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517 lines
20 KiB
517 lines
20 KiB
2 years ago
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from __future__ import print_function
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from neuron import h
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from .cell import Cell
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# from .. import synapses
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from ..util import nstomho
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from ..util import Params
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import numpy as np
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from .. import data
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__all__ = ["MSO"]
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class MSO(Cell):
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type = "mso"
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@classmethod
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def create(cls, model="MSO-principal", **kwds):
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if model == "MSO-principal":
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return MSOPrincipal(**kwds)
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else:
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raise ValueError("MSO cell model %s is unknown", model)
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def make_psd(self, terminal, psd_type, **kwds):
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"""
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Connect a presynaptic terminal to one post section at the specified location, with the fraction
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of the "standard" conductance determined by gbar.
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The default condition is designed to pass the unit test (loc=0.5)
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Parameters
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----------
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terminal : Presynaptic terminal (NEURON object)
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psd_type : either simple or multisite PSD for MSO cell
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kwds: dictionary of options.
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Two are currently handled:
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postsite : expect a list consisting of [sectionno, location (float)]
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AMPAScale : float to scale the ampa currents
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"""
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if (
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"postsite" in kwds
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): # use a defined location instead of the default (soma(0.5)
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postsite = kwds["postsite"]
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loc = postsite[1] # where on the section?
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uname = (
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"sections[%d]" % postsite[0]
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) # make a name to look up the neuron section object
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post_sec = self.hr.get_section(uname) # Tell us where to put the synapse.
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else:
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loc = 0.5
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post_sec = self.soma
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if psd_type == "simple":
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return self.make_exp2_psd(post_sec, terminal, loc=loc)
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elif psd_type == "multisite":
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if terminal.cell.type == "bushy":
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# Max conductances for the glu mechanisms are calibrated by
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# running `synapses/tests/test_psd.py`. The test should fail
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# if these values are incorrect
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self.AMPAR_gmax = (
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data.get(
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"bushy_synapse",
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species=self.species,
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post_type=self.type,
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field="AMPAR_gmax",
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)
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* 1e3
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)
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self.NMDAR_gmax = (
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data.get(
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"bushy_synapse",
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species=self.species,
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post_type=self.type,
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field="NMDAR_gmax",
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)
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* 1e3
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)
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self.Pr = data.get(
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"bushy_synapse",
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species=self.species,
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post_type=self.type,
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field="Pr",
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)
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# adjust gmax to correct for initial Pr
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self.AMPAR_gmax = self.AMPAR_gmax / self.Pr
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self.NMDAR_gmax = self.NMDAR_gmax / self.Pr
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if "AMPAScale" in kwds: # normally, this should not be done!
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self.AMPAR_gmax = (
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self.AMPAR_gmax * kwds["AMPAScale"]
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) # allow scaling of AMPA conductances
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if "NMDAScale" in kwds:
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self.NMDAR_gmax = self.NMDAR_gmax * kwds["NMDAScale"] # and NMDA...
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return self.make_glu_psd(
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post_sec, terminal, self.AMPAR_gmax, self.NMDAR_gmax, loc=loc
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)
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else:
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raise TypeError(
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"Cannot make PSD for %s => %s" % (terminal.cell.type, self.type)
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)
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else:
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raise ValueError("Unsupported psd type %s" % psd_type)
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class MSOPrincipal(MSO):
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"""
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VCN MSO cell models.
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Using Rothman and Manis, 2003abc (Type II)
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MSO principal cell type
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"""
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def __init__(
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self,
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morphology=None,
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decorator=None,
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nach=None,
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ttx=False,
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species="guineapig",
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modelType=None,
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debug=False,
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temperature=None,
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):
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"""
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Create a MSO principal cell, using the default parameters for guinea pig from
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R&M2003, as a type II cell.
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Additional modifications to the cell can be made by calling methods below.
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Parameters
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----------
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morphology : string (default: None)
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Name of a .hoc file representing the morphology. This file is used to constructe
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an electrotonic (cable) model.
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If None (default), then a "point" (really, single cylinder) model is made, exactly according to RM03.
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decorator : Python function (default: None)
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decorator is a function that "decorates" the morphology with ion channels according
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to a set of rules.
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If None, a default set of channels is inserted into the first soma section, and the
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rest of the structure is "bare".
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nach : string (default: None)
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nach selects the type of sodium channel that will be used in the model. A channel mechanism
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by that name must exist. The default channel is set to 'nacn' (R&M03)
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temperature : float (default: 22)
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temperature to run the cell at.
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ttx : Boolean (default: False)
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If ttx is True, then the sodium channel conductance is set to 0 everywhere in the cell.
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This flag duplicates the effects of tetrodotoxin in the model. Currently, the flag is not implemented.
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temperature : float (default: None, sets to model default of 22)
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temperature (deg C) to run the cell at. Must be a valid temperature for the model.
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species: string (default 'guineapig')
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species defines the pattern of ion channel densities that will be inserted, according to
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prior measurements in various species. Note that
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if a decorator function is specified, this argument is ignored as the decorator will
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specify the channel density.
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modelType: string (default: None)
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modelType specifies the subtype of the cell model that will be used (e.g., "II", "II-I", etc).
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modelType is passed to the decorator, or to species_scaling to adjust point (single cylinder) models.
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debug: boolean (default: False)
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When True, there will be multiple printouts of progress and parameters.
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Returns
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-------
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Nothing
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"""
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super(MSO, self).__init__()
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self.i_test_range = {
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"pulse": (-1, 1, 0.05)
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} # note that this gets reset with decorator according to channels
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# Changing the default values will cause the unit tests to fail!
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if modelType == None:
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modelType = "principal"
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if nach == None and species == "guineapig":
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nach = "na"
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if nach == None and species == "mouse":
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nach = "na"
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self.i_test_range = {"pulse": (-1, 1.2, 0.05)}
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self.status = {
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"soma": True,
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"axon": False,
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"dendrites": False,
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"pumps": False,
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"hillock": False,
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"initialsegment": False,
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"myelinatedaxon": False,
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"unmyelinatedaxon": False,
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"na": nach,
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"species": species,
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"modelType": modelType,
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"ttx": ttx,
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"name": "MSO",
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"morphology": morphology,
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"decorator": decorator,
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"temperature": temperature,
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}
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self.spike_threshold = -40
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self.vrange = [-70.0, -55.0] # set a default vrange for searching for rmp
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print("model type, species: ", modelType, species, nach)
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if morphology is None:
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"""
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instantiate a basic soma-only ("point") model
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"""
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print("<< MSO model: Creating point principal cell >>")
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soma = h.Section(
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name="MSO_Soma_%x" % id(self)
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) # one compartment of about 29000 um2
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soma.nseg = 1
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self.add_section(soma, "soma")
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else:
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"""
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instantiate a structured model with the morphology as specified by
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the morphology file
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"""
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print(
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"<< MSO principal cell model: Creating cell with morphology from %s >>"
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% morphology
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)
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self.set_morphology(morphology_file=morphology)
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# decorate the morphology with ion channels
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if decorator is None: # basic model, only on the soma
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self.mechanisms = ["klt", "kht", "ihvcn", "leak", nach]
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for mech in self.mechanisms:
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self.soma.insert(mech)
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self.soma.ena = self.e_na
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self.soma.ek = self.e_k
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self.soma().ihvcn.eh = self.e_h
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self.soma().leak.erev = self.e_leak
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self.c_m = 0.9
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self.species_scaling(
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silent=True, species=species, modelType=modelType
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) # set the default type II cell parameters
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else: # decorate according to a defined set of rules on all cell compartments
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self.decorate()
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self.save_all_mechs() # save all mechanisms inserted, location and gbar values...
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self.get_mechs(self.soma)
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if debug:
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print(" << Created cell >>")
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def get_cellpars(self, dataset, species="guineapig", celltype="principal"):
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cellcap = data.get(
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dataset, species=species, cell_type=celltype, field="soma_Cap"
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)
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chtype = data.get(
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dataset, species=species, cell_type=celltype, field="soma_na_type"
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)
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pars = Params(cap=cellcap, natype=chtype)
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for g in ["soma_kht_gbar", "soma_klt_gbar", "soma_ih_gbar", "soma_leak_gbar"]:
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pars.additem(
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g, data.get(dataset, species=species, cell_type=celltype, field=g)
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)
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return pars
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def species_scaling(self, species="guineapig", modelType="principal", silent=True):
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"""
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Adjust all of the conductances and the cell size according to the species requested.
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This scaling should be used ONLY for point models, as no other compartments
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are scaled.
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This scaling routine also sets the temperature for the model to a default value. Some models
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can be run at multiple temperatures, and so a default from one of the temperatures is used.
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The calling cell.set_temperature(newtemp) will change the conductances and reinitialize
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the cell to the new temperature settings.
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Parameters
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----------
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species : string (default: 'guineapig')
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name of the species to use for scaling the conductances in the base point model
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Must be one of mouse, cat, guineapig
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modelType: string (default: 'principal')
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definition of model type from RM03 models, principal cell for mso
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silent : boolean (default: True)
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run silently (True) or verbosely (False)
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"""
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# print '\nSpecies scaling: %s %s' % (species, type)
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knownspecies = ["guineapig"]
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soma = self.soma
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if modelType == "principal":
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celltype = (
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"MSO-principal"
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) # There are other possiblities in the literature - this is just the main one
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else:
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raise ValueError("model type not recognized")
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if species == "guineapig":
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print(
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" Setting conductances for guinea pig %s MSO cell, based on Rothman and Manis, 2003 bushy cell"
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% modelType
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)
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self._valid_temperatures = (22.0, 38.0)
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if self.status["temperature"] is None:
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self.status["temperature"] = 22.0
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self.i_test_range = {"pulse": (-0.4, 0.4, 0.02)}
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sf = 1.0
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if (
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self.status["temperature"] == 38.0
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): # adjust for 2003 model conductance levels at 38
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sf = 2 # Q10 of 2, 22->38C. (p3106, R&M2003c)
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# note that kinetics are scaled in the mod file.
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dataset = "MSO_principal_channels"
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pars = self.get_cellpars(dataset, species=species, celltype=celltype)
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self.set_soma_size_from_Cm(pars.cap)
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self.status["na"] = pars.natype
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self.adjust_na_chans(soma, sf=sf)
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soma().kht.gbar = nstomho(pars.soma_kht_gbar, self.somaarea)
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soma().klt.gbar = nstomho(pars.soma_klt_gbar, self.somaarea)
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soma().ihvcn.gbar = nstomho(pars.soma_ih_gbar, self.somaarea)
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soma().leak.gbar = nstomho(pars.soma_leak_gbar, self.somaarea)
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self.axonsf = 0.57
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else:
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errmsg = (
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'Species "%s" or model type "%s" is not recognized for MSO cells.'
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% (species, modelType)
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)
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errmsg += "\n Valid species are: \n"
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for s in knownspecies:
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errmsg += " %s\n" % s
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errmsg += "-" * 40
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raise ValueError(errmsg)
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self.status["species"] = species
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self.status["modelType"] = modelType
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self.check_temperature()
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# self.cell_initialize(vrange=self.vrange) # no need to do this just yet.
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if not silent:
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print(" set cell as: ", species)
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print(" with Vm rest = %6.3f" % self.vm0)
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def channel_manager(self, modelType="MSO-principal"):
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"""
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This routine defines channel density maps and distance map patterns
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for each type of compartment in the cell. The maps
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are used by the ChannelDecorator class (specifically, its private
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\_biophys function) to decorate the cell membrane.
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These settings are only used if the decorator is called; otherwise
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for point cells, the species_scaling routine defines the channel
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densities.
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Parameters
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----------
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modelType : string (default: 'RM03')
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A string that defines the type of the model. Currently, only 1 type is implemented:
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RM03: Rothman and Manis, 2003 somatic densities based on guinea pig bushy cell
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|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
Nothing
|
||
|
|
|
||
|
|
Notes
|
||
|
|
-----
|
||
|
|
This routine defines the following variables for the class:
|
||
|
|
|
||
|
|
* conductances (gBar)
|
||
|
|
* a channelMap (dictonary of channel densities in defined anatomical compartments)
|
||
|
|
* a current injection range for IV's (used for testing)
|
||
|
|
* a distance map, which defines how each conductance in a selected compartment
|
||
|
|
changes with distance from the soma. The current implementation includes both
|
||
|
|
linear and exponential gradients,
|
||
|
|
the minimum conductance at the end of the gradient, and the space constant or
|
||
|
|
slope for the gradient.
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
self.c_m = 1e-6 # default in units of F/cm^2
|
||
|
|
if modelType == "MSO-principal":
|
||
|
|
#
|
||
|
|
# Create a model based on the Rothman and Manis 2003 conductance set from guinea pig
|
||
|
|
#
|
||
|
|
self.c_m = 0.9e-6 # default in units of F/cm^2
|
||
|
|
totcap = 12.0e-12 # in units of F, from Rothman and Manis, 2003.
|
||
|
|
refarea = totcap / self.c_m # area is in cm^2
|
||
|
|
# MSO Rothman-Manis, guinea pig type II
|
||
|
|
# model gave cell conductance in nS, but we want S/cm^2 for NEURON
|
||
|
|
# so conversion is 1e-9*nS = uS, and refarea is already in cm2
|
||
|
|
self._valid_temperatures = (22.0, 38.0)
|
||
|
|
sf = 1.0
|
||
|
|
if self.status["temperature"] == None:
|
||
|
|
self.status["temperature"] = 22.0
|
||
|
|
if self.status["temperature"] == 38:
|
||
|
|
sf = 3.03
|
||
|
|
self.gBar = Params(
|
||
|
|
nabar=sf * 1000.0e-9 / refarea,
|
||
|
|
khtbar=sf * 150.0e-9 / refarea,
|
||
|
|
kltbar=sf * 200.0e-9 / refarea,
|
||
|
|
ihbar=sf * 20.0e-9 / refarea,
|
||
|
|
leakbar=sf * 2.0e-9 / refarea,
|
||
|
|
)
|
||
|
|
print("MSO principal channels gbar:\n", self.gBar.show())
|
||
|
|
|
||
|
|
self.channelMap = {
|
||
|
|
"axon": {
|
||
|
|
"nacn": self.gBar.nabar,
|
||
|
|
"klt": self.gBar.kltbar,
|
||
|
|
"kht": self.gBar.khtbar,
|
||
|
|
"ihvcn": 0.0,
|
||
|
|
"leak": self.gBar.leakbar / 2.0,
|
||
|
|
},
|
||
|
|
"hillock": {
|
||
|
|
"nacn": self.gBar.nabar,
|
||
|
|
"klt": self.gBar.kltbar,
|
||
|
|
"kht": self.gBar.khtbar,
|
||
|
|
"ihvcn": 0.0,
|
||
|
|
"leak": self.gBar.leakbar,
|
||
|
|
},
|
||
|
|
"initseg": {
|
||
|
|
"nacn": self.gBar.nabar,
|
||
|
|
"klt": self.gBar.kltbar,
|
||
|
|
"kht": self.gBar.khtbar,
|
||
|
|
"ihvcn": self.gBar.ihbar / 2.0,
|
||
|
|
"leak": self.gBar.leakbar,
|
||
|
|
},
|
||
|
|
"soma": {
|
||
|
|
"nacn": self.gBar.nabar,
|
||
|
|
"klt": self.gBar.kltbar,
|
||
|
|
"kht": self.gBar.khtbar,
|
||
|
|
"ihvcn": self.gBar.ihbar,
|
||
|
|
"leak": self.gBar.leakbar,
|
||
|
|
},
|
||
|
|
"dend": {
|
||
|
|
"nacn": self.gBar.nabar,
|
||
|
|
"klt": self.gBar.kltbar * 0.5,
|
||
|
|
"kht": self.gBar.khtbar * 0.5,
|
||
|
|
"ihvcn": self.gBar.ihbar / 3.0,
|
||
|
|
"leak": self.gBar.leakbar * 0.5,
|
||
|
|
},
|
||
|
|
"apic": {
|
||
|
|
"nacn": self.gBar.nabar,
|
||
|
|
"klt": self.gBar.kltbar * 0.2,
|
||
|
|
"kht": self.gBar.khtbar * 0.2,
|
||
|
|
"ihvcn": self.gBar.ihbar / 4.0,
|
||
|
|
"leak": self.gBar.leakbar * 0.2,
|
||
|
|
},
|
||
|
|
}
|
||
|
|
# self.irange = np.linspace(-1., 1., 21)
|
||
|
|
self.distMap = {
|
||
|
|
"dend": {
|
||
|
|
"klt": {"gradient": "linear", "gminf": 0.0, "lambda": 100.0},
|
||
|
|
"kht": {"gradient": "linear", "gminf": 0.0, "lambda": 100.0},
|
||
|
|
"nacn": {"gradient": "exp", "gminf": 0.0, "lambda": 100.0},
|
||
|
|
}, # linear with distance, gminf (factor) is multiplied by gbar
|
||
|
|
"apic": {
|
||
|
|
"klt": {"gradient": "linear", "gminf": 0.0, "lambda": 100.0},
|
||
|
|
"kht": {"gradient": "linear", "gminf": 0.0, "lambda": 100.0},
|
||
|
|
"nacn": {"gradient": "exp", "gminf": 0.0, "lambda": 100.0},
|
||
|
|
}, # gradients are: flat, linear, exponential
|
||
|
|
}
|
||
|
|
|
||
|
|
else:
|
||
|
|
raise ValueError("model type %s is not implemented" % modelType)
|
||
|
|
self.check_temperature()
|
||
|
|
|
||
|
|
def adjust_na_chans(self, soma, sf=1.0, gbar=1000.0, debug=False):
|
||
|
|
"""
|
||
|
|
adjust the sodium channel conductance
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
soma : neuron section object
|
||
|
|
A soma object whose sodium channel complement will have its
|
||
|
|
conductances adjusted depending on the channel type
|
||
|
|
|
||
|
|
gbar : float (default: 1000.)
|
||
|
|
The maximal conductance for the sodium channel
|
||
|
|
|
||
|
|
debug : boolean (false):
|
||
|
|
Verbose printing
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
Nothing :
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
if self.status["ttx"]:
|
||
|
|
gnabar = 0.0
|
||
|
|
else:
|
||
|
|
gnabar = nstomho(gbar, self.somaarea) * sf
|
||
|
|
nach = self.status["na"]
|
||
|
|
if nach == "jsrna":
|
||
|
|
soma().jsrna.gbar = gnabar
|
||
|
|
soma.ena = self.e_na
|
||
|
|
if debug:
|
||
|
|
print("jsrna gbar: ", soma().jsrna.gbar)
|
||
|
|
elif nach == "nav11":
|
||
|
|
soma().nav11.gbar = gnabar
|
||
|
|
soma.ena = 50 # self.e_na
|
||
|
|
# print('gnabar: ', soma().nav11.gbar, ' vs: 0.0192307692308')
|
||
|
|
soma().nav11.vsna = 4.3
|
||
|
|
if debug:
|
||
|
|
print("MSO using inva11")
|
||
|
|
elif nach in ["na", "nacn"]:
|
||
|
|
soma().na.gbar = gnabar
|
||
|
|
soma.ena = self.e_na
|
||
|
|
if debug:
|
||
|
|
print("na gbar: ", soma().na.gbar)
|
||
|
|
else:
|
||
|
|
raise ValueError("Sodium channel %s is not recognized for MSO cells", nach)
|