise Exception("Tsodyks-Markram mechanism not available for alpha-function-shaped synapses.")
elif ei == 'excitatory':
syn = self.esyn_TM
elif ei == 'inhibitory':
syn = self.isyn_TM
else:
raise Exception("Tsodyks-Markram mechanism not yet implemented for user-defined synapse types. ei = %s" % ei)
syn.U = U
syn.tau_rec = tau_rec
syn.tau_facil = tau_facil
syn.u0 = u0
def set_parameters(self, param_dict):
for name in self.parameter_names:
setattr(self, name, param_dict[name])
class LeakySingleCompartmentNeuron(SingleCompartmentNeuron):
def __init__(self, syn_type, syn_shape, tau_m, c_m, v_rest, i_offset,
tau_e, tau_i, e_e, e_i):
SingleCompartmentNeuron.__init__(self, syn_type, syn_shape, c_m, i_offset,
tau_e, tau_i, e_e, e_i)
self.insert('pas')
self.v_init = v_rest # default value
def __set_tau_m(self, value):
#print "setting tau_m to", value, "cm =", self.seg.cm
self.seg.pas.g = 1e-3*self.seg.cm/value # cm(nF)/tau_m(ms) = G(uS) = 1e-6G(S). Divide by area (1e-3) to get factor of 1e-3
def __get_tau_m(self):
#print "tau_m = ", 1e-3*self.seg.cm/self.seg.pas.g, "cm = ", self.seg.cm
return 1e-3*self.seg.cm/self.seg.pas.g
def __get_cm(self):
#print "cm = ", self.seg.cm
return self.seg.cm
def __set_cm(self, value): # when we set cm, need to change g to maintain the same value of tau_m
#print "setting cm to", value
tau_m = self.tau_m
self.seg.cm = value
self.tau_m = tau_m
v_rest = _new_property('seg.pas', 'e')
tau_m = property(fget=__get_tau_m, fset=__set_tau_m)
c_m = property(fget=__get_cm, fset=__set_cm) # if the property were called 'cm'
# it would never get accessed as the
# built-in Section.cm would always
# be used first
class StandardIF(LeakySingleCompartmentNeuron):
"""docstring"""
def __init__(self, syn_type, syn_shape, tau_m=20, c_m=1.0, v_rest=-65,
v_thresh=-55, t_refrac=2, i_offset=0, v_reset=None,
tau_e=5, tau_i=5, e_e=0, e_i=-70):
if v_reset is None:
v_reset = v_rest
LeakySingleCompartmentNeuron.__init__(self, syn_type, syn_shape, tau_m, c_m, v_rest,
i_offset, tau_e, tau_i, e_e, e_i)
# insert spike reset mechanism
self.spike_reset = h.ResetRefrac(0.5, sec=self)
self.spike_reset.vspike = 40 # (mV) spike height
self.source = self.spike_reset
# process arguments
self.parameter_names = ['c_m', 'tau_m', 'v_rest', 'v_thresh', 't_refrac', # 'c_m' must come before 'tau_m'
'i_offset', 'v_reset', 'tau_e', 'tau_i']
if syn_type == 'conductance':
self.parameter_names.extend(['e_e', 'e_i'])
self.set_parameters(locals())
v_thresh = _new_property('spike_reset', 'vthresh')
v_reset = _new_property('spike_reset', 'vreset')
t_refrac = _new_property('spike_reset', 'trefrac')
class BretteGerstnerIF(LeakySingleCompartmentNeuron):
"""docstring"""
def __init__(self, syn_type, syn_shape, tau_m=20, c_m=1.0, v_rest=-65,
v_thresh=-55, t_refrac=2, i_offset=0,
tau_e=5, tau_i=5, e_e=0, e_i=-70,
v_spike=0.0, v_reset=-70.6, A=4.0, B=0.0805, tau_w=144.0,
delta=2.0):
LeakySingleCompartmentNeuron.__init__(self, syn_type, syn_shape, tau_m,
c_m, v_rest, i_offset,
tau_e, tau_i, e_e, e_i)
# insert Brette-Gerstner spike mechanism
self.adexp = h.AdExpIF(0.5, sec=self)
self.source = self.seg._ref_v
self.parameter_names = ['c_m', 'tau_m', 'v_rest', 'v_thresh', 't_refrac',
'i_offset', 'v_reset', 'tau_e', 'tau_i',
'A', 'B', 'tau_w', 'delta', 'v_spike']
if syn_type == 'conductance':
self.parameter_names.extend(['e_e', 'e_i'])
self.set_parameters(locals())
self.w_init = None
v_thresh = _new_property('adexp', 'vthresh')
v_reset = _new_property('adexp', 'vreset')
t_refrac = _new_property('adexp', 'trefrac')
B = _new_property('adexp', 'b')
A = _new_property('adexp', 'a')
## using 'A' because for some reason, cell.a gives the error "NameError: a, the mechanism does not exist at PySec_170bb70(0.5)"
tau_w = _new_property('adexp', 'tauw')
delta = _new_property('adexp', 'delta')
def __set_v_spike(self, value):
self.adexp.vspike = value
self.adexp.vpeak = value + 10.0
def __get_v_spike(self):
return self.adexp.vspike
v_spike = property(fget=__get_v_spike, fset=__set_v_spike)
def __set_tau_m(self, value):
self.seg.pas.g = 1e-3*self.seg.cm/value # cm(nF)/tau_m(ms) = G(uS) = 1e-6G(S). Divide by area (1e-3) to get factor of 1e-3
self.adexp.GL = self.seg.pas.g * self.area() * 1e-2 # S/cm2 to uS
def __get_tau_m(self):
return 1e-3*self.seg.cm/self.seg.pas.g
def __set_v_rest(self, value):
self.seg.pas.e = value
self.adexp.EL = value
def __get_v_rest(self):
return self.seg.pas.e
tau_m = property(fget=__get_tau_m, fset=__set_tau_m)
v_rest = property(fget=__get_v_rest, fset=__set_v_rest)
def record(self, active):
if active:
self.rec = h.NetCon(self.source, None,
self.get_threshold(), 0.0, 0.0,
sec=self)
self.rec.record(self.spike_times)
def get_threshold(self):
return self.adexp.vspike
def memb_init(self):
assert self.v_init is not None, "cell is a %s" % self.__class__.__name__
assert self.w_init is not None
for seg in self:
seg.v = self.v_init
seg.w = self.w_init
class GsfaGrrIF(StandardIF):
"""docstring"""
def __init__(self, syn_type, syn_shape, tau_m=10.0, c_m=1.0, v_rest=-70.0,
v_thresh=-57.0, t_refrac=0.1, i_offset=0.0,
tau_e=1.5, tau_i=10.0, e_e=0.0, e_i=-75.0,
v_spike=0.0, v_reset=-70.0, q_rr=3214.0, q_sfa=14.48,
e_rr=-70.0, e_sfa=-70.0,
tau_rr=1.97, tau_sfa=110.0):
StandardIF.__init__(self, syn_type, syn_shape, tau_m, c_m, v_rest,
v_thresh, t_refrac, i_offset, v_reset,
tau_e, tau_i, e_e, e_i)
# insert GsfaGrr mechanism
self.gsfa_grr = h.GsfaGrr(0.5, sec=self)
self.v_thresh = v_thresh
self.parameter_names = ['c_m', 'tau_m', 'v_rest', 'v_thresh', 'v_reset',
't_refrac', 'tau_e', 'tau_i', 'i_offset',
'e_rr', 'e_sfa', 'q_rr', 'q_sfa', 'tau_rr', 'tau_sfa']
if syn_type == 'conductance':
self.parameter_names.extend(['e_e', 'e_i'])
self.set_parameters(locals())
q_sfa = _new_property('gsfa_grr', 'q_s')
q_rr = _new_property('gsfa_grr', 'q_r')
tau_sfa = _new_property('gsfa_grr', 'tau_s')
tau_rr = _new_property('gsfa_grr', 'tau_r')
e_sfa = _new_property('gsfa_grr', 'E_s')
e_rr = _new_property('gsfa_grr', 'E_r')
def __set_v_thresh(self, value):
self.spike_reset.vthresh = value
# this can fail on constructor
try:
self.gsfa_grr.vthresh = value
except AttributeError:
pass
def __get_v_thresh(self):
return self.spike_reset.vthresh
v_thresh = property(fget=__get_v_thresh, fset=__set_v_thresh)
class SingleCompartmentTraub(SingleCompartmentNeuron):
def __init__(self, syn_type, syn_shape, c_m=1.0, e_leak=-65,
i_offset=0, tau_e=5, tau_i=5, e_e=0, e_i=-70,
gbar_Na=20e-3, gbar_K=6e-3, g_leak=0.01e-3, ena=50,
ek=-90, v_offset=-63):
"""
Conductances are in millisiemens (S/cm2, since A = 1e-3)
"""
SingleCompartmentNeuron.__init__(self, syn_type, syn_shape, c_m, i_offset,
tau_e, tau_i, e_e, e_i)
self.source = self.seg._ref_v
self.insert('k_ion')
self.insert('na_ion')
self.insert('hh_traub')
self.parameter_names = ['c_m', 'e_leak', 'i_offset', 'tau_e',
'tau_i', 'gbar_Na', 'gbar_K', 'g_leak', 'ena',
'ek', 'v_offset']
if syn_type == 'conductance':
self.parameter_names.extend(['e_e', 'e_i'])
self.set_parameters(locals())
self.v_init = e_leak # default value
# not sure ena and ek are handled correctly
e_leak = _new_property('seg.hh_traub', 'el')
v_offset = _new_property('seg.hh_traub', 'vT')
gbar_Na = _new_property('seg.hh_traub', 'gnabar')
gbar_K = _new_property('seg.hh_traub', 'gkbar')
g_leak = _new_property('seg.hh_traub', 'gl')
def get_threshold(self):
return 10.0
def record(self, active):
if active:
rec = h.NetCon(self.source, None, sec=self)
rec.record(self.spike_times)
class RandomSpikeSource(hclass(h.NetStimFD)):
parameter_names = ('start', '_interval', 'duration')
def __init__(self, start=0, _interval=1e12, duration=0):
self.start = start
self.interval = _interval
self.duration = duration
self.noise = 1
self.spike_times = h.Vector(0)
self.source = self
self.switch = h.NetCon(None, self)
self.source_section = None
self.seed(state.mpi_rank) # should allow user to set specific seeds somewhere, e.g. in setup()
def _set_interval(self, value):
self.switch.weight[0] = -1
self.switch.event(h.t+1e-12, 0)
self.interval = value
self.switch.weight[0] = 1
self.switch.event(h.t+2e-12, 1)
def _get_interval(self):
return self.interval
_interval = property(fget=_get_interval, fset=_set_interval)
def record(self, active):
if active:
self.rec = h.NetCon(self, None)
self.rec.record(self.spike_times)
class VectorSpikeSource(hclass(h.VecStim)):
parameter_names = ('spike_times',)
def __init__(self, spike_times=[]):
self.spike_times = spike_times
self.source = self
self.source_section = None
def _set_spike_times(self, spike_times):
try:
self._spike_times = h.Vector(spike_times)
except RuntimeError:
raise errors.InvalidParameterValueError("spike_times must be an array of floats")
self.play(self._spike_times)
def _get_spike_times(self):
return self._spike_times
spike_times = property(fget=_get_spike_times,
fset=_set_spike_times)
def record(self, active):
"""
Since spike_times are specified by user, recording is meaningless, but
we need to provide a stub for consistency with other models.
"""
pass
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