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620 lines
23 KiB
620 lines
23 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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import numpy as np
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# import neuron as nrn
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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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from .. import data
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__all__ = ["Tuberculoventral"]
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class Tuberculoventral(Cell):
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type = "tuberculoventral"
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@classmethod
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def create(cls, model="TVmouse", **kwds):
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if model in ["TVmouse", "I"]:
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return Tuberculoventral(**kwds)
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elif model == "dummy":
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return DummyTuberculoventral(**kwds)
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else:
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raise ValueError("Tuberculoventral type %s is unknown", model)
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def __init__(self):
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Cell.__init__(self)
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self.spike_source = (
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None
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) # used by DummyTuberculoventral to connect VecStim to terminal
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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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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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if terminal.cell.type in ["sgc", "dstellate", "tuberculoventral"]:
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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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elif psd_type == "multisite":
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if terminal.cell.type == "sgc":
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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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"sgc_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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"sgc_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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"sgc_synapse", species=self.species, post_type=self.type, 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:
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self.AMPA_gmax = (
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self.AMPA_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.NMDA_gmax = self.NMDA_gmax * kwds["NMDAScale"]
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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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elif terminal.cell.type == "dstellate": # WBI input -Voigt, Nelken, Young
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return self.make_gly_psd(post_sec, terminal, psdtype="glyfast", loc=loc)
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elif (
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terminal.cell.type == "tuberculoventral"
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): # TV cells talk to each other-Kuo et al.
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return self.make_gly_psd(post_sec, terminal, psdtype="glyfast", loc=loc)
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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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def make_terminal(self, post_cell, term_type, **kwds):
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pre_sec = self.soma
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if term_type == "simple":
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return synapses.SimpleTerminal(
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pre_sec, post_cell, spike_source=self.spike_source, **kwds
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)
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elif term_type == "multisite":
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if post_cell.type in [
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"dstellate",
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"tuberculoventral",
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"pyramidal",
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"bushy",
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"tstellate",
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]:
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nzones = data.get(
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"tuberculoventral_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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"tuberculoventral_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 tuberculoventral 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 Tuberculoventral(Tuberculoventral):
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"""
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Tuberculoventral Neuron (DCN) base model
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Adapted from T-stellate model, using target parameters from Kuo et al. J. Neurophys. 2012
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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="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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Initialize a DCN Tuberculoventral cell, using the default parameters for guinea pig from
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R&M2003, as a type I cell.
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Modifications to the cell can be made by calling methods below. These include:
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Converting to a type IA model (add transient K current) (species: guineapig-TypeIA).
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Changing "species" to mouse or cat (scales conductances)
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Parameters
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----------
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morphology : string (default: None)
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a file name to read the cell morphology from. If a valid file is found, a cell is constructed
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as a cable model from the hoc file.
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If None (default), the only a point 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 aer 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: 'na')
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nach selects the type of sodium channel that will be used in the model. A channel mechanims
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by that name must exist.
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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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Currently, this is not implemented.
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species: string (default 'guineapig')
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species defines the channel density that will be inserted for different models. Note that
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if a decorator function is specified, this argument is ignored.
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modelType: string (default: None)
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modelType specifies the type of the 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 models.
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debug: boolean (default: False)
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debug is a boolean flag. When set, 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(Tuberculoventral, self).__init__()
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if modelType == None:
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modelType = "TVmouse"
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if nach == None:
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nach = "nacncoop"
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self.debug = debug
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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": "Tuberculoventral",
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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": [(-0.35, 1.0, 0.05), (-0.04, 0.01, 0.01)]}
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self.vrange = [-80.0, -60.0] # 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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if self.debug:
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print("<< Tuberculoventral model: Creating point cell >>")
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soma = h.Section(
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name="Tuberculoventral_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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if self.debug:
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print("<< Tuberculoventral model: Creating structured cell >>")
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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 = ["kht", "ka", "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.species_scaling(
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silent=True, species=species, modelType=modelType
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) # adjust the default 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 self.debug:
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print("<< Tuberculoventral cell model created >>")
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def get_cellpars(self, dataset, species="mouse", celltype="TVmouse"):
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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(soma_cap=cellcap, soma_na_type=chtype)
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for g in [
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"soma_nacncoop_gbar",
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"soma_kht_gbar",
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"soma_ka_gbar",
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"soma_ihvcn_gbar",
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"soma_ihvcn_eh",
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"soma_leak_gbar",
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"soma_leak_erev",
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"soma_e_k",
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"soma_e_na",
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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, species="guineapig", modelType="TVmouse", 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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Used ONLY for point models.
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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: 'I-c')
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definition of model type from RM03 models, type I-c or type I-t
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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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soma = self.soma
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if self.debug:
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print("modelType: ", modelType)
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if modelType in ["TVmouse", "I"]:
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celltype = "TVmouse" # modelType
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modelType = "TVmouse"
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else:
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raise ValueError(
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"Tuberuloventral: Model type %s not recognized" % modelType
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)
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if species == "mouse" and modelType in ["TVmouse", "I"]:
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"""#From Kuo 150 Mohm, 10 msec tau
|
||
|
|
Firing at 600 pA about 400 Hz
|
||
|
|
These values from brute_force runs, getting 380 Hz at 600 pA at 35C
|
||
|
|
Input resistance and vm is ok, time constnat is short
|
||
|
|
*** Rin: 168 tau: 7.8 v: -68.4
|
||
|
|
Attempts to get longer time constant - cannot keep rate up.
|
||
|
|
"""
|
||
|
|
# Adapted from TStellate model type I-c'
|
||
|
|
self.vrange = [-80.0, -58.0]
|
||
|
|
self._valid_temperatures = (34.0,)
|
||
|
|
if self.status["temperature"] is None:
|
||
|
|
self.set_temperature(34.0)
|
||
|
|
|
||
|
|
pars = self.get_cellpars("TV_channels", species="mouse", celltype=modelType)
|
||
|
|
self.set_soma_size_from_Cm(pars.soma_cap)
|
||
|
|
self.status["na"] = pars.soma_na_type
|
||
|
|
self.adjust_na_chans(soma, gbar=pars.soma_nacncoop_gbar, debug=self.debug)
|
||
|
|
soma().kht.gbar = nstomho(pars.soma_kht_gbar, self.somaarea)
|
||
|
|
soma().ka.gbar = nstomho(pars.soma_ka_gbar, self.somaarea)
|
||
|
|
soma().ihvcn.gbar = nstomho(pars.soma_ihvcn_gbar, self.somaarea)
|
||
|
|
soma().ihvcn.eh = pars.soma_ihvcn_eh
|
||
|
|
soma().leak.gbar = nstomho(pars.soma_leak_gbar, self.somaarea)
|
||
|
|
soma().leak.erev = pars.soma_leak_erev
|
||
|
|
self.e_leak = pars.soma_leak_erev
|
||
|
|
self.soma.ek = self.e_k = pars.soma_e_k
|
||
|
|
self.soma.ena = self.e_na = pars.soma_e_na
|
||
|
|
|
||
|
|
self.axonsf = 0.5
|
||
|
|
else:
|
||
|
|
raise ValueError(
|
||
|
|
"Species %s or species-type %s is not recognized for Tuberculoventralcells"
|
||
|
|
% (species, type)
|
||
|
|
)
|
||
|
|
|
||
|
|
self.status["species"] = species
|
||
|
|
self.status["modelType"] = modelType
|
||
|
|
self.check_temperature()
|
||
|
|
|
||
|
|
def channel_manager(self, modelType="TVmouse"):
|
||
|
|
"""
|
||
|
|
This routine defines channel density maps and distance map patterns
|
||
|
|
for each type of compartment in the cell. The maps
|
||
|
|
are used by the ChannelDecorator class (specifically, it's private
|
||
|
|
_biophys function) to decorate the cell membrane.
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
modelType : string (default: 'RM03')
|
||
|
|
A string that defines the type of the model. Currently, 3 types are implemented:
|
||
|
|
RM03: Rothman and Manis, 2003 somatic densities for guinea pig
|
||
|
|
XM13: Xie and Manis, 2013, somatic densities for mouse
|
||
|
|
XM13PasDend: XM13, but with only passive dendrites, no channels.
|
||
|
|
|
||
|
|
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 (when testing)
|
||
|
|
- a distance map, which defines how selected conductances in selected compartments
|
||
|
|
will change with distance. This includes both linear and exponential gradients,
|
||
|
|
the minimum conductance at the end of the gradient, and the space constant or
|
||
|
|
slope for the gradient.
|
||
|
|
|
||
|
|
"""
|
||
|
|
if modelType == "TVmouse":
|
||
|
|
print("decorate as tvmouse")
|
||
|
|
# totcap = 95.0E-12 # Tuberculoventral cell (type I), based on stellate, adjusted for Kuo et al. TV firing
|
||
|
|
self.set_soma_size_from_Section(self.soma)
|
||
|
|
totcap = self.totcap
|
||
|
|
refarea = self.somaarea # totcap / self.c_m # see above for units
|
||
|
|
self.gBar = Params(
|
||
|
|
nabar=1520.0e-9 / refarea,
|
||
|
|
khtbar=160.0e-9 / refarea,
|
||
|
|
kltbar=0.0e-9 / refarea,
|
||
|
|
kabar=65.0 / refarea,
|
||
|
|
ihbar=1.25e-9 / refarea,
|
||
|
|
leakbar=5.5e-9 / refarea,
|
||
|
|
)
|
||
|
|
self.channelMap = {
|
||
|
|
"axon": {
|
||
|
|
"nacn": 0.0,
|
||
|
|
"klt": 0.0,
|
||
|
|
"kht": self.gBar.khtbar,
|
||
|
|
"ihvcn": 0.0,
|
||
|
|
"leak": self.gBar.leakbar / 4.0,
|
||
|
|
},
|
||
|
|
"hillock": {
|
||
|
|
"nacn": self.gBar.nabar,
|
||
|
|
"klt": 0.0,
|
||
|
|
"kht": self.gBar.khtbar,
|
||
|
|
"ihvcn": 0.0,
|
||
|
|
"leak": self.gBar.leakbar,
|
||
|
|
},
|
||
|
|
"initseg": {
|
||
|
|
"nacn": self.gBar.nabar,
|
||
|
|
"klt": 0.0,
|
||
|
|
"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 / 2.0,
|
||
|
|
"klt": 0.0,
|
||
|
|
"kht": self.gBar.khtbar * 0.5,
|
||
|
|
"ihvcn": self.gBar.ihbar / 3.0,
|
||
|
|
"leak": self.gBar.leakbar * 0.5,
|
||
|
|
},
|
||
|
|
"apic": {
|
||
|
|
"nacn": 0.0,
|
||
|
|
"klt": 0.0,
|
||
|
|
"kht": self.gBar.khtbar * 0.2,
|
||
|
|
"ihvcn": self.gBar.ihbar / 4.0,
|
||
|
|
"leak": self.gBar.leakbar * 0.2,
|
||
|
|
},
|
||
|
|
}
|
||
|
|
self.irange = np.linspace(-0.3, 0.6, 10)
|
||
|
|
self.distMap = {
|
||
|
|
"dend": {
|
||
|
|
"klt": {"gradient": "linear", "gminf": 0.0, "lambda": 100.0},
|
||
|
|
"kht": {"gradient": "linear", "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},
|
||
|
|
}, # gradients are: flat, linear, exponential
|
||
|
|
}
|
||
|
|
else:
|
||
|
|
raise ValueError("model type %s is not implemented" % modelType)
|
||
|
|
|
||
|
|
def adjust_na_chans(self, soma, gbar=1000.0, debug=False):
|
||
|
|
"""
|
||
|
|
Adjust the sodium channel conductance, depending on the type of conductance
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
soma : NEURON section object (required)
|
||
|
|
This identifies the soma object whose sodium channel complement will have it's
|
||
|
|
conductances adjusted depending on the sodium channel type
|
||
|
|
gbar : float (default: 1000.)
|
||
|
|
The "maximal" conductance to be set in the model.
|
||
|
|
debug : boolean (default: False)
|
||
|
|
A flag the prints out messages to confirm the operations applied.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
Nothing
|
||
|
|
"""
|
||
|
|
if self.status["ttx"]:
|
||
|
|
gnabar = 0.0
|
||
|
|
else:
|
||
|
|
gnabar = nstomho(gbar, self.somaarea)
|
||
|
|
nach = self.status["na"]
|
||
|
|
if nach == "nacncoop":
|
||
|
|
soma().nacncoop.gbar = gnabar
|
||
|
|
soma().nacncoop.KJ = 2000.0
|
||
|
|
soma().nacncoop.p = 0.25
|
||
|
|
soma.ena = self.e_na
|
||
|
|
if debug:
|
||
|
|
print("nacncoop gbar: ", soma().nacncoop.gbar)
|
||
|
|
elif 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 * 0.5
|
||
|
|
soma.ena = self.e_na
|
||
|
|
soma().nav11.vsna = 4.3
|
||
|
|
if debug:
|
||
|
|
print("Tuberculoventral using inva11")
|
||
|
|
print("nav11 gbar: ", soma().nav11.gbar)
|
||
|
|
elif nach == "na":
|
||
|
|
soma().na.gbar = gnabar
|
||
|
|
soma.ena = self.e_na
|
||
|
|
if debug:
|
||
|
|
print("na gbar: ", soma().na.gbar)
|
||
|
|
elif nach == "nacn":
|
||
|
|
soma().nacn.gbar = gnabar
|
||
|
|
soma.ena = self.e_na
|
||
|
|
if debug:
|
||
|
|
print("nacn gbar: ", soma().nacn.gbar)
|
||
|
|
else:
|
||
|
|
raise ValueError(
|
||
|
|
"Tuberculoventral setting Na channels: channel %s not known" % nach
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
class DummyTuberculoventral(Tuberculoventral):
|
||
|
|
""" Tuberculoventral cell class with no cell body; this cell only replays a predetermined
|
||
|
|
spike train. Useful for testing, or replacing spike trains to determine
|
||
|
|
the importance of spike structures within a network.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, cf=None, species="mouse"):
|
||
|
|
"""
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
cf : float (default: None)
|
||
|
|
Required: the characteristic frequency for the TV cell
|
||
|
|
Really just for reference.
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
Tuberculoventral.__init__(self)
|
||
|
|
self.vecstim = h.VecStim()
|
||
|
|
|
||
|
|
# this causes the terminal to receive events from the VecStim:
|
||
|
|
self.spike_source = self.vecstim
|
||
|
|
|
||
|
|
# just an empty section for holding the terminal
|
||
|
|
self.add_section(h.Section(), "soma")
|
||
|
|
self.status = {
|
||
|
|
"soma": True,
|
||
|
|
"axon": False,
|
||
|
|
"dendrites": False,
|
||
|
|
"pumps": False,
|
||
|
|
"na": None,
|
||
|
|
"species": species,
|
||
|
|
"modelType": "Dummy",
|
||
|
|
"modelName": "DummyTuberculoventral",
|
||
|
|
"ttx": None,
|
||
|
|
"name": "DummyTuberculoventral",
|
||
|
|
"morphology": None,
|
||
|
|
"decorator": None,
|
||
|
|
"temperature": None,
|
||
|
|
}
|
||
|
|
print("<< Tuberculoventral: Dummy Tuberculoventral Cell created >>")
|
||
|
|
|
||
|
|
def set_spiketrain(self, times):
|
||
|
|
""" Set the times of spikes (in seconds) to be replayed by the cell.
|
||
|
|
"""
|
||
|
|
self._spiketrain = times
|
||
|
|
self._stvec = h.Vector(times)
|
||
|
|
self.vecstim.play(self._stvec)
|