update changes

Author: chaoming0625

changes.md

@@ -1,5 +1,4 @@
-# Change from Version 2.3.1 to Version 2.3.2
-
+# Change from Version 2.3.4 to Version 2.3.5
 
 
 This release (under the branch of ``brainpy=2.3.x``) continues to add supports for brain-inspired computation.
@@ -8,23 +7,149 @@ This release (under the branch of ``brainpy=2.3.x``) continues to add supports f
 ## New Features
 
 
-### 1. New package structure for stable API release
+### 1. ``brainpy.share`` for sharing data across submodules
+
+In this release, we abstract the shared data as a ``brainpy.share`` object. 
+
+This object together with ``brainpy.Delay`` we will introduce below 
+constitute the support that enable to define SNN models like ANN ones.
+
+
+### 2. ``brainpy.Delay`` for delay processing
+
+``Delay`` is abstracted as a dynamical system, which can be updated / retrieved by users. 
+
+```python
+import brainpy as bp
+
+class EINet(bp.DynamicalSystemNS):
+  def __init__(self, scale=1.0, e_input=20., i_input=20., delay=None):
+    super().__init__()
+
+    self.bg_exc = e_input
+    self.bg_inh = i_input
+
+    # network size
+    num_exc = int(3200 * scale)
+    num_inh = int(800 * scale)
+
+    # neurons
+    pars = dict(V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.,
+                V_initializer=bp.init.Normal(-55., 2.), input_var=False)
+    self.E = bp.neurons.LIF(num_exc, **pars)
+    self.I = bp.neurons.LIF(num_inh, **pars)
+
+    # synapses
+    we = 0.6 / scale  # excitatory synaptic weight (voltage)
+    wi = 6.7 / scale  # inhibitory synaptic weight
+    self.E2E = bp.experimental.Exponential(
+      bp.conn.FixedProb(0.02, pre=self.E.size, post=self.E.size),
+      g_max=we, tau=5., out=bp.experimental.COBA(E=0.)
+    )
+    self.E2I = bp.experimental.Exponential(
+      bp.conn.FixedProb(0.02, pre=self.E.size, post=self.I.size, ),
+      g_max=we, tau=5., out=bp.experimental.COBA(E=0.)
+    )
+    self.I2E = bp.experimental.Exponential(
+      bp.conn.FixedProb(0.02, pre=self.I.size, post=self.E.size),
+      g_max=wi, tau=10., out=bp.experimental.COBA(E=-80.)
+    )
+    self.I2I = bp.experimental.Exponential(
+      bp.conn.FixedProb(0.02, pre=self.I.size, post=self.I.size),
+      g_max=wi, tau=10., out=bp.experimental.COBA(E=-80.)
+    )
+    self.delayE = bp.Delay(self.E.spike, entries={'E': delay})
+    self.delayI = bp.Delay(self.I.spike, entries={'I': delay})
+
+  def update(self):
+    e_spike = self.delayE.at('E')
+    i_spike = self.delayI.at('I')
+    e_inp = self.E2E(e_spike, self.E.V) + self.I2E(i_spike, self.E.V) + self.bg_exc
+    i_inp = self.I2I(i_spike, self.I.V) + self.E2I(e_spike, self.I.V) + self.bg_inh
+    self.delayE(self.E(e_inp))
+    self.delayI(self.I(i_inp))
+
+```
+
+
+
+### 3.  ``brainpy.checkpoints.save_pytree`` and ``brainpy.checkpoints.load_pytree`` for saving/loading target from the filename
+
+Now we can directly use ``brainpy.checkpoints.save_pytree`` to save a 
+network state into the filepath we specified. 
+
+Similarly, we can use ``brainpy.checkpoints.load_pytree`` to load 
+states from the given file path.
+
+
+### 4. More ANN layers
+
+
+- brainpy.layers.ConvTranspose1d
+- brainpy.layers.ConvTranspose2d
+- brainpy.layers.ConvTranspose3d
+- brainpy.layers.Conv1dLSTMCell
+- brainpy.layers.Conv2dLSTMCell
+- brainpy.layers.Conv3dLSTMCell
+
+
+### 5. More compatible dense operators
+
+PyTorch operators:
+
+- brainpy.math.Tensor
+- brainpy.math.flatten
+- brainpy.math.cat
+- brainpy.math.abs
+- brainpy.math.absolute
+- brainpy.math.acos
+- brainpy.math.arccos
+- brainpy.math.acosh
+- brainpy.math.arccosh
+- brainpy.math.add
+- brainpy.math.addcdiv
+- brainpy.math.addcmul
+- brainpy.math.angle
+- brainpy.math.asin
+- brainpy.math.arcsin
+- brainpy.math.asinh
+- brainpy.math.arcsin
+- brainpy.math.atan
+- brainpy.math.arctan
+- brainpy.math.atan2
+- brainpy.math.atanh
 
-Unstable APIs are all hosted in ``brainpy._src`` module. 
-Other APIs are stable, and will be maintained in a long time. 
 
+TensorFlow operators:
 
-### 2. New schedulers
+- brainpy.math.concat
+- brainpy.math.reduce_sum
+- brainpy.math.reduce_max
+- brainpy.math.reduce_min
+- brainpy.math.reduce_mean
+- brainpy.math.reduce_all
+- brainpy.math.reduce_any
+- brainpy.math.reduce_logsumexp
+- brainpy.math.reduce_prod
+- brainpy.math.reduce_std
+- brainpy.math.reduce_variance
+- brainpy.math.reduce_euclidean_norm
+- brainpy.math.unsorted_segment_sqrt_n
+- brainpy.math.segment_mean
+- brainpy.math.unsorted_segment_sum
+- brainpy.math.unsorted_segment_prod
+- brainpy.math.unsorted_segment_max
+- brainpy.math.unsorted_segment_min
+- brainpy.math.unsorted_segment_mean
+- brainpy.math.segment_sum
+- brainpy.math.segment_prod
+- brainpy.math.segment_max
+- brainpy.math.segment_min
+- brainpy.math.clip_by_value
+- brainpy.math.cast
 
-- `brainpy.optim.CosineAnnealingWarmRestarts`
-- `brainpy.optim.CosineAnnealingLR`
-- `brainpy.optim.ExponentialLR`
-- `brainpy.optim.MultiStepLR`
-- `brainpy.optim.StepLR`
 
+### Others
 
-### 3. Others
+- Remove the hard requirements of ``brainpylib`` and ``numba``.
 
-- support `static_argnums` in `brainpy.math.jit`
-- fix bugs of `reset_state()` and `clear_input()` in `brainpy.channels`
-- fix jit error checking