Math

brainpy/_src/dynold/neurons/reduced_models.py

@@ -886,7 +886,7 @@ def __init__(
       self.integral = sdeint(method=self.method, f=self.derivative, g=self.noise)
     self.reset_state(self.mode)
 
-  def reset_state(self, batch_size=None):
+  def reset_state(self, batch_size=None, **kwargs):
     super().reset_state(batch_size)
     if self.input_var:
       self.input = variable_(bm.zeros, self.varshape, batch_size)
@@ -1023,7 +1023,7 @@ def __init__(
 
       # parameters for training
       mode: bm.Mode = None,
-      spike_fun: Callable = bm.surrogate.inv_square_grad,
+      spike_fun: Callable = bm.surrogate.inv_square_grad2,
   ):
     # initialization
     super(HindmarshRose, self).__init__(size=size,

brainpy/_src/math/interoperability.py

@@ -7,14 +7,20 @@
 
 
 __all__ = [
-  'as_device_array', 'as_jax', 'as_ndarray', 'as_numpy', 'as_variable',
+  'as_device_array', 'as_jax', 'as_ndarray', 'as_numpy', 'as_variable', 'is_bp_array'
 ]
 
 
 def _as_jax_array_(obj):
   return obj.value if isinstance(obj, Array) else obj
 
 
+def is_bp_array(x):
+  """Check if the input is a ``brainpy.math.Array``.
+  """
+  return isinstance(x, Array)
+
+
 def as_device_array(tensor, dtype=None):
   """Convert the input to a ``jax.numpy.DeviceArray``.
 

brainpy/_src/math/surrogate/__init__.py

@@ -2,5 +2,5 @@
 
 
 from .base import *
-from ._one_input import *
+from ._one_input_new import *
 from ._two_inputs import *