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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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# ==============================================================================
from typing import Union, Optional, Sequence
import numpy as np
from brainpy import math as bm, initialize as init
from brainpy.types import ArrayType
from .base import SynOut
__all__ = [
'COBA',
'CUBA',
'MgBlock'
]
[docs]
class COBA(SynOut):
r"""Conductance-based synaptic output.
Given the synaptic conductance, the model output the post-synaptic current with
.. math::
I_{syn}(t) = g_{\mathrm{syn}}(t) (E - V(t))
Parameters::
E: float, ArrayType, ndarray
The reversal potential.
sharding: sequence of str
The axis names for variable for parallelization.
name: str
The model name.
scaling: brainpy.Scaling
The scaling object.
See Also::
CUBA
"""
def __init__(
self,
E: Union[float, ArrayType],
sharding: Optional[Sequence[str]] = None,
name: Optional[str] = None,
scaling: Optional[bm.Scaling] = None,
):
super().__init__(name=name, scaling=scaling)
self.sharding = sharding
self.E = self.offset_scaling(init.parameter(E, np.shape(E), sharding=sharding))
[docs]
def update(self, conductance, potential):
return conductance * (self.E - potential)
[docs]
class CUBA(SynOut):
r"""Current-based synaptic output.
Given the conductance, this model outputs the post-synaptic current with a identity function:
.. math::
I_{\mathrm{syn}}(t) = g_{\mathrm{syn}}(t)
Parameters::
name: str
The model name.
scaling: brainpy.Scaling
The scaling object.
See Also::
COBA
"""
def __init__(
self,
name: Optional[str] = None,
scaling: Optional[bm.Scaling] = None,
):
super().__init__(name=name, scaling=scaling)
[docs]
def update(self, conductance, potential=None):
return conductance
[docs]
class MgBlock(SynOut):
r"""Synaptic output based on Magnesium blocking.
Given the synaptic conductance, the model output the post-synaptic current with
.. math::
I_{syn}(t) = g_{\mathrm{syn}}(t) (E - V(t)) g_{\infty}(V,[{Mg}^{2+}]_{o})
where The fraction of channels :math:`g_{\infty}` that are not blocked by magnesium can be fitted to
.. math::
g_{\infty}(V,[{Mg}^{2+}]_{o}) = (1+{e}^{-\alpha V} \frac{[{Mg}^{2+}]_{o}} {\beta})^{-1}
Here :math:`[{Mg}^{2+}]_{o}` is the extracellular magnesium concentration.
Parameters::
E: float, ArrayType
The reversal potential for the synaptic current. [mV]
alpha: float, ArrayType
Binding constant. Default 0.062
beta: float, ArrayType
Unbinding constant. Default 3.57
cc_Mg: float, ArrayType
Concentration of Magnesium ion. Default 1.2 [mM].
sharding: sequence of str
The axis names for variable for parallelization.
name: str
The model name.
"""
def __init__(
self,
E: Union[float, ArrayType] = 0.,
cc_Mg: Union[float, ArrayType] = 1.2,
alpha: Union[float, ArrayType] = 0.062,
beta: Union[float, ArrayType] = 3.57,
V_offset: Union[float, ArrayType] = 0.,
sharding: Optional[Sequence[str]] = None,
name: Optional[str] = None,
scaling: Optional[bm.Scaling] = None,
):
super().__init__(name=name, scaling=scaling)
self.sharding = sharding
self.E = self.offset_scaling(init.parameter(E, np.shape(E), sharding=sharding))
self.V_offset = self.offset_scaling(init.parameter(V_offset, np.shape(V_offset), sharding=sharding))
self.cc_Mg = init.parameter(cc_Mg, np.shape(cc_Mg), sharding=sharding)
self.alpha = self.inv_scaling(init.parameter(alpha, np.shape(alpha), sharding=sharding))
self.beta = init.parameter(beta, np.shape(beta), sharding=sharding)
[docs]
def update(self, conductance, potential):
norm = (1 + self.cc_Mg / self.beta * bm.exp(self.alpha * (self.V_offset - potential)))
return conductance * (self.E - potential) / norm
