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compartment.py
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compartment.py
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"""
This module defines the classes for different types of compartments in a neuron
model.
The `Compartment` class is a base class that provides the basic functionality for
a single compartment. It handles all differential equations and parameters needed
to describe a single compartment and any currents passing through it.
The `Soma` and `Dendrite` classes inherit from the `Compartment` class and represent
specific types of compartments.
Classes:
Compartment: Represents a single compartment in a neuron model.
Soma: Represents the somatic compartment in a neuron model.
Dendrite: Represents a dendritic compartment in a neuron model.
"""
from __future__ import annotations
import pprint as pp
from typing import Optional, Union
import numpy as np
from brian2 import defaultclock
from brian2.core.functions import timestep
from brian2.units import Quantity, ms, pA
from .ephysproperties import EphysProperties
from .equations import library
from .utils import (DimensionlessCompartmentError, DuplicateEquationsError,
get_logger)
logger = get_logger(__name__)
class Compartment:
"""
A class that automatically generates and handles all differential
equations and parameters needed to describe a single compartment and
any currents (synaptic, dendritic, noise) passing through it.
Parameters
----------
name : str
A unique name used to tag compartment-specific equations and parameters.
It is also used to distinguish the various compartments belonging to the
same :class:`.NeuronModel`.
model : str, optional
A keyword for accessing Dendrify's library models. Custom models can
also be provided but they should be in the same formattable structure as
the library models. Available options: ``'passive'`` (default),
``'adaptiveIF'``, ``'leakyIF'``, ``'adex'``.
length : ~brian2.units.fundamentalunits.Quantity, optional
A compartment's length.
diameter : ~brian2.units.fundamentalunits.Quantity, optional
A compartment's diameter.
cm : ~brian2.units.fundamentalunits.Quantity, optional
Specific capacitance (usually μF / cm^2).
gl : ~brian2.units.fundamentalunits.Quantity, optional
Specific leakage conductance (usually μS / cm^2).
cm_abs : ~brian2.units.fundamentalunits.Quantity, optional
Absolute capacitance (usually pF).
gl_abs : ~brian2.units.fundamentalunits.Quantity, optional
Absolute leakage conductance (usually nS).
r_axial : ~brian2.units.fundamentalunits.Quantity, optional
Axial resistance (usually Ohm * cm).
v_rest : ~brian2.units.fundamentalunits.Quantity, optional
Resting membrane voltage.
scale_factor : float, optional
A global area scale factor, by default ``1.0``.
spine_factor : float, optional
A dendritic area scale factor to account for spines, by default ``1.0``.
Examples
--------
>>> # specifying equations only:
>>> compX = Compartment('nameX', 'leakyIF')
>>> # specifying equations and ephys properties:
>>> compY = Compartment('nameY', 'adaptiveIF', length=100*um, diameter=1*um,
>>> cm=1*uF/(cm**2), gl=50*uS/(cm**2))
>>> # specifying equations and absolute ephys properties:
>>> compY = Compartment('nameZ', 'adaptiveIF', cm_abs=100*pF, gl_abs=20*nS)
"""
def __init__(
self,
name: str,
model: str = 'passive',
length: Optional[Quantity] = None,
diameter: Optional[Quantity] = None,
cm: Optional[Quantity] = None,
gl: Optional[Quantity] = None,
cm_abs: Optional[Quantity] = None,
gl_abs: Optional[Quantity] = None,
r_axial: Optional[Quantity] = None,
v_rest: Optional[Quantity] = None,
scale_factor: Optional[float] = 1.0,
spine_factor: Optional[float] = 1.0
):
self.name = name
self._equations = None
self._params = None
self._connections = None
self._synapses = None
# Add membrane equations:
self._add_equations(model)
# Keep track of electrophysiological properties:
self._ephys_object = EphysProperties(
name=self.name,
length=length,
diameter=diameter,
cm=cm,
gl=gl,
cm_abs=cm_abs,
gl_abs=gl_abs,
r_axial=r_axial,
v_rest=v_rest,
scale_factor=scale_factor,
spine_factor=spine_factor
)
def __str__(self):
equations = self.equations
parameters = pp.pformat(self.parameters)
user = pp.pformat(self._ephys_object.__dict__)
txt = (f"\nOBJECT\n{6*'-'}\n{self.__class__}\n\n\n"
f"EQUATIONS\n{9*'-'}\n{equations}\n\n\n"
f"PARAMETERS\n{10*'-'}\n{parameters}\n\n\n"
f"USER PARAMETERS\n{15*'-'}\n{user}")
return txt
def _add_equations(self, model: str):
"""
Adds equations to a compartment.
Parameters
----------
model : str
"""
# Pick a model template or provide a custom model:
if model in library:
self._equations = library[model].format('_'+self.name)
else:
logger.warning(("The model you provided is not found. The default "
"'passive' membrane model will be used instead."))
self._equations = library['passive'].format('_'+self.name)
def connect(self,
other: Compartment,
g: Union[Quantity, str] = 'half_cylinders'):
"""
Connects two compartments (electrical coupling).
Parameters
----------
other : Compartment
Another compartment.
g : str or :class:`~brian2.units.fundamentalunits.Quantity`, optional
The coupling conductance. It can be set explicitly or calculated
automatically (provided all necessary parameters exist).
Available options: ``'half_cylinders'`` (default),
``'cylinder_<compartment name>'``.
Warning
-------
The automatic approaches require that both compartments to be connected
have specified **length**, **diameter** and **axial resistance**.
Examples
--------
>>> compX, compY = Compartment('x', **kwargs), Compartment('y', **kwargs)
>>> # explicit approach:
>>> compX.connect(compY, g=10*nS)
>>> # half cylinders (default):
>>> compX.connect(compY)
>>> # cylinder of one compartment:
>>> compX.connect(compY, g='cylinder_x')
"""
# Prohibit connecting compartments with the same name
if self.name == other.name:
raise ValueError(
"Cannot connect compartments with the same name.\n")
if (self.dimensionless or other.dimensionless) and isinstance(g, str):
raise DimensionlessCompartmentError(
("Cannot automatically calculate the coupling \nconductance of "
"dimensionless compartments. To resolve this error, perform\n"
"one of the following:\n\n"
f"1. Provide [length, diameter, r_axial] for both '{self.name}'"
f" and '{other.name}'.\n\n"
f"2. Turn both compartment into dimensionless by providing only"
" values for \n [cm_abs, gl_abs] and then connect them using "
"an exact coupling conductance."
)
)
# Current from Comp2 -> Comp1
forward_current = 'I_{1}_{0} = (V_{1}-V_{0}) * g_{1}_{0} :amp'.format(
self.name, other.name)
# Current from Comp1 -> Comp2
backward_current = 'I_{0}_{1} = (V_{0}-V_{1}) * g_{0}_{1} :amp'.format(
self.name, other.name)
# Add them to their respective compartments:
self._equations += '\n'+forward_current
other._equations += '\n'+backward_current
# Include them to the I variable (I_ext -> Inj + new_current):
self_change = f'= I_ext_{self.name}'
other_change = f'= I_ext_{other.name}'
self._equations = self._equations.replace(
self_change, self_change + ' + ' + forward_current.split('=')[0])
other._equations = other._equations.replace(
other_change, other_change + ' + ' + backward_current.split('=')[0])
# add them to connected comps
if not self._connections:
self._connections = []
if not other._connections:
other._connections = []
g_to_self = f'g_{other.name}_{self.name}'
g_to_other = f'g_{self.name}_{other.name}'
# when g is specified by user
if isinstance(g, Quantity):
self._connections.append((g_to_self, 'user', g))
other._connections.append((g_to_other, 'user', g))
# when g is a string
elif isinstance(g, str):
if g == 'half_cylinders':
self._connections.append((g_to_self, g, other._ephys_object))
other._connections.append((g_to_other, g, self._ephys_object))
elif g.split('_')[0] == "cylinder":
ctype, name = g.split('_')
comp = self if self.name == name else other
self._connections.append(
(g_to_self, ctype, comp._ephys_object))
other._connections.append(
(g_to_other, ctype, comp._ephys_object))
else:
raise ValueError(
"Please provide a valid conductance option."
)
def synapse(self, channel: str,
tag: str,
g: Optional[Quantity] = None,
t_rise: Optional[Quantity] = None,
t_decay: Optional[Quantity] = None,
scale_g: bool = False):
"""
Adds synaptic currents equations and parameters. When only the decay
time constant ``t_decay`` is provided, the synaptic model assumes an
instantaneous rise of the synaptic conductance followed by an exponential
decay. When both the rise ``t_rise`` and decay ``t_decay`` constants are
provided, synapses are modelled as a sum of two exponentials. For more
information see:
`Modeling Synapses by Arnd Roth & Mark C. W. van Rossum
<https://doi.org/10.7551/mitpress/9780262013277.003.0007>`_
Parameters
----------
channel : str
Synaptic channel type. Available options: ``'AMPA'``, ``'NMDA'``,
``'GABA'``.
tag : str
A unique name to distinguish synapses of the same type.
g : :class:`~brian2.units.fundamentalunits.Quantity`
Maximum synaptic conductance
t_rise : :class:`~brian2.units.fundamentalunits.Quantity`
Rise time constant
t_decay : :class:`~brian2.units.fundamentalunits.Quantity`
Decay time constant
scale_g : bool, optional
Option to add a normalization factor to scale the maximum
conductance at 1 when synapses are modelled as a difference of
exponentials (have both rise and decay kinetics), by default
``False``.
Examples
--------
>>> comp = Compartment('comp')
>>> # adding an AMPA synapse with instant rise & exponential decay:
>>> comp.synapse('AMPA', tag='X', g=1*nS, t_decay=5*ms)
>>> # same channel, different conductance & source:
>>> comp.synapse('AMPA', tag='Y', g=2*nS, t_decay=5*ms)
>>> # different channel with both rise & decay kinetics:
>>> comp.synapse('NMDA', tag='X' g=1*nS, t_rise=5*ms, t_decay=50*ms)
"""
synapse_id = "_".join([channel, tag, self.name])
if self._synapses:
# Check if this synapse already exists
if synapse_id in self._synapses:
raise DuplicateEquationsError(
f"The equations of '{channel}_{tag}' have already been "
f"added to '{self.name}'. \nPlease use a different "
f"combination of [channel, tag] when calling the synapse() "
"method \nmultiple times on a single compartment. You might"
" also see this error if you are using \nJupyter/iPython "
"which store variable values in memory. Try cleaning all "
"variables or \nrestart the kernel before running your "
"code. If this problem persists, please report it \n"
"by creating a new issue here: "
"https://github.com/Poirazi-Lab/dendrify/issues."
)
else:
self._synapses = []
# Switch to rise/decay equations if t_rise & t_decay are provided
key = f"{channel}_rd" if all([t_rise, t_decay]) else channel
current_name = f'I_{channel}_{tag}_{self.name}'
current_eqs = library[key].format(self.name, tag)
to_replace = f'= I_ext_{self.name}'
self._equations = self._equations.replace(
to_replace,
f'{to_replace} + {current_name}'
)
self._equations += '\n'+current_eqs
if not self._params:
self._params = {}
weight = f"w_{channel}_{tag}_{self.name}"
self._params[weight] = 1.0
# If user provides a value for g, then add it to _params
if g:
self._params[f'g_{channel}_{tag}_{self.name}'] = g
if t_rise:
self._params[f't_{channel}_rise_{tag}_{self.name}'] = t_rise
if t_decay:
self._params[f't_{channel}_decay_{tag}_{self.name}'] = t_decay
if scale_g:
if all([t_rise, t_decay, g]):
norm_factor = Compartment.g_norm_factor(t_rise, t_decay)
self._params[f'g_{channel}_{tag}_{self.name}'] *= norm_factor
self._synapses.append(synapse_id)
def noise(self, tau: Quantity = 20*ms, sigma: Quantity = 1*pA,
mean: Quantity = 0*pA):
"""
Adds a stochastic noise current. For more information see the Noise
section: of :doc:`brian2:user/models`
Parameters
----------
tau : :class:`~brian2.units.fundamentalunits.Quantity`, optional
Time constant of the Gaussian noise, by default ``20*ms``
sigma : :class:`~brian2.units.fundamentalunits.Quantity`, optional
Standard deviation of the Gaussian noise, by default ``3*pA``
mean : :class:`~brian2.units.fundamentalunits.Quantity`, optional
Mean of the Gaussian noise, by default ``0*pA``
"""
noise_current = f'I_noise_{self.name}'
if noise_current in self.equations:
raise DuplicateEquationsError(
f"The equations of '{noise_current}' have already been "
f"added to '{self.name}'. \nYou might be seeing this error if "
"you are using Jupyter/iPython "
"which store variable values \nin memory. Try cleaning all "
"variables or restart the kernel before running your "
"code. If this \nproblem persists, please report it "
"by creating a new issue here:\n"
"https://github.com/Poirazi-Lab/dendrify/issues."
)
noise_eqs = library['noise'].format(self.name)
to_change = f'= I_ext_{self.name}'
self._equations = self._equations.replace(
to_change,
f'{to_change} + {noise_current}'
)
self._equations += '\n'+noise_eqs
# Add _params:
if not self._params:
self._params = {}
self._params[f'tau_noise_{self.name}'] = tau
self._params[f'sigma_noise_{self.name}'] = sigma
self._params[f'mean_noise_{self.name}'] = mean
@property
def parameters(self) -> dict:
"""
Returns all the parameters that have been generated for a single
compartment.
Returns
-------
dict
"""
d_out = {}
for i in [self._params, self._g_couples]:
if i:
d_out.update(i)
if self._ephys_object:
d_out.update(self._ephys_object.parameters)
return d_out
@property
def area(self) -> Quantity:
"""
Returns a compartment's surface area (open cylinder) based on its length
and diameter.
Returns
-------
:class:`~brian2.units.fundamentalunits.Quantity`
"""
return self._ephys_object.area
@property
def capacitance(self) -> Quantity:
"""
Returns a compartment's absolute capacitance.
Returns
-------
:class:`~brian2.units.fundamentalunits.Quantity`
"""
return self._ephys_object.capacitance
@property
def g_leakage(self) -> Quantity:
"""
A compartment's absolute leakage conductance.
Returns
-------
:class:`~brian2.units.fundamentalunits.Quantity`
"""
return self._ephys_object.g_leakage
@property
def equations(self) -> str:
"""
Returns all differential equations that describe a single compartment
and the mechanisms that have been added to it.
Returns
-------
str
"""
return self._equations
@property
def _g_couples(self) -> Union[dict, None]:
# If not _connections have been specified yet
if not self._connections:
return None
d_out = {}
for i in self._connections:
# If ephys objects are not created yet
if not i[2]:
return None
name, ctype, helper_ephys = i[0], i[1], i[2]
if ctype == 'half_cylinders':
value = EphysProperties.g_couple(
self._ephys_object, helper_ephys)
elif ctype == 'cylinder':
value = helper_ephys.g_cylinder
elif ctype == 'user':
value = helper_ephys
d_out[name] = value
return d_out
@staticmethod
def g_norm_factor(t_rise: Quantity, t_decay: Quantity):
"""
Calculates the normalization factor for synaptic conductance with
t_rise and t_decay kinetics.
Parameters:
t_rise (Quantity): The rise time of the function.
t_decay (Quantity): The decay time of the function.
Returns:
float: The normalization factor for the g function.
"""
t_peak = (t_decay*t_rise / (t_decay-t_rise)) * np.log(t_decay/t_rise)
factor = (((t_decay*t_rise) / (t_decay-t_rise))
* (-np.exp(-t_peak/t_rise) + np.exp(-t_peak/t_decay))
/ ms)
return 1/factor
@property
def dimensionless(self) -> bool:
"""
Checks if a compartment has been flagged as dimensionless.
Returns
-------
bool
"""
return bool(self._ephys_object._dimensionless)
class Soma(Compartment):
"""
A class representing a somatic compartment in a neuron model.
This class automatically generates and handles all differential equations
and parameters needed to describe a somatic compartment and any currents
(synaptic, dendritic, noise) passing through it.
.. seealso::
Soma acts as a wrapper for Compartment with slight changes to account for
certain somatic properties. For a full list of its methods and attributes,
please see: :class:`.Compartment`.
Parameters
----------
name : str
A unique name used to tag compartment-specific equations and parameters.
It is also used to distinguish the various compartments belonging to the
same :class:`.NeuronModel`.
model : str, optional
A keyword for accessing Dendrify's library models. Custom models can
also be provided but they should be in the same formattable structure as
the library models. Available options: ``'leakyIF'`` (default),
``'adaptiveIF'``, ``'adex'``.
length : ~brian2.units.fundamentalunits.Quantity, optional
A compartment's length.
diameter : ~brian2.units.fundamentalunits.Quantity, optional
A compartment's diameter.
cm : ~brian2.units.fundamentalunits.Quantity, optional
Specific capacitance (usually μF / cm^2).
gl : ~brian2.units.fundamentalunits.Quantity, optional
Specific leakage conductance (usually μS / cm^2).
cm_abs : ~brian2.units.fundamentalunits.Quantity, optional
Absolute capacitance (usually pF).
gl_abs : ~brian2.units.fundamentalunits.Quantity, optional
Absolute leakage conductance (usually nS).
r_axial : ~brian2.units.fundamentalunits.Quantity, optional
Axial resistance (usually Ohm * cm).
v_rest : ~brian2.units.fundamentalunits.Quantity, optional
Resting membrane voltage.
scale_factor : float, optional
A global area scale factor, by default ``1.0``.
spine_factor : float, optional
A dendritic area scale factor to account for spines, by default ``1.0``.
Examples
--------
>>> # specifying equations only:
>>> compX = Soma('nameX', 'leakyIF')
>>> # specifying equations and ephys properties:
>>> compY = Soma('nameY', 'adaptiveIF', length=100*um, diameter=1*um,
>>> cm=1*uF/(cm**2), gl=50*uS/(cm**2))
>>> # specifying equations and absolute ephys properties:
>>> compY = Soma('nameZ', 'adaptiveIF', cm_abs=100*pF, gl_abs=20*nS)
"""
def __init__(
self,
name: str,
model: str = 'leakyIF',
length: Optional[Quantity] = None,
diameter: Optional[Quantity] = None,
cm: Optional[Quantity] = None,
gl: Optional[Quantity] = None,
cm_abs: Optional[Quantity] = None,
gl_abs: Optional[Quantity] = None,
r_axial: Optional[Quantity] = None,
v_rest: Optional[Quantity] = None,
scale_factor: Optional[float] = 1.0,
spine_factor: Optional[float] = 1.0
):
super().__init__(
name=name,
model=model,
length=length,
diameter=diameter,
cm=cm,
gl=gl,
cm_abs=cm_abs,
gl_abs=gl_abs,
r_axial=r_axial,
v_rest=v_rest,
scale_factor=scale_factor,
spine_factor=spine_factor
)
class Dendrite(Compartment):
"""
A class that automatically generates and handles all differential equations
and parameters needed to describe a dendritic compartment, its active
mechanisms, and any currents (synaptic, dendritic, ionic, noise) passing
through it.
.. seealso::
Dendrite inherits all the methods and attributes of its parent class
:class:`.Compartment`. For a complete list, please
refer to the documentation of the latter.
Parameters
----------
name : str
A unique name used to tag compartment-specific equations and parameters.
It is also used to distinguish the various compartments belonging to the
same :class:`.NeuronModel`.
model : str, optional
A keyword for accessing Dendrify's library models. Dendritic compartments
are by default set to ``'passive'``.
length : ~brian2.units.fundamentalunits.Quantity, optional
A compartment's length.
diameter : ~brian2.units.fundamentalunits.Quantity, optional
A compartment's diameter.
cm : ~brian2.units.fundamentalunits.Quantity, optional
Specific capacitance (usually μF / cm^2).
gl : ~brian2.units.fundamentalunits.Quantity, optional
Specific leakage conductance (usually μS / cm^2).
cm_abs : ~brian2.units.fundamentalunits.Quantity, optional
Absolute capacitance (usually pF).
gl_abs : ~brian2.units.fundamentalunits.Quantity, optional
Absolute leakage conductance (usually nS).
r_axial : ~brian2.units.fundamentalunits.Quantity, optional
Axial resistance (usually Ohm * cm).
v_rest : ~brian2.units.fundamentalunits.Quantity, optional
Resting membrane voltage.
scale_factor : float, optional
A global area scale factor, by default ``1.0``.
spine_factor : float, optional
A dendritic area scale factor to account for spines, by default ``1.0``.
Examples
--------
>>> # specifying equations only:
>>> compX = Dendrite('nameX')
>>> # specifying equations and ephys properties:
>>> compY = Dendrite('nameY', length=100*um, diameter=1*um,
>>> cm=1*uF/(cm**2), gl=50*uS/(cm**2))
>>> # specifying equations and absolute ephys properties:
>>> compY = Dendrite('nameZ', cm_abs=100*pF, gl_abs=20*nS)
"""
def __init__(
self,
name: str,
model: str = 'passive',
length: Optional[Quantity] = None,
diameter: Optional[Quantity] = None,
cm: Optional[Quantity] = None,
gl: Optional[Quantity] = None,
cm_abs: Optional[Quantity] = None,
gl_abs: Optional[Quantity] = None,
r_axial: Optional[Quantity] = None,
v_rest: Optional[Quantity] = None,
scale_factor: Optional[float] = 1.0,
spine_factor: Optional[float] = 1.0
):
super().__init__(
name=name,
model=model,
length=length,
diameter=diameter,
cm=cm,
gl=gl,
cm_abs=cm_abs,
gl_abs=gl_abs,
r_axial=r_axial,
v_rest=v_rest,
scale_factor=scale_factor,
spine_factor=spine_factor
)
self._events = None
self._event_actions = None
self._dspike_params = None
def __str__(self):
equations = self.equations
parameters = pp.pformat(self.parameters)
events = pp.pformat(self.events, width=120)
event_names = pp.pformat(self.event_names)
user = self._ephys_object.__dict__
user_clean = pp.pformat({k: v for k, v in user.items() if v})
txt = (f"\nOBJECT\n{6*'-'}\n{self.__class__}\n\n\n"
f"EQUATIONS\n{9*'-'}\n{equations}\n\n\n"
f"PARAMETERS\n{10*'-'}\n{parameters}\n\n\n"
f"EVENTS\n{6*'-'}\n{event_names}\n\n\n"
f"EVENT CONDITIONS\n{16*'-'}\n{events}\n\n\n"
f"USER PARAMETERS\n{15*'-'}\n{user_clean}")
return txt
def dspikes(self, name: str,
threshold: Optional[Quantity] = None,
g_rise: Optional[Quantity] = None,
g_fall: Optional[Quantity] = None,
duration_rise: Optional[Quantity] = None,
duration_fall: Optional[Quantity] = None,
reversal_rise: Union[Quantity, str, None] = None,
reversal_fall: Union[Quantity, str, None] = None,
offset_fall: Optional[Quantity] = None,
refractory: Optional[Quantity] = None
):
"""
Adds the ionic mechanisms and parameters needed for dendritic spiking.
Under the hood, this method creates the equations, conditions and
actions to take advantage of Brian's custom events. dSpikes are
generated through the sequential activation of a positive (sodium or
calcium-like) and a negative current (potassium-like current) when a
specified dSpike threshold is crossed.
.. hint::
The dendritic spiking mechanism as implemented here has three
distinct phases.
**INACTIVE PHASE:**\n
When the dendritic voltage is subthreshold OR the simulation step is
within the refractory period. dSpikes cannot be generated during this
phase.
**RISE PHASE:**\n
When the dendritic voltage crosses the dSpike threshold AND the
refractory period has elapsed. This triggers the instant activation
of a positive current that is deactivated after a specified amount
of time (``duration_rise``). Also a new refractory period begins.
**FALL PHASE:**\n
This phase starts automatically with a delay (``offset_fall``) after
the dSpike threshold is crossed. A negative current is activated
instantly and then is deactivated after a specified amount of time
(``duration_fall``).
Parameters
----------
name : str
A unique name to describe a single dSpike type.
threshold : ~brian2.units.fundamentalunits.Quantity, optional
The membrane voltage threshold for dendritic spiking.
g_rise : ~brian2.units.fundamentalunits.Quantity, optional
The max conductance of the channel that is activated during the rise
(depolarization phase).
g_fall : ~brian2.units.fundamentalunits.Quantity, optional
The max conductance of the channel that is activated during the fall
(repolarization phase).
duration_rise : ~brian2.units.fundamentalunits.Quantity, optional
The duration of g_rise staying open.
duration_fall : ~brian2.units.fundamentalunits.Quantity, optional
The duration of g_fall staying open.
reversal_rise : (~brian2.units.fundamentalunits.Quantity, str), optional
The reversal potential of the channel that is activated during the rise
(depolarization) phase.
reversal_fall : (~brian2.units.fundamentalunits.Quantity, str), optional
The reversal potential of the channel that is activated during the fall
(repolarization) phase.
offset_fall : ~brian2.units.fundamentalunits.Quantity, optional
The delay for the activation of g_rise.
refractory : ~brian2.units.fundamentalunits.Quantity, optional
The time interval required before dSpike can be activated again.
"""
# The following code creates all necessary equations for dspikes:
comp = self.name
event_id = f"{name}_{comp}"
event_name = f"spike_{event_id}"
if self._events:
# Check if this event already exists
if event_name in self._events:
raise DuplicateEquationsError(
f"The equations for '{event_name}' have already been "
f"added to '{self.name}'. \nPlease use a different "
f"[name] when adding multiple dSpike mechanisms to "
" a single compartment. \nYou might"
" also see this error if you are using Jupyter/iPython "
"which store variable values in \nmemory. Try cleaning all "
"variables or restart the kernel before running your "
"code. If this \nproblem persists, please report it "
"by creating a new issue here: \n"
"https://github.com/Poirazi-Lab/dendrify/issues."
)
else:
self._events = {}
dspike_currents = f"I_rise_{event_id} + I_fall_{event_id}"
# Both currents take into account the reversal potential of Na/K
current_rise_eqs = f"I_rise_{event_id} = g_rise_{event_id} * (E_rise_{name}-V_{comp}) :amp"
current_fall_eqs = f"I_fall_{event_id} = g_fall_{event_id} * (E_fall_{name}-V_{comp}) :amp"
# Ion conductances
g_rise_eqs = (
f"g_rise_{event_id} = "
f"g_rise_max_{event_id} * "
f"int(t_in_timesteps <= spiketime_{event_id} + duration_rise_{event_id}) * "
f"gate_{event_id} "
":siemens"
)
g_fall_eqs = (
f"g_fall_{event_id} = "
f"g_fall_max_{event_id} * "
f"int(t_in_timesteps <= spiketime_{event_id} + offset_fall_{event_id} + duration_fall_{event_id}) * "
f"int(t_in_timesteps >= spiketime_{event_id} + offset_fall_{event_id}) * "
f"gate_{event_id} "
":siemens"
)
spiketime = f'spiketime_{event_id} :1' # in units of timestep
gate = f'gate_{event_id} :1' # zero or one
# Add equations to a compartment
to_replace = f'= I_ext_{comp}'
self._equations = self._equations.replace(
to_replace,
f'{to_replace} + {dspike_currents}'
)
self._equations += '\n'.join(['', current_rise_eqs, current_fall_eqs,
g_rise_eqs, g_fall_eqs,
spiketime, gate]
)
# Create and add custom dspike event
event_name = f"spike_{event_id}"
condition = (f"V_{comp} >= Vth_{event_id} and "
f"t_in_timesteps >= spiketime_{event_id} + refractory_{event_id} * gate_{event_id}"
)
self._events[event_name] = condition
# Specify what is going to happen inside run_on_event()
action = {f"spike_{event_id}": f"spiketime_{event_id} = t_in_timesteps; gate_{event_id} = 1"}
if not self._event_actions:
self._event_actions = action
else:
self._event_actions.update(action)
# Include params needed
if not self._dspike_params:
self._dspike_params = {}
dt = defaultclock.dt
params = [
threshold,
g_rise,
g_fall,
self._ionic_param(reversal_rise),
self._ionic_param(reversal_fall),
self._timestep(duration_rise, dt),
self._timestep(duration_fall, dt),
self._timestep(offset_fall, dt),
self._timestep(refractory, dt)]
variables = [
f"Vth_{event_id}",
f"g_rise_max_{event_id}",
f"g_fall_max_{event_id}",
f"E_rise_{name}",
f"E_fall_{name}",
f"duration_rise_{event_id}",
f"duration_fall_{event_id}",
f"offset_fall_{event_id}",
f"refractory_{event_id}"]
d = dict(zip(variables, params))
self._dspike_params[event_id] = d
def _timestep(self,
param: Union[Quantity, None], dt
) -> Union[int, None]:
if not param:
return None
if isinstance(param, Quantity):
return timestep(param, dt)
raise ValueError(
f"Please provide a valid time parameter for '{self.name}'."
)
def _ionic_param(self,
param: Union[str, Quantity, None],
) -> Union[Quantity, None]:
default_params = EphysProperties.DEFAULT_PARAMS
valid_params = {k: v for k, v in default_params.items() if k[0] == 'E'}
if not param:
return None
if isinstance(param, Quantity):
return param
if isinstance(param, str):
try:
return default_params[param]
except KeyError:
raise ValueError(
f"Please provide a valid ionic parameter for '{self.name}'."
" Available options:\n"
f"{pp.pformat(valid_params)}"
)
else:
raise ValueError(
f"Please provide a valid ionic parameter for '{self.name}'."
" Available options:\n"
f"{pp.pformat(valid_params)}"
)
@property
def parameters(self) -> dict:
"""
Returns a dictionary of all parameters that have been generated for a
single compartment.
Returns
-------
dict
"""
d_out = {}
for i in [self._params, self._g_couples]:
if i:
d_out.update(i)
if self._dspike_params:
for d in self._dspike_params.values():
d_out.update(d)
if self._ephys_object:
d_out.update(self._ephys_object.parameters)
return d_out
@property
def events(self) -> dict:
"""
Returns a dictionary of all dSpike events created for a single dendrite.
Returns
-------
dict
Keys: event names, values: events conditions.
"""
return self._events if self._events else {}
@property
def event_names(self) -> list:
"""
Returns a list of all dSpike event names created for a single dendrite.
Returns
-------
list
"""
if not self._events:
return []
return list(self._events.keys())