update state neuron and synapse (#794)

* Update version to 2.7.1 and refactor Dynamics class: enhance input handling and add new properties

* Update version to 2.7.1 and refactor Dynamics class: enhance input handling and add new properties

* Add state version implementations for neuron and synapse models

This commit implements state versions of several neuron and synapse models
using the brainstate framework, following BrainPy v2.7+ architecture.

Neuron Models Added:
- LIF (Leaky Integrate-and-Fire) neurons with multiple variants:
  * LIF: Basic LIF neuron with exponential synaptic input
  * LifRef: LIF with refractory period
  * ExpIF: Exponential Integrate-and-Fire
  * ExpIFRef: ExpIF with refractory period
  * AdExIF: Adaptive Exponential Integrate-and-Fire
  * AdExIFRef: AdExIF with refractory period
  * QuaIF: Quadratic Integrate-and-Fire
  * QuaIFRef: QuaIF with refractory period
  * AdQuaIF: Adaptive Quadratic Integrate-and-Fire
  * AdQuaIFRef: AdQuaIF with refractory period
  * GifRef: Generalized Integrate-and-Fire with refractory

- Izhikevich neuron model with variants:
  * Izhikevich: Basic Izhikevich neuron
  * IzhikevichRef: With refractory period

- Hodgkin-Huxley (HH) neuron model:
  * HH: Classic Hodgkin-Huxley model with Na+ and K+ channels

Synapse Models Added:
- BioNMDA: Biological NMDA receptor with second-order kinetics
  * Implements two-state cascade dynamics (x and g variables)
  * Slower rise time compared to AMPA (biologically realistic)
  * Comprehensive documentation with mathematical formulation

Testing:
- Comprehensive test suites added for all models
- AMPA and GABAa synapse tests added
- All tests passing with proper unit handling

Key Features:
- Uses brainstate ecosystem (HiddenState, ShortTermState, LongTermState)
- Proper unit support with brainunit
- Exponential Euler integration for numerical stability
- Batch processing support
- Consistent API design across all models

Files Modified:
- brainpy/state/_lif.py: Added LIF variants
- brainpy/state/_izhikevich.py: Added Izhikevich variants (new file)
- brainpy/state/_hh.py: Added HH model (new file)
- brainpy/state/_synapse.py: Added BioNMDA model
- brainpy/state/_synapse_test.py: Added comprehensive tests
- brainpy/state/_lif_test.py: Added LIF tests
- brainpy/state/__init__.py: Updated exports
- brainpy/dyn/neurons/lif.py: Minor documentation updates

* Add Examples sections to neuron classes in _lif.py

Added comprehensive Examples sections to 5 neuron classes that were
missing them:
- QuaIF: Quadratic Integrate-and-Fire neuron
- AdQuaIF: Adaptive Quadratic Integrate-and-Fire neuron
- AdQuaIFRef: AdQuaIF with refractory period
- Gif: Generalized Integrate-and-Fire neuron
- GifRef: Gif with refractory period

Each Examples section includes:
- Import statements for required modules
- Basic usage with parameter specifications
- State initialization examples
- Update and spike generation examples
- Network integration with brainstate.nn.Sequential
- Additional Notes highlighting key features

All 13 neuron classes in _lif.py now have complete documentation
with Examples sections following consistent format and style.

* Refactor module assignments to 'brainpy.state' for consistency across files

* Update import paths in _base.py: change references from brainstate to brainpy for consistency

* fix tests

* Update import paths in _inputs.py: change references from brainpy to brainpy.state for consistency

---------

Co-authored-by: Chaoming Wang <adaduo@outlook.com>
Co-authored-by: Chaoming Wang <chao.brain@qq.com>

Author: 932179209

brainpy/dyn/neurons/lif.py

@@ -857,6 +857,10 @@ class ExpIF(ExpIFLTC):
            conductance-based synaptic drive." Physical Review E 76, no. 2 (2007): 021919.
     .. [5] https://en.wikipedia.org/wiki/Exponential_integrate-and-fire
 
+    .. seealso::
+
+       :class:`brainpy.state.ExpIF` provides the state-based formulation of this neuron.
+
     **Examples**
 
     There is a simple usage example::
@@ -978,6 +982,10 @@ class ExpIFRefLTC(ExpIFLTC):
            conductance-based synaptic drive." Physical Review E 76, no. 2 (2007): 021919.
     .. [5] https://en.wikipedia.org/wiki/Exponential_integrate-and-fire
 
+    .. seealso::
+
+       :class:`brainpy.state.ExpIFRef` provides the state-based formulation of this neuron.
+
     **Examples**
 
     There is a simple usage example::
@@ -1319,6 +1327,10 @@ class AdExIFLTC(GradNeuDyn):
            inputs." Journal of Neuroscience 23.37 (2003): 11628-11640.
     .. [2] http://www.scholarpedia.org/article/Adaptive_exponential_integrate-and-fire_model
 
+    .. seealso::
+
+       :class:`brainpy.state.AdExIF` provides the state-based formulation of this model.
+
     **Examples**
 
     An example usage:

brainpy/state/__init__.py

@@ -18,8 +18,12 @@
 from ._base import __all__ as base_all
 from ._exponential import *
 from ._exponential import __all__ as exp_all
+from ._hh import *
+from ._hh import __all__ as hh_all
 from ._inputs import *
 from ._inputs import __all__ as inputs_all
+from ._izhikevich import *
+from ._izhikevich import __all__ as izh_all
 from ._lif import *
 from ._lif import __all__ as neuron_all
 from ._projection import *
@@ -34,13 +38,8 @@
 from ._synaptic_projection import __all__ as synproj_all
 from ._synouts import *
 from ._synouts import __all__ as synout_all
-from .. import mixin
 
-__main__ = ['version2', 'mixin'] + inputs_all + neuron_all + readout_all + stp_all + synapse_all
+__main__ = inputs_all + neuron_all + izh_all + hh_all + readout_all + stp_all + synapse_all
 __main__ = __main__ + synout_all + base_all + exp_all + proj_all + synproj_all
-del inputs_all, neuron_all, readout_all, stp_all, synapse_all, synout_all, base_all
+del inputs_all, neuron_all, izh_all, hh_all, readout_all, stp_all, synapse_all, synout_all, base_all
 del exp_all, proj_all, synproj_all
-
-if __name__ == '__main__':
-    mixin
-

brainpy/state/_base.py

@@ -40,6 +40,8 @@ def _input_label_repr(name: str, label: Optional[str] = None):
 
 
 class Dynamics(brainstate.nn.Dynamics):
+    __module__ = 'brainpy.state'
+
     def __init__(self, in_size: Size, name: Optional[str] = None):
         # initialize
         super().__init__(name=name, in_size=in_size)
@@ -401,21 +403,21 @@ def align_pre(self, dyn: Union[ParamDescriber[T], T]) -> T:
         Examples
         --------
         >>> import brainstate
-        >>> n1 = brainstate.nn.LIF(10)
-        >>> n1.align_pre(brainstate.nn.Expon.desc(n1.varshape))  # n2 will run after n1
+        >>> n1 = brainpy.state.LIF(10)
+        >>> n1.align_pre(brainpy.state.Expon.desc(n1.varshape))  # n2 will run after n1
         """
         if isinstance(dyn, Dynamics):
-            self._add_after_update(id(dyn), dyn)
+            self.add_after_update(id(dyn), dyn)
             return dyn
         elif isinstance(dyn, ParamDescriber):
             if not issubclass(dyn.cls, Dynamics):
                 raise TypeError(f'The input {dyn} should be an instance of {Dynamics}.')
-            if not self._has_after_update(dyn.identifier):
-                self._add_after_update(
+            if not self.has_after_update(dyn.identifier):
+                self.add_after_update(
                     dyn.identifier,
                     dyn() if ('in_size' in dyn.kwargs or len(dyn.args) > 0) else dyn(in_size=self.varshape)
                 )
-            return self._get_after_update(dyn.identifier)
+            return self.get_after_update(dyn.identifier)
         else:
             raise TypeError(f'The input {dyn} should be an instance of {Dynamics} or a delayed initializer.')
 
@@ -425,7 +427,7 @@ class Neuron(Dynamics):
     Base class for all spiking neuron models.
 
     This abstract class serves as the foundation for implementing various spiking neuron
-    models in the BrainPy framework. It extends the ``brainstate.nn.Dynamics`` class and
+    models in the BrainPy framework. It extends the ``brainpy.state.Dynamics`` class and
     provides common functionality for spike generation, membrane potential dynamics, and
     surrogate gradient handling required for training spiking neural networks.
 
@@ -595,7 +597,7 @@ class Neuron(Dynamics):
     .. [3] Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014). Neuronal dynamics:
            From single neurons to networks and models of cognition. Cambridge University Press.
     """
-    __module__ = 'brainpy'
+    __module__ = 'brainpy.state'
 
     def __init__(
         self,
@@ -849,4 +851,4 @@ class Synapse(Dynamics):
     .. [3] Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014). Neuronal dynamics:
            From single neurons to networks and models of cognition. Cambridge University Press.
     """
-    __module__ = 'brainpy'
+    __module__ = 'brainpy.state'

brainpy/state/_base_test.py

@@ -148,7 +148,7 @@ def test_neuron_soft_vs_hard_reset(self):
     def test_neuron_module_attribute(self):
         """Test __module__ attribute is correctly set."""
         neuron = Neuron(in_size=self.in_size)
-        self.assertEqual(neuron.__module__, 'brainpy')
+        self.assertEqual(neuron.__module__, 'brainpy.state')
 
 
 class TestSynapseBaseClass(unittest.TestCase):
@@ -247,7 +247,7 @@ def update(self, x=None):
     def test_synapse_module_attribute(self):
         """Test __module__ attribute is correctly set."""
         synapse = Synapse(in_size=self.in_size)
-        self.assertEqual(synapse.__module__, 'brainpy')
+        self.assertEqual(synapse.__module__, 'brainpy.state')
 
     def test_synapse_varshape_attribute(self):
         """Test varshape attribute is correctly set."""

brainpy/state/_exponential.py

@@ -71,7 +71,7 @@ class Expon(Synapse, AlignPost):
     where synaptic variables are aligned with post-synaptic neurons, enabling event-driven
     computation and more efficient handling of sparse connectivity patterns.
     """
-    __module__ = 'brainpy'
+    __module__ = 'brainpy.state'
 
     def __init__(
         self,
@@ -156,7 +156,7 @@ class DualExpon(Synapse, AlignPost):
     where synaptic variables are aligned with post-synaptic neurons, enabling event-driven
     computation and more efficient handling of sparse connectivity patterns.
     """
-    __module__ = 'brainpy'
+    __module__ = 'brainpy.state'
 
     def __init__(
         self,