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448 lines
16 KiB
448 lines
16 KiB
2 years ago
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from __future__ import print_function
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import numpy as np
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from neuron import h
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from .cell import Cell
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from .. import data
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from .. import synapses
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from ..util import Params
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from ..util import nstomho
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__all__ = ["Cartwheel", "CartwheelDefault"]
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class Cartwheel(Cell):
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type = "cartwheel"
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@classmethod
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def create(cls, model="CW", **kwds):
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if model == "CW":
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return CartwheelDefault(**kwds)
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else:
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raise ValueError("Carthweel model 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 to try to pass the default 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 bushy cell
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kwds: dict of options. Two are currently handled:
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postsize : 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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self.pre_sec = terminal.section
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pre_cell = terminal.cell
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post_sec = self.soma
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if psd_type == "simple":
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if terminal.cell.type in [
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"sgc",
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"dstellate",
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"tuberculoventral",
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"cartwheel",
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]:
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weight = data.get(
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"%s_synapse" % terminal.cell.type,
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species=self.species,
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post_type=self.type,
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field="weight",
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)
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tau1 = data.get(
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"%s_synapse" % terminal.cell.type,
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species=self.species,
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post_type=self.type,
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field="tau1",
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)
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tau2 = data.get(
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"%s_synapse" % terminal.cell.type,
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species=self.species,
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post_type=self.type,
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field="tau2",
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)
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erev = data.get(
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"%s_synapse" % terminal.cell.type,
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species=self.species,
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post_type=self.type,
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field="erev",
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)
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return self.make_exp2_psd(
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post_sec,
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terminal,
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weight=weight,
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loc=loc,
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tau1=tau1,
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tau2=tau2,
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erev=erev,
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)
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else:
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raise TypeError(
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"Cannot make simple PSD for %s => %s"
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% (terminal.cell.type, self.type)
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)
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else:
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raise ValueError(
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"Unsupported psd type %s for cartwheel cell (inputs not implemented yet)"
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% psd_type
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)
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def make_terminal(self, post_cell, term_type, **kwds):
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if term_type == "simple":
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return synapses.SimpleTerminal(self.soma, post_cell, **kwds)
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elif term_type == "multisite":
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if post_cell.type in ["tuberculoventral", "pyramidal"]:
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nzones = data.get(
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"cartwheel_synapse",
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species=self.species,
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post_type=post_cell.type,
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field="n_rsites",
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)
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delay = data.get(
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"cartwheel_synapse",
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species=self.species,
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post_type=post_cell.type,
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field="delay",
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)
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else:
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raise NotImplementedError(
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"No knowledge as to how to connect cartwheel cell to cell type %s"
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% type(post_cell)
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)
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pre_sec = self.soma
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return synapses.StochasticTerminal(
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pre_sec,
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post_cell,
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nzones=nzones,
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spike_source=self.spike_source,
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delay=delay,
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**kwds
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)
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else:
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raise ValueError("Unsupported terminal type %s" % term_type)
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class CartwheelDefault(Cartwheel, Cell):
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"""
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DCN cartwheel cell model.
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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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ttx=False,
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nach=None,
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species="mouse",
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modelType=None,
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debug=False,
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):
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"""
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Create cartwheel cell model, based on a Purkinje cell model from Raman.
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There are no variations available for this model.
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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 is naRsg, a resurgent sodium channel model.
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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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species: string (default 'rat')
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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.
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modelType is passed to the decorator, or to species_scaling to adjust point (single cylinder) models.
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Only type "I" is recognized for the cartwheel cell model.
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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(CartwheelDefault, self).__init__()
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if modelType == None:
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modelType = "I"
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if nach == None:
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nach = "naRsg"
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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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"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": "Cartwheel",
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"morphology": morphology,
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"decorator": decorator,
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"temperature": None,
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}
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self.i_test_range = {"pulse": (-0.2, 0.2, 0.02)}
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# self.spike_threshold = 0
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self.vrange = [-75.0, -52.0] # set a default vrange for searching for rmp
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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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soma = h.Section(
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name="Cartwheel_Soma_%x" % id(self)
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) # one compartment of about 29000 um2
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# cm = 1
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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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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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# v_potassium = -80 # potassium reversal potential
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# v_sodium = 50 # sodium reversal potential
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self.mechanisms = [
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"naRsg",
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"bkpkj",
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"hpkj",
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"kpkj",
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"kpkj2",
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"kpkjslow",
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"kpksk",
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"lkpkj",
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"cap",
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]
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for mech in self.mechanisms:
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self.soma.insert(mech)
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self.soma.insert("cadiff")
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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(
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"<< Cartwheel: Modified version of Raman Purkinje cell model created >>"
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)
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def get_cellpars(self, dataset, species="guineapig", celltype="II"):
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somaDia = data.get(
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dataset, species=species, cell_type=celltype, field="soma_Dia"
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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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pcabar = data.get(
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dataset, species=species, cell_type=celltype, field="soma_pcabar"
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)
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pars = Params(soma_Dia=somaDia, soma_natype=chtype, soma_pcabar=pcabar)
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for g in [
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"soma_narsg_gbar",
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"soma_kpkj_gbar",
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"soma_kpkj2_gbar",
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"soma_kpkjslow_gbar",
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"soma_kpksk_gbar",
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"soma_lkpkj_gbar",
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"soma_bkpkj_gbar",
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"soma_hpkj_gbar",
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"soma_hpkj_eh",
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"soma_lkpkj_e",
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"soma_e_k",
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"soma_e_na",
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"soma_e_ca",
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]:
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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, silent=True, species="mouse", modelType="I"):
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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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Parameters
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----------
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species : string (default: 'rat')
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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: 'I')
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definition of model type from RM03 models, type II or type II-I
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silent : boolean (default: True)
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run silently (True) or verbosely (False)
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Note
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----
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For the cartwheel cell model, there is only a single scaling recognized.
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"""
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if species is not "mouse":
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raise ValueError('Cartwheel species: only "mouse" is recognized')
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if modelType is not "I":
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raise ValueError('Cartwheel modelType: only "I" is recognized')
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self._valid_temperatures = (34.0,)
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if self.status["temperature"] is None:
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self.set_temperature(34.0)
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pars = self.get_cellpars("CW_channels", species=species, celltype="cartwheel")
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self.set_soma_size_from_Diam(pars.soma_Dia)
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self.soma().bkpkj.gbar = nstomho(pars.soma_bkpkj_gbar, self.somaarea)
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self.soma().hpkj.gbar = nstomho(pars.soma_hpkj_gbar, self.somaarea)
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self.soma().kpkj.gbar = nstomho(pars.soma_kpkj_gbar, self.somaarea)
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self.soma().kpkj2.gbar = nstomho(pars.soma_kpkj2_gbar, self.somaarea)
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self.soma().kpkjslow.gbar = nstomho(pars.soma_kpkjslow_gbar, self.somaarea)
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self.soma().kpksk.gbar = nstomho(pars.soma_kpksk_gbar, self.somaarea)
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self.soma().lkpkj.gbar = nstomho(pars.soma_lkpkj_gbar, self.somaarea)
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self.soma().naRsg.gbar = nstomho(pars.soma_narsg_gbar, self.somaarea)
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self.soma().cap.pcabar = pars.soma_pcabar
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self.soma().ena = pars.soma_e_na # 50
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self.soma().ek = pars.soma_e_k # -80
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self.soma().lkpkj.e = pars.soma_lkpkj_e # -65
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self.soma().hpkj.eh = pars.soma_hpkj_eh # -43
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self.soma().eca = pars.soma_e_ca # 50
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self.status["na"] = pars.soma_natype
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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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if not silent:
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print("set cell as: ", species)
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print(" with Vm rest = %f" % self.vm0)
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# print 'set up'
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def i_currents(self, V):
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"""
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For the steady-state case, return the total current at voltage V
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Used to find the zero current point.
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Overrides i_currents in cells.py, because this model uses conductances
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that are not specified in the default cell mode.
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Parameters
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----------
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V : float, mV (no default)
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Voltage at which the current for each conductance is computed.
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Returns
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-------
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I : float, nA
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The sum of the currents at steady-state for all of the conductances.
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"""
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for part in self.all_sections.keys():
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for sec in self.all_sections[part]:
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|
sec.v = V
|
||
|
|
h.celsius = self.status["temperature"]
|
||
|
|
h.finitialize()
|
||
|
|
self.ix = {}
|
||
|
|
|
||
|
|
if "naRsg" in self.mechanisms:
|
||
|
|
self.ix["naRsg"] = self.soma().naRsg.gna * (V - self.soma().ena)
|
||
|
|
if "cap" in self.mechanisms:
|
||
|
|
a = self.soma().cap.pcabar * self.soma().cap.minf
|
||
|
|
self.ix["cap"] = a * self.ghk(V, self.soma().cao, self.soma().cai, 2)
|
||
|
|
if "kpkj" in self.mechanisms:
|
||
|
|
self.ix["kpkj"] = self.soma().kpkj.gk * (V - self.soma().ek)
|
||
|
|
if "kpkj2" in self.mechanisms:
|
||
|
|
self.ix["kpkj2"] = self.soma().kpkj2.gk * (V - self.soma().ek)
|
||
|
|
if "kpkjslow" in self.mechanisms:
|
||
|
|
self.ix["kpkjslow"] = self.soma().kpkjslow.gk * (V - self.soma().ek)
|
||
|
|
if "kpksk" in self.mechanisms:
|
||
|
|
self.ix["kpksk"] = self.soma().kpksk.gk * (V - self.soma().ek)
|
||
|
|
if "bkpkj" in self.mechanisms:
|
||
|
|
self.ix["bkpkj"] = self.soma().bkpkj.gbkpkj * (V - self.soma().ek)
|
||
|
|
if "hpkj" in self.mechanisms:
|
||
|
|
self.ix["hpkj"] = self.soma().hpkj.gh * (V - self.soma().hpkj.eh)
|
||
|
|
# leak
|
||
|
|
if "lkpkj" in self.mechanisms:
|
||
|
|
self.ix["lkpkj"] = self.soma().lkpkj.gbar * (V - self.soma().lkpkj.e)
|
||
|
|
return np.sum([self.ix[i] for i in self.ix])
|
||
|
|
|
||
|
|
def ghk(self, v, ci, co, z):
|
||
|
|
"""
|
||
|
|
GHK flux equation, used to calculate current density through calcium channels
|
||
|
|
rather than standard Nernst equation.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
v : float, mV
|
||
|
|
voltage for GHK calculation
|
||
|
|
ci : float, mM
|
||
|
|
internal ion concentration
|
||
|
|
co : float, mM
|
||
|
|
external ion concentraion
|
||
|
|
z : float, no units
|
||
|
|
valence
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
flux : A/m^2
|
||
|
|
|
||
|
|
"""
|
||
|
|
F = 9.6485e4 # (coul)
|
||
|
|
R = 8.3145 # (joule/degC)
|
||
|
|
T = h.celsius + 273.19 # Kelvin
|
||
|
|
E = (1e-3) * v # convert mV to V
|
||
|
|
Ci = ci + (self.soma().cap.monovalPerm) * (
|
||
|
|
self.soma().cap.monovalConc
|
||
|
|
) # : Monovalent permeability
|
||
|
|
if (
|
||
|
|
np.fabs(1 - np.exp(-z * (F * E) / (R * T))) < 1e-6
|
||
|
|
): # denominator is small -> Taylor series
|
||
|
|
ghk = (
|
||
|
|
(1e-6)
|
||
|
|
* z
|
||
|
|
* F
|
||
|
|
* (Ci - co * np.exp(-z * (F * E) / (R * T)))
|
||
|
|
* (1 - (z * (F * E) / (R * T)))
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
ghk = (
|
||
|
|
(1e-6)
|
||
|
|
* z ** 2.0
|
||
|
|
* (E * F ** 2.0)
|
||
|
|
/ (R * T)
|
||
|
|
* (Ci - co * np.exp(-z * (F * E) / (R * T)))
|
||
|
|
/ (1 - np.exp(-z * (F * E) / (R * T)))
|
||
|
|
)
|
||
|
|
return ghk
|