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398 lines
13 KiB
398 lines
13 KiB
2 years ago
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"""
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The Assumptions of this cell are modified to the test_sgc_input_PSTH.py but should be identical in function
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synapse type = simple
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synapse type = single
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species = mouse
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sgc = dummy SGC lacks cell body
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cell connection = pyramidal
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"""
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import sys
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import numpy as np
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import pyqtgraph as pg
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from neuron import h
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from cnmodel.protocols import Protocol
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from cnmodel import cells
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from cnmodel.util import sound
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from cnmodel.util import custom_init
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import cnmodel.util.pynrnutilities as PU
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from cnmodel import data
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synapseType = "simple" # no other options exist
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species = "mouse" # tables for other species do not yet exist
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def main():
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prot = SGCInputTestPSTH(seed=1233451898)
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prot.run()
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prot.show()
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if sys.flags.interactive == 0:
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pg.QtGui.QApplication.exec_()
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class SGCInputTestPSTH(Protocol):
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def __init__(self, temp=34.0, dt=0.025, seed=575982035):
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"""
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:param temp: Celcius
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:param dt: resolution in ms
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:param seed: seed should be generated randomly
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Pre-determined variables that can be changed by editing the value next to them
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AMPA_gmax and n_sgc = loaded from data that is kept in cnmodel/data dir
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Defines a single tone parameters with which to test the neuron complex
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"""
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# super only defines reset function
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super(SGCInputTestPSTH, self).__init__()
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#
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self.temp = temp
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self.dt = dt
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self.seed = seed
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self.simulator = "cochlea"
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# predetermined variables
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self.cell = "pyramidal"
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self.nrep = 50 # number of repetitions
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self.stimulus = "tone"
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self.Fs = 100e3 # in Hz
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self.f0 = 4000.0 # stimulus in Hz
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self.cf = 4000.0 # SGCs in Hz
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self.fMod = 100.0 # mod freq, Hz
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self.dMod = 0.0 # % mod depth, Hz
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self.dbspl = 50.0
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self.sr = 2 # set SR group
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# variables loaded from data
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AMPA_gmax, n_sgc = self.load_variables_from_data()
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self.n_sgc = int(np.round(n_sgc))
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# value needed for simple synapses
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self.AMPA_gmax = AMPA_gmax
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# the stimulation delivered is a Tone
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self.run_duration = 0.20 # in seconds
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self.pip_duration = 0.05 # in seconds
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self.pip_start = [0.1] # in seconds
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self.f0 = 4000.0
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self.cf = 4000.0
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self.stim = sound.TonePip(
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rate=self.Fs,
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duration=self.run_duration,
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f0=self.f0,
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dbspl=self.dbspl,
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ramp_duration=2.5e-3,
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pip_duration=self.pip_duration,
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pip_start=self.pip_start,
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)
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# creates empty lists that will contain the results of each rep
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self.vms = [None for n in range(self.nrep)]
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self.synapses = [None for n in range(self.nrep)]
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self.xmtrs = [None for n in range(self.nrep)]
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self.pre_cells = [None for n in range(self.nrep)]
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self.time = [None for n in range(self.nrep)]
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def load_variables_from_data(self):
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AMPA_gmax = (
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data.get(
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"sgc_synapse", species=species, post_type=self.cell, field="AMPA_gmax"
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)[0]
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/ 1e3
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)
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n_sgc = data.get(
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"convergence", species=species, post_type=self.cell, pre_type="sgc"
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)[0]
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print(n_sgc)
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return AMPA_gmax, n_sgc
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def check_assertations(self):
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assert self.cell in [
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"bushy",
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"tstellate",
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"octopus",
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"dstellate",
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"tuberculoventral",
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"pyramidal",
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]
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assert self.stimulus == "tone" # cases available
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assert synapseType == "simple"
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def run(self):
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self.check_assertations()
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# info based on the parameters set in the __init__ statement
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info = {
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"n_sgc": self.n_sgc,
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"gmax": self.AMPA_gmax,
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"stim": self.stim,
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"simulator": self.simulator,
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"cf": self.cf,
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"sr": self.sr,
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"seed": self.seed,
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"run_duration": self.run_duration,
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"temp": self.temp,
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"dt": self.dt,
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"init": custom_init,
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}
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# run number of trials based on nrep defined in __init__ statement
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for nr in range(self.nrep):
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info["seed"] = self.seed + 3 * self.n_sgc * nr
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res = run_trial(info)
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# res contains: {'time': time, 'vm': Vm, 'xmtr': xmtr, 'pre_cells': pre_cells, 'post_cell': post_cell}
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# unpacks the res dict returned from the run_trial() into refrenceable variables
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self.pre_cells[nr] = res["pre_cells"]
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self.time[nr] = res["time"]
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self.xmtr = {k: v.to_python() for k, v in res["xmtr"].items()}
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self.vms[nr] = res["vm"]
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self.synapses[nr] = res["synapses"]
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self.xmtrs[nr] = self.xmtr
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def show(self):
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"""
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Creates a single page graph that contains all of the graphs based on the graphical functions in the class
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"""
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self.win = pg.GraphicsWindow()
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self.win.setBackground("w")
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p1 = self.stimulus_graph()
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p2 = self.an_spikes_graph()
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p3 = self.cell_spikes_graph()
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p4 = self.voltage_graph()
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p5 = self.xmtr_graph()
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p6 = (
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self.an_psth_graph()
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) # requires that an_spikes_graph() has been called before
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p7 = (
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self.cell_psth_graph()
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) # requires that cell_spikes_graph() has been called before
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# links x axis
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p1.setXLink(p1)
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p2.setXLink(p1)
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p3.setXLink(p1)
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p4.setXLink(p1)
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p5.setXLink(p1)
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self.win.show()
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############# Graph options to be included in the show() method ###################
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def stimulus_graph(self):
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p1 = self.win.addPlot(
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title="Stimulus", row=0, col=0, labels={"bottom": "T (ms)", "left": "V"}
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)
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p1.plot(
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self.stim.time * 1000, self.stim.sound, pen=pg.mkPen("k", width=0.75)
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)
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return p1
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def an_spikes_graph(self):
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p2 = self.win.addPlot(
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title="AN spikes",
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row=1,
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col=0,
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labels={"bottom": "T (ms)", "left": "AN spikes (first trial)"},
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)
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for nr in range(self.nrep):
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xan = []
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yan = []
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for k in range(len(self.pre_cells[nr])):
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r = self.pre_cells[nr][k]._spiketrain
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xan.extend(r)
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yr = k + np.zeros_like(r) + 0.2
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yan.extend(yr)
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c = pg.PlotCurveItem()
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xp = np.repeat(np.array(xan), 2)
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yp = np.repeat(np.array(yan), 2)
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yp[1::2] = yp[::2] + 0.6
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c.setData(
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xp.flatten(),
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yp.flatten(),
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connect="pairs",
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pen=pg.mkPen(pg.intColor(nr, self.nrep), hues=self.nrep, width=1.0),
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)
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self.xan = xan
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self.yan = yan
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p2.addItem(c)
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return p2
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def cell_spikes_graph(self):
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p3 = self.win.addPlot(
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title="%s Spikes" % self.cell,
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row=2,
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col=0,
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labels={"bottom": "T (ms)", "left": "Trial #"},
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)
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xcn = []
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ycn = []
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xspks = []
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for k in range(self.nrep):
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bspk = PU.findspikes(self.time[k], self.vms[k], -35.0)
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xcn.extend(bspk)
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yr = k + np.zeros_like(bspk) + 0.2
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ycn.extend(yr)
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d = pg.PlotCurveItem()
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xp = np.repeat(np.array(xcn), 2)
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yp = np.repeat(np.array(ycn), 2)
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yp[1::2] = yp[::2] + 0.6
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d.setData(
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xp.flatten(),
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yp.flatten(),
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connect="pairs",
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pen=pg.mkPen("k", width=1.5),
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)
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self.xcn = xcn
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self.ycn = ycn
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p3.addItem(d)
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return p3
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def voltage_graph(self):
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p4 = self.win.addPlot(
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title="%s Vm" % self.cell,
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row=3,
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col=0,
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labels={"bottom": "T (ms)", "left": "Vm (mV)"},
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)
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for nr in range(self.nrep):
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p4.plot(
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self.time[nr],
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self.vms[nr],
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pen=pg.mkPen(pg.intColor(nr, self.nrep), hues=self.nrep, width=1.0),
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)
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return p4
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def xmtr_graph(self):
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p5 = self.win.addPlot(
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title="xmtr", row=0, col=1, labels={"bottom": "T (ms)", "left": "gSyn"}
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)
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if synapseType == "multisite":
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for nr in [0]:
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syn = self.synapses[nr]
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j = 0
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for k in range(self.n_sgc):
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synapse = syn[k]
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for i in range(synapse.terminal.n_rzones):
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p5.plot(
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self.time[nr],
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self.xmtrs[nr]["xmtr%04d" % j],
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pen=pg.mkPen(
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pg.intColor(nr, self.nrep),
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hues=self.nrep,
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width=1.0,
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),
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)
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j = j + 1
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return p5
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def an_psth_graph(self):
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p6 = self.win.addPlot(
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title="AN PSTH",
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row=1,
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col=1,
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labels={"bottom": "T (ms)", "left": "Sp/ms/trial"},
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)
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bins = np.arange(0, 200, 1)
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(hist, binedges) = np.histogram(self.xan, bins)
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curve6 = p6.plot(
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binedges,
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hist,
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stepMode=True,
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fillBrush=(0, 0, 0, 255),
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brush=pg.mkBrush("k"),
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fillLevel=0,
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)
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return p6
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def cell_psth_graph(self):
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p7 = self.win.addPlot(
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title="%s PSTH" % self.cell,
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row=2,
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col=1,
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labels={"bottom": "T (ms)", "left": "Sp/ms/trial"},
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)
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bins = np.arange(0, 200, 1)
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(hist, binedges) = np.histogram(self.xcn, bins)
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curve7 = p7.plot(
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binedges,
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hist,
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stepMode=True,
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fillBrush=(0, 0, 0, 255),
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brush=pg.mkBrush("k"),
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fillLevel=0,
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)
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return p7
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def run_trial(info):
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"""
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This function is really the bread
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and butter of the run and determines the layout
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of the model and the stimulus is
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created and delivered by the SGC.
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Runs a single trial and returns it
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into another dictionary that is stored
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before the next trial is run.
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:param info: dict containing :'n_sgc': self.n_sgc,
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'gmax': self.AMPA_gmax,
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'stim': self.stim,
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'simulator': self.simulator,
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'cf': self.cf, 'sr': self.sr,
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'seed': self.seed,
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'run_duration': self.run_duration,
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'temp': self.temp,
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'dt': self.dt,
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'init': custom_init
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post cell allowed to use defaults in the case of things like pyramidal cells are guinea pigs instead of a mouse
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"""
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# for the model to change the post_cell needs to be changed here otherwise the model will not be changed.
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# special consideration needs to be taken because not all cell parameters are universal
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# ex: Pyramidal does not have a species='mouse', nothing just assumes a default and not all defaults are the same
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post_cell = cells.Pyramidal.create()
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pre_cells = []
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synapses = []
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xmtr = {}
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# connects all of the SGC fibers to the post_cell using simple synapses and then generates a sound stim based on a
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# seed the number of repetitions desired
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for nsgc, sgc in enumerate(range(info["n_sgc"])):
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pre_cells.append(cells.DummySGC(cf=info["cf"], sr=info["sr"]))
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synapses.append(pre_cells[-1].connect(post_cell, type="simple"))
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|
synapses[-1].terminal.netcon.weight[0] = info["gmax"]
|
||
|
|
|
||
|
|
# sets sounds stim for each of the SGC fibers independently
|
||
|
|
pre_cells[-1].set_sound_stim(
|
||
|
|
info["stim"], seed=info["seed"] + nsgc, simulator=info["simulator"]
|
||
|
|
)
|
||
|
|
|
||
|
|
# Recording each trial and returning it as a dictionary
|
||
|
|
Vm = h.Vector()
|
||
|
|
Vm.record(post_cell.soma(0.5)._ref_v)
|
||
|
|
rtime = h.Vector()
|
||
|
|
rtime.record(h._ref_t)
|
||
|
|
h.tstop = 1e3 * info["run_duration"] # duration of a run
|
||
|
|
h.celsius = info["temp"]
|
||
|
|
h.dt = info["dt"]
|
||
|
|
post_cell.cell_initialize()
|
||
|
|
info["init"]()
|
||
|
|
h.t = 0.0
|
||
|
|
h.run()
|
||
|
|
return {
|
||
|
|
"time": np.array(rtime),
|
||
|
|
"vm": Vm.to_python(),
|
||
|
|
"xmtr": xmtr,
|
||
|
|
"pre_cells": pre_cells,
|
||
|
|
"post_cell": post_cell,
|
||
|
|
"synapses": synapses,
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
main()
|