def __init__(self, sensory_unit=None, T=5, dt=.001, **kwargs):
"""
Pass in a wc unit, set up recordings, get ready to run.
Args:
sensory_unit (SensoryWCUnit): it should already have everything you want connected to it
T (float): T in seconds
dt (float): integration timestep (seconds)
**kwargs:
Derived attributes:
self.tax (numpy.array): time axis
self.ttot (int): total number of time steps
self.t_i (int): the current time step
self.traces (dict): traces
"""
Simulation.__init__(self, T=T, dt=dt, **kwargs)
self.unit = sensory_unit
trace_sources = {
"FR": sensory_unit.r,
"stim": sensory_unit.stim,
"SFA": sensory_unit.a,
# "u1_S": wc_unit.S
}
for k, v in trace_sources.items():
self.add_new_trace(source=v, trace_name=k)
def __init__(self, network, T=5, dt=.001, **kwargs):
"""
Pass in a wc unit, set up recordings, get ready to run.
Args:
sensory_units (SensoryWCUnit): it should already have everything you want connected to it
T (float): T in seconds
dt (float): integration timestep (seconds)
**kwargs:
Derived attributes:
self.tax (numpy.array): time axis
self.ttot (int): total number of time steps
self.t_i (int): the current time step
self.traces (dict): traces
"""
Simulation.__init__(self, T=T, dt=dt, **kwargs)
self.network = network
self.traces = OrderedDict()
# just slap a trace on there?
for unit in self.network.units:
# we'll just staple the traces to the unit, maybe this is wrong but it should make things easier
unit.trace_dict = OrderedDict()
trace_sources = OrderedDict(
FR=unit.r) # , stim=unit.stim, SFA=unit.a
# )
for k, v in trace_sources.items():
self.add_new_trace(unit, source=v, trace_name=k)
current_trace_sources = unit.currents
for k, v in current_trace_sources.items():
self.add_new_current_trace(unit, source=v, trace_name=k)
def __init__(self, network, T=5, dt=.001, **kwargs):
"""
Pass in a wc unit, set up recordings, get ready to run.
Args:
sensory_units (SensoryWCUnit): it should already have everything you want connected to it
T (float): T in seconds
dt (float): integration timestep (seconds)
**kwargs:
Derived attributes:
self.tax (numpy.array): time axis
self.ttot (int): total number of time steps
self.t_i (int): the current time step
self.traces (dict): traces
"""
Simulation.__init__(self, T=T, dt=dt, **kwargs)
self.network = network
self.traces = OrderedDict()
for name, unit in self.network.units.items():
trace_sources = OrderedDict(
FR=unit.r, stim=unit.stim, SFA=unit.a
)
self.traces[unit.name] = OrderedDict()
for k, v in trace_sources.items():
self.add_new_trace(unit, source=v, trace_name=k)
def __init__(self,
syn_weights=weights,
selectivities=s_units,
stimulus=stim,
**kwargs):
TonotopicNetwork.__init__(self, selectivities, stimulus, **kwargs)
Simulation.__init__(self, **kwargs) # is this black magic?
""" this guy would take a bunch of selectivities and make some units """
self.syn_weights = syn_weights
# now we build us some arrays - we're going to vectorize
self.R_var_array = np.zeros([len(self.units), self.ttot])
self.A_var_array = np.zeros([len(self.units), self.ttot])
self.S_var_array = np.zeros([len(self.units), self.ttot])
# pull out the vars from the vars arrays
self.vars = [
x.split("_")[0] for x in self.__dict__ if x.endswith("_var_array")
]
self.stim_currents = self.build_stimulus_currents()
# list of units
units_array = self.units
self.gstims = np.array(
[x.currents["stim"].weight for x in units_array])
self.i_0s = np.array([x.i_0 for x in units_array])
self.gees = np.array([x.gee for x in units_array])
self.gSFAs = np.array([x.gSFA for x in units_array])
self.Gs = np.array([x.G for x in units_array])
self.taus = np.array([x.tau for x in units_array])
self.tauAs = np.array([x.tauA for x in units_array])
self.tauNMDAs = np.array([x.tauNMDA for x in units_array])
# activation function parameters
self.kes = np.array([x.ke for x in units_array])
self.thes = np.array([x.the for x in units_array])
self.f_e = self.f_activation_builder(self.kes, self.thes)
self.kSs = np.array([x.kS for x in units_array])
self.thSs = np.array([x.thS for x in units_array])
self.f_S = self.f_activation_builder(self.kSs, self.thSs, self.b_00)
# initialize
self.point_wise_Isyn = np.outer(self.syn_weights, self.R_var_array)