def __init__(self):
logging.debug('Loading PTcell')
neuron_utils.createProject(name='PTcell Neuron')
resource_package = "neuron_ui"
resource_path = '/'.join(('models', 'PTcell.hoc'))
filepath = pkg_resources.resource_filename(resource_package,
resource_path)
h.load_file(filepath)
self.PTCell = h.PTcell()
self.t_vec = h.Vector()
self.t_vec.record(h._ref_t)
neuron_utils.createStateVariable(id='time',
name='time',
units='ms',
python_variable={
"record_variable": self.t_vec,
"segment": None
})
neuron_geometries_utils.extractGeometries()
logging.debug('PTcell loaded')
def __init__(self):
print("loading very simple cell")
neuron_utils.createProject(name='Very Simple Cell')
print('Loading Model...')
self.soma = h.Section(name='soma')
self.soma.insert('pas')
self.asyn = h.AlphaSynapse(self.soma(0.5))
self.asyn.onset = 20
self.asyn.gmax = 1
self.v_vec = h.Vector() # Membrane potential vector
self.t_vec = h.Vector() # Time stamp vector
self.v_vec.record(self.soma(0.5)._ref_v)
neuron_utils.createStateVariable(id='v_vec',
name='v_vec',
units='ms',
python_variable={
"record_variable": self.v_vec,
"segment": self.soma(0.5)
})
self.t_vec.record(h._ref_t)
neuron_utils.createStateVariable(id='time',
name='time',
units='ms',
python_variable={
"record_variable": self.t_vec,
"segment": None
})
h.tstop = 80.0
neuron_geometries_utils.extractGeometries()
def __init__(self):
logging.debug('Loading HNN')
neuron_utils.createProject(name='HNN')
import run
self.t_vec = h.Vector()
self.t_vec.record(h._ref_t)
neuron_utils.createStateVariable(id='time',
name='time',
units='ms',
python_variable={
"record_variable": self.t_vec,
"segment": None
})
neuron_geometries_utils.extractGeometries()
logging.debug('HNN loaded')
self.RunSimButton = neuron_utils.add_button('Run',
extraData={'commands': []})
self.StopSimButton = neuron_utils.add_button(
'Stop', extraData={'commands': []})
self.SetParams = neuron_utils.add_button('Set Parameters')
self.BasePanel = neuron_utils.add_panel(
'HNN Control',
items=[self.RunSimButton, self.StopSimButton, self.SetParams],
widget_id='hnnBasePanel')
self.BasePanel.on_close(self.close)
self.BasePanel.display()
def __init__(self):
logging.debug('Loading CA3 Pyramidal')
neuron_utils.createProject(name='CA3 Pyramidal Neuron')
resource_package = "neuron_ui"
resource_path = '/'.join(('models', 'geo-cell1zr.hoc'))
filepath = pkg_resources.resource_filename(resource_package,
resource_path)
h.load_file(filepath)
neuron_geometries_utils.extractGeometries()
logging.debug('CA3 Pyramidal loaded')
def __init__(self,
N=5,
stim_w=0.004,
stim_number=1,
syn_w=0.01,
syn_delay=5):
"""
:param N: Number of cells.
:param stim_w: Weight of the stimulus
:param stim_number: Number of spikes in the stimulus
:param syn_w: Synaptic weight
:param syn_delay: Delay of the synapse
"""
neuron_utils.createProject(name='Ring')
self._N = N # Total number of cells in the net
self.cells = [] # Cells in the net
self.nclist = [] # NetCon list
self.stim = None # Stimulator
self.stim_w = stim_w # Weight of stim
self.stim_number = stim_number # Number of stim spikes
self.syn_w = syn_w # Synaptic weight
self.syn_delay = syn_delay # Synaptic delay
self.t_vec = h.Vector() # Spike time of all cells
self.id_vec = h.Vector() # Ids of spike times
self.set_numcells(N) # Actually build the net.
self.time_vec = h.Vector() # Time stamp vector
self.time_vec.record(h._ref_t)
neuron_utils.createStateVariable(id='time',
name='time',
units='ms',
python_variable={
"record_variable": self.time_vec,
"segment": None
})
for i in range(N):
self.set_recording_vectors(i)
h.tstop = 50
neuron_geometries_utils.extractGeometries()
def __init__(self, numcells):
neuron_utils.createProject(name='Simple Network')
self.spkt = h.Vector() # Spike time of all cells
self.spkid = h.Vector() # cell ids of spike times
self.create_cells(numcells) # call method to create cells
def __init__(self):
neuron_utils.createProject(name='Simple Cell')
self.soma = h.Section(name='soma')
self.dend = h.Section(name='dend')
self.dend.connect(self.soma(1))
# Surface area of cylinder is 2*pi*r*h (sealed ends are implicit).
# Makes a soma of 500 microns squared.
self.soma.L = self.soma.diam = 12.6157
self.dend.L = 200 # microns
self.dend.diam = 1 # microns
for sec in h.allsec():
sec.Ra = 100 # Axial resistance in Ohm * cm
sec.cm = 1 # Membrane capacitance in micro Farads / cm^2
# Insert active Hodgkin-Huxley current in the soma
self.soma.insert('hh')
self.soma.gnabar_hh = 0.12 # Sodium conductance in S/cm2
self.soma.gkbar_hh = 0.036 # Potassium conductance in S/cm2
self.soma.gl_hh = 0.0003 # Leak conductance in S/cm2
self.soma.el_hh = -54.3 # Reversal potential in mV
# Insert passive current in the dendrite
self.dend.insert('pas')
self.dend.g_pas = 0.001 # Passive conductance in S/cm2
self.dend.e_pas = -65 # Leak reversal potential mV
self.dend.nseg = 10
# add synapse (custom channel)
self.syn = h.AlphaSynapse(self.dend(1.0))
self.syn.e = 0 # equilibrium potential in mV
self.syn.onset = 20 # turn on after this time in ms
self.syn.gmax = 0.05 # set conductance in uS
self.syn.tau = 0.1 # set time constant
# self.stim = h.IClamp(self.dend(1.0))
# self.stim.amp = 0.3 # input current in nA
# self.stim.delay = 1 # turn on after this time in ms
# self.stim.dur = 1 # duration of 1 ms
# record soma voltage and time
self.t_vec = h.Vector()
self.t_vec.record(h._ref_t)
neuron_utils.createStateVariable(id='time',
name='time',
units='ms',
python_variable={
"record_variable": self.t_vec,
"segment": None
})
self.v_vec_soma = h.Vector()
self.v_vec_soma.record(self.soma(1.0)._ref_v) # change recoding pos
# TODO How do we extract the units?
neuron_utils.createStateVariable(id='v_vec_soma',
name='v_vec_soma',
units='mV',
python_variable={
"record_variable":
self.v_vec_soma,
"segment": self.soma(1.0)
})
self.v_vec_dend = h.Vector()
self.v_vec_dend.record(self.dend(1.0)._ref_v)
# TODO How do we extract the units?
neuron_utils.createStateVariable(id='v_vec_dend',
name='v_vec_dend',
units='mV',
python_variable={
"record_variable":
self.v_vec_dend,
"segment": self.dend(1.0)
})
# run simulation
h.tstop = 60 # ms
# h.run()
neuron_geometries_utils.extractGeometries()