Source code for brainpy.dynold.synouts.conductances

# -*- coding: utf-8 -*-
# Copyright 2025 BrainX Ecosystem Limited. All Rights Reserved.
#
# 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
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
from typing import Union, Callable, Optional

from brainpy.dynold.synapses.base import _SynOut
from brainpy.initialize import parameter, Initializer
from brainpy.math import Variable
from brainpy.types import ArrayType

__all__ = [
    'COBA',
    'CUBA',
]




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.


    See Also::

    COBA
    """



    def __init__(
        self,
        target_var: Optional[Union[str, Variable]] = 'input',
        name: str = None,
    ):
        self._target_var = target_var
        super().__init__(name=name, target_var=target_var)


    def clone(self):
        return CUBA(target_var=self._target_var)





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, callable, Initializer
      The reversal potential.
    name: str
      The model name.

    See Also::

    CUBA
    """



    def __init__(
        self,
        E: Union[float, ArrayType, Callable, Initializer] = 0.,
        target_var: Optional[Union[str, Variable]] = 'input',
        membrane_var: Union[str, Variable] = 'V',
        name: str = None,
    ):
        super().__init__(name=name, target_var=target_var)
        self._E = E
        self._target_var = target_var
        self._membrane_var = membrane_var


    def clone(self):
        return COBA(E=self._E,
                    target_var=self._target_var,
                    membrane_var=self._membrane_var)

    def register_master(self, master):
        super().register_master(master)

        # reversal potential
        self.E = parameter(self._E, self.master.post.num, allow_none=False)

        # membrane potential
        if isinstance(self._membrane_var, str):
            if not hasattr(self.master.post, self._membrane_var):
                raise KeyError(f'Post-synaptic group does not have membrane variable: {self._membrane_var}')
            self.membrane_var = getattr(self.master.post, self._membrane_var)
        elif isinstance(self._membrane_var, Variable):
            self.membrane_var = self._membrane_var
        else:
            raise TypeError('"membrane_var" must be instance of string or Variable. '
                            f'But we got {type(self._membrane_var)}')

    def filter(self, g):
        V = self.membrane_var.value
        I = g * (self.E - V)
        return super(COBA, self).filter(I)