Release notes (brainpy)

Release notes (`brainpy`)#

brainpy>2.3.x#

Version 2.5.0#

This release contains many new features and fixes. It is the first release with a mature solution for Brain Dynamics Operator Customization on both CPU and GPU platforms.

New Features#

Add synapse projection with Delta synapse models through brainpy.dyn.HalfProjDelta and brainpy.dyn.FullProjDelta.
Add brainpy.math.exprel, and change the code in the corresponding HH neuron models to improve numerical computation accuracy. These changes can significantly improve the numerical integration accuracy of HH-like models under x32 computation.
Add brainpy.reset_level() decorator so that the state resetting order can be customized by users.
Add brainpy.math.ein_rearrange, brainpy.math.ein_reduce, and brainpy.math.ein_repeat functions
Add brainpy.math.scan transformation.
Rebase all customized operators using Taichi JIT compiler. On the CPU platform, the speed performance can be boosted ten to hundred times. On the GPU platforms, the flexibility can be greatly improved.
Many bug fixes.
A new version of brainpylib>=0.2.4 has been released, supporting operator customization through the Taichi compiler. The supported backends include Linux, Windows, MacOS Intel, and MacOS M1 platforms. Tutorials please see https://brainpy.readthedocs.io/en/latest/tutorial_advanced/operator_custom_with_taichi.html

What’s Changed#

[docs] Add taichi customized operators tutorial by @Routhleck in https://github.com/brainpy/BrainPy/pull/545
[docs] Optimize tutorial code in operator_custom_with_taichi.ipynb of documentations by @Routhleck in https://github.com/brainpy/BrainPy/pull/546
[running] fix multiprocessing bugs by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/547
[docs] Fix typo in docs by @Routhleck in https://github.com/brainpy/BrainPy/pull/549
:arrow_up: Bump conda-incubator/setup-miniconda from 2 to 3 by @dependabot in https://github.com/brainpy/BrainPy/pull/551
updates by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/550
brainpy.math.defjvp and brainpy.math.XLACustomOp.defjvp by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/554
:arrow_up: Bump actions/setup-python from 4 to 5 by @dependabot in https://github.com/brainpy/BrainPy/pull/555
Fix brainpy.math.ifelse bugs by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/556
[math & dyn] add brainpy.math.exprel, and change the code in the corresponding HH neuron models to improve numerical computation accuracy by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/557
Update README by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/558
[doc] add conductance neuron model tutorial by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/559
Doc by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/560
add brainpy.math.functional_vector_grad and brainpy.reset_level() decorator by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/561
[math] change the internal implementation of surrogate function by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/562
Math by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/563
[doc] update citations by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/564
add support for multi-class margin loss by @charlielam0615 in https://github.com/brainpy/BrainPy/pull/566
Support for Delta synapse projections by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/568
[math] Add taichi customized operators(event csrmv, csrmv, jitconn event mv, jitconn mv) by @Routhleck in https://github.com/brainpy/BrainPy/pull/553
fix doc by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/571
Fix default math parameter setting bug by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/572
fix bugs in brainpy.math.random.truncated_normal by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/574
[doc] fix doc by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/576
fix bugs in truncated_normal; add TruncatedNormal init. by @charlielam0615 in https://github.com/brainpy/BrainPy/pull/575
[Dyn] Fix alpha synapse bugs by @ztqakita in https://github.com/brainpy/BrainPy/pull/578
fix brainpy.math.softplus and brainpy.dnn.SoftPlus by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/581
add TruncatedNormal to initialize.py by @charlielam0615 in https://github.com/brainpy/BrainPy/pull/583
Fix _format_shape in random_inits.py by @charlielam0615 in https://github.com/brainpy/BrainPy/pull/584
fix bugs in truncated_normal by @charlielam0615 in https://github.com/brainpy/BrainPy/pull/585
[dyn] fix warning of reset_state by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/587
[math] upgrade variable retrival by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/589
[math & dnn] add brainpy.math.unflatten and brainpy.dnn.Unflatten by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/588
[math] add ein_rearrange, ein_reduce, and ein_repeat functions by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/590
[math] Support taichi customized op with metal cpu backend by @Routhleck in https://github.com/brainpy/BrainPy/pull/579
Doc fix and standardize Dual Exponential model again by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/591
update doc, upgrade reset_state, update projection models by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/592
[taichi] Make taichi caches more transparent and Add clean caches function by @Routhleck in https://github.com/brainpy/BrainPy/pull/596
[test] remove test skip on macos, since brainpylib supports taichi interface on macos by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/597
[dyn] add clear_input in the step_run function. by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/601
[math] Refactor taichi operators by @Routhleck in https://github.com/brainpy/BrainPy/pull/598
[math] fix brainpy.math.scan by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/604
disable_ jit support in brainpy.math.scan by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/606
[math] Remove the logs that taichi.init() print by @Routhleck in https://github.com/brainpy/BrainPy/pull/609
Version control in Publish.yml CI by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/610

New Contributors#

@charlielam0615 made their first contribution in https://github.com/brainpy/BrainPy/pull/566

Full Changelog: https://github.com/brainpy/BrainPy/compare/V2.4.6…V2.5.0

Version 2.4.6#

This release contains more than 130 commit updates, and has provided several new features.

New Features#

1. surrogate gradient functions are more transparent.#

New instances can be used to compute the surrogate gradients. For example:

import brainpy.math as bm
fun = bm.surrogate.Sigmoid()

# forward function
spk = fun(membrane_potential)

# backward function
dV = fun.surrogate_grad(1., membrane_potential)

# surrogate forward function
surro_spk = fun.surrogate_fun(membrane_potential)

2. Add `brainpy.math.eval_shape` for evaluating the all dynamical variables used in the target function.#

This function is similar to jax.eval_shape which has no FLOPs, while it can extract all variables used in the target function. For example:

net = ...  # any dynamical system
inputs = ...  # inputs to the dynamical system
variables, outputs= bm.eval_shape(net, inputs)  
# "variables" are all variables used in the target "net"

In future, this function will be used everywhere to transform all jax transformations into brainpy’s oo transformations.

3. Generalize tools and interfaces for state managements.#

For a single object:

The .reset_state() defines the state resetting of all local variables in this node.
The .load_state() defines the state loading from external disks (typically, a dict is passed into this .load_state() function).
The .save_state() defines the state saving to external disks (typically, the .save_state() function generates a dict containing all variable values).

Here is an example to define a full class of brainpy.DynamicalSystem.

import brainpy as bp

class YouDynSys(bp.DynamicalSystem):
   def __init__(self, ):  # define parameters
      self.par1 = ....
      self.num = ...

  def reset_state(self, batch_or_mode=None):  # define variables
     self.a = bp.init.variable_(bm.zeros, (self.num,), batch_or_mode)

  def load_state(self, state_dict):  # load states from an external dict
     self.a.value = bm.as_jax(state_dict['a'])

  def save_state(self):  # save states as an external dict
     return {'a': self.a.value}

For a complex network model, brainpy provide unified state managment interface for initializing, saving, and loading states.

The brainpy.reset_state() defines the state resetting of all variables in this node and its children nodes.
The brainpy.load_state() defines the state loading from external disks of all variables in the node and its children.
The brainpy.save_state() defines the state saving to external disks of all variables in the node and its children.
The brainpy.clear_input() defines the clearing of all input variables in the node and its children.

4. Unified brain simulation and brain-inspired computing interface through automatic membrane scaling.#

The same model used in brain simulation can be easily transformed into the one used for brain-inspired computing for training. For example,

class EINet(bp.DynSysGroup):
  def __init__(self):
    super().__init__()
    self.N = bp.dyn.LifRefLTC(4000, V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.,
                              V_initializer=bp.init.Normal(-55., 2.))
    self.delay = bp.VarDelay(self.N.spike, entries={'I': None})
    self.E = bp.dyn.ProjAlignPost1(
      comm=bp.dnn.EventCSRLinear(bp.conn.FixedProb(0.02, pre=3200, post=4000), weight=bp.init.Normal(0.6, 0.01)),
      syn=bp.dyn.Expon(size=4000, tau=5.),
      out=bp.dyn.COBA(E=0.),
      post=self.N
    )
    self.I = bp.dyn.ProjAlignPost1(
      comm=bp.dnn.EventCSRLinear(bp.conn.FixedProb(0.02, pre=800, post=4000), weight=bp.init.Normal(6.7, 0.01)),
      syn=bp.dyn.Expon(size=4000, tau=10.),
      out=bp.dyn.COBA(E=-80.),
      post=self.N
    )

  def update(self, input):
    spk = self.delay.at('I')
    self.E(spk[:3200])
    self.I(spk[3200:])
    self.delay(self.N(input))
    return self.N.spike.value


# used for brain simulation
with bm.environment(mode=bm.nonbatching_mode):
  net = EINet()


# used for brain-inspired computing
# define the `membrane_scaling` parameter
with bm.environment(mode=bm.TrainingMode(128), membrane_scaling=bm.Scaling.transform([-60., -50.])):
  net = EINet()

5. New apis for operator customization on CPU and GPU devices through `brainpy.math.XLACustomOp`.#

Starting from this release, brainpy introduces Taichi for operator customization. Now, users can write CPU and GPU operators through numba and taichi syntax on CPU device, and taichi syntax on GPu device. Particularly, to define an operator, user can use:

import numba as nb
import taichi as ti
import numpy as np
import jax
import brainpy.math as bm


@nb.njit
def numba_cpu_fun(a, b, out_a, out_b):
  out_a[:] = a
  out_b[:] = b


@ti.kernel
def taichi_gpu_fun(a, b, out_a, out_b):
  for i in range(a.size):
    out_a[i] = a[i]
  for i in range(b.size):
    out_b[i] = b[i]


prim = bm.XLACustomOp(cpu_kernel=numba_cpu_fun, gpu_kernel=taichi_gpu_fun)
a2, b2 = prim(np.random.random(1000), np.random.random(1000),
              outs=[jax.ShapeDtypeStruct(1000, dtype=np.float32),
                    jax.ShapeDtypeStruct(1000, dtype=np.float32)])

6. Generalized STDP models which are compatible with diverse synapse models.#

See https://github.com/brainpy/BrainPy/blob/master/brainpy/_src/dyn/projections/tests/test_STDP.py

What’s Changed#

[bug] fix compatible bug by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/508
[docs] add low-level op customization by @ztqakita in https://github.com/brainpy/BrainPy/pull/507
Compatible with jax==0.4.16 by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/511
updates for parallelization support by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/514
Upgrade surrogate gradient functions by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/516
[doc] update operator customization by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/517
Updates for OO transforma and surrogate functions by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/519
[dyn] add neuron scaling by @ztqakita in https://github.com/brainpy/BrainPy/pull/520
State saving, loading, and resetting by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/521
[delay] rewrite previous delay APIs so that they are compatible with new brainpy version by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/522
[projection] upgrade projections so that APIs are reused across different models by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/523
[math] the interface for operator registration by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/524
FIx bug in Delay by @ztqakita in https://github.com/brainpy/BrainPy/pull/525
Fix bugs in membrane scaling by @ztqakita in https://github.com/brainpy/BrainPy/pull/526
[math] Implement taichi op register by @Routhleck in https://github.com/brainpy/BrainPy/pull/527
Link libtaichi_c_api.so when import brainpylib by @Routhleck in https://github.com/brainpy/BrainPy/pull/528
update taichi op customization by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/529
Fix error message by @HoshinoKoji in https://github.com/brainpy/BrainPy/pull/530
[math] remove the hard requirement of taichi by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/531
[math] Resolve encoding of source kernel when ti.func is nested in ti… by @Routhleck in https://github.com/brainpy/BrainPy/pull/532
[math] new abstract function for XLACustomOp, fix its bugs by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/534
[math] fix numpy array priority by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/533
[brainpy.share] add category shared info by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/535
[doc] update documentations by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/536
[doc] update doc by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/537
[dyn] add brainpy.reset_state() and brainpy.clear_input() for more consistent and flexible state managements by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/538
[math] simplify the taichi AOT operator customization interface by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/540
[dyn] add save_state, load_state, reset_state, and clear_input helpers by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/542
[dyn] update STDP APIs on CPUs and fix bugs by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/543

New Contributors#

@HoshinoKoji made their first contribution in https://github.com/brainpy/BrainPy/pull/530

Full Changelog: https://github.com/brainpy/BrainPy/compare/V2.4.5…V2.4.6

Version 2.4.5#

New Features#

A new version of brainpylib==0.1.10 has been released. In this release, we have fixed some bugs of brainpy dedicated GPU operators. Users can freely use them in any application.
Correspondingly, dedicated operators in brainpy.math have been refined.
.tracing_variable() has been created to support tracing Variables during computations and compilations. Example usage please see #472
Add a new random API for creating multiple random keys: brainpy.math.random.split_keys().
Fix bugs, including
- brainpy.dnn.AllToAll module
- RandomState.
- brainpy.math.cond and brainpy.math.while_loop when variables are used in both branches

What’s Changed#

Creat random key automatically when it is detected by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/461
[encoding] upgrade encoding methods by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/464
fix #466 by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/467
Update operators for compatible with brainpylib>=0.1.10 by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/468
Support tracing Variable during computation and compilation by using tracing_variable() function by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/472
Add code of conduct and contributing guides by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/473
add Funding and Development roadmap by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/475
Create SECURITY.md by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/474
Create dependabot.yml by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/476
update maintainence info in README by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/479
:arrow_up: Bump actions/setup-python from 2 to 4 by @dependabot in https://github.com/brainpy/BrainPy/pull/477
:arrow_up: Bump actions/checkout from 2 to 4 by @dependabot in https://github.com/brainpy/BrainPy/pull/478
ad acknowledgment.md by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/482
update quickstart of simulating a brain dynamics model with new APIs by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/483
update advanced tutorials by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/484
[docs] Update installation.rst by @Routhleck in https://github.com/brainpy/BrainPy/pull/485
update requirements by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/486
[doc] update docs by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/487
[doc] update docs by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/488
Decouple Online and Offline training algorithms as brainpy.mixin.SupportOnline and brainpy.mixin.SupportOffline by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/489
[dyn] add STDP_Song2000 LTP model by @ztqakita in https://github.com/brainpy/BrainPy/pull/481
update STDP by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/491
[doc] update the API of brainpy.dyn module & add synaptic plasticity module by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/492
fix bug by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/493
[math] fix bugs in cond and while_loop when same variables are used in both branches by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/494
[docs] add BrainPy docker and docs by @ztqakita in https://github.com/brainpy/BrainPy/pull/496
[docs] update README and installation by @ztqakita in https://github.com/brainpy/BrainPy/pull/499
:arrow_up: Bump docker/build-push-action from 4 to 5 by @dependabot in https://github.com/brainpy/BrainPy/pull/498
:arrow_up: Bump docker/login-action from 2 to 3 by @dependabot in https://github.com/brainpy/BrainPy/pull/497
Add strings in bp._src.dyn.bio_models and abstract_models by @AkitsuFaye in https://github.com/brainpy/BrainPy/pull/500
[reset] update logics of state reset in DynamicalSystem by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/501
[doc] upgrade docs with the latest APIs, fix #463 by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/502
[doc] add synapse model documentations by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/503
Changed the order of code blocks in the docs of hh models and lif models by @AkitsuFaye in https://github.com/brainpy/BrainPy/pull/505
[mode] move recurrent models in brainpy.dnn model into brainpy.dyn module by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/506

New Contributors#

@dependabot made their first contribution in https://github.com/brainpy/BrainPy/pull/477

Full Changelog: https://github.com/brainpy/BrainPy/compare/V2.4.4…V2.4.5

Version 2.4.4#

This release has fixed several bugs and updated the sustainable documentation.

What’s Changed#

[mixin] abstract the behavior of supporting input projection by brainpy.mixin.ReceiveInputProj by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/428
Update delays, models, and projections by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/429
Compatible with jax=0.4.14 by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/431
Add new tests by @yygf123 in https://github.com/brainpy/BrainPy/pull/430
Add NonBatchingMode function by @yygf123 in https://github.com/brainpy/BrainPy/pull/433
[connect] Complete FixedTotalNum class and fix bugs by @Routhleck in https://github.com/brainpy/BrainPy/pull/434
Update the document “Concept 2: Dynamical System” by @yygf123 in https://github.com/brainpy/BrainPy/pull/435
[docs] Update three part of tutorial toolbox by @Routhleck in https://github.com/brainpy/BrainPy/pull/436
[docs] Update index.rst for surrogate gradient by @Routhleck in https://github.com/brainpy/BrainPy/pull/437
Reconstruct BrainPy documentations by @ztqakita in https://github.com/brainpy/BrainPy/pull/438
Renew doc requirements.txt by @ztqakita in https://github.com/brainpy/BrainPy/pull/441
Compatibility updates by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/442
update docs by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/443
Update optimizer by @yygf123 in https://github.com/brainpy/BrainPy/pull/451
[docs] Update custom saving and loading by @Routhleck in https://github.com/brainpy/BrainPy/pull/439
[doc] add new strings in bp._src.dyn.hh.py and bp._src.dyn.lif.py by @AkitsuFaye in https://github.com/brainpy/BrainPy/pull/454
Serveral updates by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/452
Update doc bug in index.rst by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/458
add brainpy.dyn.Alpha synapse model by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/459
[doc] update ODE doc by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/460

New Contributors#

@AkitsuFaye made their first contribution in https://github.com/brainpy/BrainPy/pull/454

Full Changelog: https://github.com/brainpy/BrainPy/compare/V2.4.3…V2.4.4

Version 2.4.3#

This release has standardized the modeling of DNN and SNN models by two intercorrelated packages: brainpy.dnn and brainpy.dyn.

Overall, the modeling of brain dynamics in this release has the following advantages:

the automatic merging of the duplicate synapses, keeping the minimal device memory
easy model and data parallelization across multiple devices
easy integration with artificial neural networks
a new abstraction that decouples dynamics from communication
the unified DynamicalSystem interface

New Features#

Support to define ion channel models which rely on multiple ions. For example,

class HH(bp.dyn.CondNeuGroup):
   def __init__(self, size):
      super().__init__(size)
      self.k = bp.dyn.PotassiumFixed(size)
      self.ca = bp.dyn.CalciumFirstOrder(size)

      self.kca = bp.dyn.mix_ions(self.k, self.ca)  # Ion that mixing Potassium and Calcium
      self.kca.add_elem(ahp=bp.dyn.IAHP_De1994v2(size))  # channel that relies on both Potassium and Calcium

New style .update() function in brainpy.DynamicalSystem which resolves all compatible issues. Starting from this version, all update() no longer needs to receive a global shared argument such as tdi.

class YourDynSys(bp.DynamicalSystem):
  def update(self, x):
    t = bp.share['t']
    dt = bp.share['dt']
    i = bp.share['i']
    ...

Optimize the connection-building process when using brainpy.conn.ScaleFreeBA, brainpy.conn.ScaleFreeBADual, brainpy.conn.PowerLaw
New dual exponential model brainpy.dyn.DualExponV2 can be aligned with post dimension.
More synaptic projection abstractions, including
- brainpy.dyn.VanillaProj
- brainpy.dyn.ProjAlignPostMg1
- brainpy.dyn.ProjAlignPostMg2
- brainpy.dyn.ProjAlignPost1
- brainpy.dyn.ProjAlignPost2
- brainpy.dyn.ProjAlignPreMg1
- brainpy.dyn.ProjAlignPreMg2
Fix compatible issues, fix unexpected bugs, and improve the model tests.

What’s Changed#

[connect] Optimize the connector about ScaleFreeBA, ScaleFreeBADual, PowerLaw by @Routhleck in https://github.com/brainpy/BrainPy/pull/412
[fix] bug of connect.base.py’s require function by @Routhleck in https://github.com/brainpy/BrainPy/pull/413
Many Updates by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/414
Update docs by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/415
fix conflict by @yygf123 in https://github.com/brainpy/BrainPy/pull/416
add a new implementation of Dual Exponential Synapse model which can be aligned post. by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/417
Enable test when pull requests by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/418
Add random.seed() by @yygf123 in https://github.com/brainpy/BrainPy/pull/419
Remove windows CI because it always generates strange errors by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/420
Recent updates by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/421
upgrade Runner and Trainer for new style of DynamicalSystem.update() function by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/422
update docs by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/424
fix lif model bugs and support two kinds of spike reset: soft and hard by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/423
rewrite old synapses with decomposed components by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/425
fix autograd bugs by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/426

New Contributors#

@yygf123 made their first contribution in https://github.com/brainpy/BrainPy/pull/416

Full Changelog: https://github.com/brainpy/BrainPy/compare/V2.4.2…V2.4.3

Version 2.4.2#

We are very excited to release this new version of BrainPy V2.4.2. In this new update, we cover several exciting features:

New Features#

Reorganize the model to decouple dynamics and communication.
Add brainpy.dyn for dynamics models and brainpy.dnn for the ANN layer and connection structures.
Supplement many docs for dedicated operators and common bugs of BrainPy.
Fix many bugs.

What’s Changed#

[ANN] add more activation functions by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/379
Optimize Gaussian Decay initializer by @Routhleck in https://github.com/brainpy/BrainPy/pull/381
[update] new loss functions, surrograte base class, Array built-in functions by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/383
[parallelization] new module of brainpy.pnn for auto parallelization of brain models by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/385
[fix] fix the bug of loading states by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/388
[math] support jax.disable_jit() for debugging by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/389
[initialize] speed up brainpy.init.DOGDecay by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/390
[doc] fix doc build by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/391
Add deprecations for deprecated APIs or functions by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/393
[math] enable debugging for new style of transformations in BrainPy by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/392
[math] flow control updates by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/396
Test of rates by @shangyangli in https://github.com/brainpy/BrainPy/pull/386
Add math docs: NumPy-like operations and Dedicated operators by @c-xy17 in https://github.com/brainpy/BrainPy/pull/395
[doc] documentation about how to debug and common gotchas by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/397
Update requirements-doc.txt by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/399
debug (images not displayed) by @c-xy17 in https://github.com/brainpy/BrainPy/pull/400
Decouple dynamics and comminucations by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/401
[fix] bugs of control flows by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/404
Test for channels, neurons and synapses. by @ztqakita in https://github.com/brainpy/BrainPy/pull/403
Implement function to visualize connection matrix by @Routhleck in https://github.com/brainpy/BrainPy/pull/405
Optimize GaussianProb by @Routhleck in https://github.com/brainpy/BrainPy/pull/406
[dyn] add reduce models, HH-type models and channels by @ztqakita in https://github.com/brainpy/BrainPy/pull/408
[dnn] add various linear layers by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/407
[delay] VariableDelay and DataDelay by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/409
[dyn] add COBA examples using the interface of new brainpy.dyn module by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/410
[dyn] Update dyn.neurons docs and fix several bugs by @ztqakita in https://github.com/brainpy/BrainPy/pull/411

New Contributors#

@shangyangli made their first contribution in https://github.com/brainpy/BrainPy/pull/386

Full Changelog: https://github.com/brainpy/BrainPy/compare/V2.4.1…V2.4.2

Version 2.4.1#

New Features#

[math] Support the error report when modifying a brainpy.math.Array during compilation
[math] add brainpy.math.event, brainpy.math.sparse and brainpy.math.jitconn module, needs brainpylib >= 0.1.9
[interoperation] add apis and docs for brainpy.layers.FromFlax and brainpy.layer.ToFlaxRNNCell
[fix] Bug fixes:
- fix WilsonCowan bug
- fix brainpy.connect.FixedProb bug
- fix analysis jit bug

What’s Changed#

Update structures by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/364
create blocksparse matrix matrix multiplication opearator by @Routhleck in https://github.com/brainpy/BrainPy/pull/365
commit by @grysgreat in https://github.com/brainpy/BrainPy/pull/367
Fix bugs by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/368
[math] update dedicated operators by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/370
fix bugs by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/371
[bug] fix merging bug by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/372
[structure] update package structure by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/369
[test] update csrmv tests by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/373
[interoperation] add apis and docs for brainpy.layers.FromFlax and brainpy.layer.ToFlaxRNNCell by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/374
[doc] update documentation by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/375
[bug] fix brainpy.connect.FixedProb bug by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/376
[bug] fix analysis jit bug by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/377
update brainpylib requirements by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/378

New Contributors#

@Routhleck made their first contribution in https://github.com/brainpy/BrainPy/pull/365
@grysgreat made their first contribution in https://github.com/brainpy/BrainPy/pull/367

Full Changelog: https://github.com/brainpy/BrainPy/compare/V2.4.0…V2.4.1

Version 2.4.0#

This branch of releases (brainpy==2.4.x) are going to support the large-scale modeling for brain dynamics.

As the start, this release provides support for automatic object-oriented (OO) transformations.

What’s New#

Automatic OO transformations on longer need to take dyn_vars or child_objs information. These transformations are capable of automatically inferring the underlying dynamical variables. Specifically, they include:
- brainpy.math.grad and other autograd functionalities
- brainpy.math.jit
- brainpy.math.for_loop
- brainpy.math.while_loop
- brainpy.math.ifelse
- brainpy.math.cond
Update documentation
Fix several bugs

What’s Changed#

reorganize operators in brainpy.math by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/357
Automatic transformations any function/object using brainpy.math.Variable by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/358
New OO transforms support jax.disable_jit mode by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/359
[oo transform] Enable new style of jit transformation to support static_argnums and static_argnames by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/360
[documentation] update documentation to brainpy>=2.4.0 by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/361

Full Changelog: https://github.com/brainpy/BrainPy/compare/V2.3.8…V2.4.0

Version 2.3.8#

This release continues to add support for improving the usability of BrainPy.

New Features#

New data structures for object-oriented transformations.
- NodeList and NodeDict for a list/tuple/dict of BrainPyObject instances.
- ListVar and DictVar for a list/tuple/dict of brainpy data.
Clip transformation for brainpy initializers.
All brainpylib operators are accessible in brainpy.math module. Especially there are some dedicated operators for scaling up the million-level neuron networks. For an example, see example in Simulating 1-million-neuron networks with 1GB GPU memory
Enable monitoring GPU models on CPU when setting DSRunner(..., memory_efficient=True). This setting can usually reduce so much memory usage.
brainpylib wheels on the Linux platform support the GPU operators. Users can install GPU version of brainpylib (require brainpylib>=0.1.7) directly by pip install brainpylib. @ztqakita

What’s Changed#

Fix bugs and add more variable structures: ListVar and DictVar by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/345
add CI for testing various models by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/346
Update docs and tests by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/347
Fix `Runner(jit=False)`` bug by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/348
Compatible with jax>=0.4.7 by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/349
Updates by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/350
reconstruct BrainPy by merging brainpylib by @ztqakita in https://github.com/brainpy/BrainPy/pull/351
Intergate brainpylib operators into brainpy by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/352
fix brainpylib call bug by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/354
Enable memory-efficient DSRunner by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/355
fix Array transform bug by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/356

Full Changelog: https://github.com/brainpy/BrainPy/compare/V2.3.7…V2.3.8

Version 2.3.7#

Fix bugs on population models in brainpy.rate module
Fix bug on brainpy.LoopOverTime
Add more synaptic models including DualExpoenetial model and Alpha model in brainpy.experimental module
Support call a module through right shift, such as data >> module1 >> module2

Version 2.3.6#

This release continues to add support for brain-inspired computation.

New Features#

More flexible customization of surrogate gradient functions.#

brainpy.math.surrogate.Sigmoid
brainpy.math.surrogate.PiecewiseQuadratic
brainpy.math.surrogate.PiecewiseExp
brainpy.math.surrogate.SoftSign
brainpy.math.surrogate.Arctan
brainpy.math.surrogate.NonzeroSignLog
brainpy.math.surrogate.ERF
brainpy.math.surrogate.PiecewiseLeakyRelu
brainpy.math.surrogate.SquarewaveFourierSeries
brainpy.math.surrogate.S2NN
brainpy.math.surrogate.QPseudoSpike
brainpy.math.surrogate.LeakyRelu
brainpy.math.surrogate.LogTailedRelu
brainpy.math.surrogate.ReluGrad
brainpy.math.surrogate.GaussianGrad
brainpy.math.surrogate.InvSquareGrad
brainpy.math.surrogate.MultiGaussianGrad
brainpy.math.surrogate.SlayerGrad

Fix bugs#

brainpy.LoopOverTime

Version 2.3.5#

This release continues to add support for brain-inspired computation.

New Features#

2. `brainpy.Delay` for delay processing#

Delay is abstracted as a dynamical system, which can be updated/retrieved by users.

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 file path 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

TensorFlow operators:

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

Others#

Remove the hard requirements of brainpylib and numba.

Version 2.3.4#

This release mainly focuses on the compatibility with other frameworks:

Fix Jax import error when jax>=0.4.2
Backward compatibility of brainpy.dyn module
Start to implement and be compatible with operators in pytorch and tensorflow, so that user’s pytorch/tensorflow models can be easily migrated to brainpy

Full Changelog: https://github.com/brainpy/BrainPy/compare/V2.3.3…V2.3.4

Version 2.3.3#

Improve backward compatibility:

monitors and inputs in DSRunner
models in brainpy.dyn
constants and function in brainpy.analysis

Version 2.3.2#

This release (under the branch of brainpy=2.3.x) continues to add support for brain-inspired computation.

New Features#

1. New package structure for stable API release#

Unstable APIs are all hosted in brainpy._src module. Other APIs are stable and will be maintained for a long time.

2. New schedulers#

brainpy.optim.CosineAnnealingWarmRestarts
brainpy.optim.CosineAnnealingLR
brainpy.optim.ExponentialLR
brainpy.optim.MultiStepLR
brainpy.optim.StepLR

3. Others#

support static_argnums in brainpy.math.jit
fix bugs of reset_state() and clear_input() in brainpy.channels
fix jit error checking

Version 2.3.1#

This release (under the release branch of brainpy=2.3.x) continues to add supports for brain-inspired computation.

import brainpy as bp
import brainpy.math as bm

Backwards Incompatible Changes#

1. Error: module ‘brainpy’ has no attribute ‘datasets’#

brainpy.datasets module is now published as an independent package brainpy_datasets.

Please change your dataset access from

bp.datasets.xxxxx

to

import brainpy_datasets as bp_data

bp_data.chaos.XXX
bp_data.vision.XXX

For a chaotic data series,

# old version
data = bp.datasets.double_scroll_series(t_warmup + t_train + t_test, dt=dt)
x_var = data['x']
y_var = data['y']
z_var = data['z']

# new version
data = bd.chaos.DoubleScrollEq(t_warmup + t_train + t_test, dt=dt)
x_var = data.xs
y_var = data.ys
z_var = data.zs

For a vision dataset,

# old version
dataset = bp.datasets.FashionMNIST(root, train=True, download=True)

# new version
dataset = bd.vision.FashionMNIST(root, split='train', download=True)

2. Error: DSTrainer must receive an instance with BatchingMode#

This error will happen when using brainpy.OnlineTrainer , brainpy.OfflineTrainer, brainpy.BPTT , brainpy.BPFF.

From version 2.3.1, BrainPy explicitly consider the computing mode of each model. For trainers, all training target should be a model with BatchingMode or TrainingMode.

If you are training model with OnlineTrainer or OfflineTrainer,

# old version
class NGRC(bp.DynamicalSystem):
  def __init__(self, num_in):
    super(NGRC, self).__init__()
    self.r = bp.layers.NVAR(num_in, delay=2, order=3)
    self.di = bp.layers.Dense(self.r.num_out, num_in)

  def update(self, sha, x):
    di = self.di(sha, self.r(sha, x))
    return x + di


# new version
bm.set_enviroment(mode=bm.batching_mode)

class NGRC(bp.DynamicalSystem):
  def __init__(self, num_in):
    super(NGRC, self).__init__()
    self.r = bp.layers.NVAR(num_in, delay=2, order=3)
    self.di = bp.layers.Dense(self.r.num_out, num_in, mode=bm.training_mode)

  def update(self, sha, x):
    di = self.di(sha, self.r(sha, x))
    return x + di

If you are training models with BPTrainer, adding the following line at the top of the script,

bm.set_enviroment(mode=bm.training_mode)

3. Error: inputs_are_batching is no longer supported.#

This is because if the training target is in batching mode, this has already indicated that the inputs should be batching.

Simple remove the inputs_are_batching from your functional call of .predict() will solve the issue.

New Features#

1. `brainpy.math` module upgrade#

`brainpy.math.surrogate` module for surrogate gradient functions.#

Currently, we support

brainpy.math.surrogate.arctan
brainpy.math.surrogate.erf
brainpy.math.surrogate.gaussian_grad
brainpy.math.surrogate.inv_square_grad
brainpy.math.surrogate.leaky_relu
brainpy.math.surrogate.log_tailed_relu
brainpy.math.surrogate.multi_gaussian_grad
brainpy.math.surrogate.nonzero_sign_log
brainpy.math.surrogate.one_input
brainpy.math.surrogate.piecewise_exp
brainpy.math.surrogate.piecewise_leaky_relu
brainpy.math.surrogate.piecewise_quadratic
brainpy.math.surrogate.q_pseudo_spike
brainpy.math.surrogate.relu_grad
brainpy.math.surrogate.s2nn
brainpy.math.surrogate.sigmoid
brainpy.math.surrogate.slayer_grad
brainpy.math.surrogate.soft_sign
brainpy.math.surrogate.squarewave_fourier_series

New transformation function `brainpy.math.to_dynsys`#

New transformation function brainpy.math.to_dynsys supports to transform a pure Python function into a DynamicalSystem. This will be useful when running a DynamicalSystem with arbitrary customized inputs.

import brainpy.math as bm

hh = bp.neurons.HH(1)

@bm.to_dynsys(child_objs=hh)
def run_hh(tdi, x=None):
    if x is not None:
	    hh.input += x
    
runner = bp.DSRunner(run_hhh, monitors={'v': hh.V})
runner.run(inputs=bm.random.uniform(3, 6, 1000))

Default data types#

Default data types brainpy.math.int_, brainpy.math.float_ and brainpy.math.complex_ are initialized according to the default x64 settings. Then, these data types can be set or get by brainpy.math.set_* or brainpy.math.get_* syntaxes.

Take default integer type int_ as an example,

# set the default integer type
bm.set_int_(jax.numpy.int64)

# get the default integer type
a1 = bm.asarray([1], dtype=bm.int_)
a2 = bm.asarray([1], dtype=bm.get_int()) # equivalent

Default data types are changed according to the x64 setting of JAX. For instance,

bm.enable_x64()
assert bm.int_ == jax.numpy.int64
bm.disable_x64()
assert bm.int_ == jax.numpy.int32

brainpy.math.float_ and brainpy.math.complex_ behaves similarly with brainpy.math.int_.

Environment context manager#

This release introduces a new concept computing environment in BrainPy. Computing environment is a default setting for current computation jobs, including the default data type (int_, float_, complex_), the default numerical integration precision (dt), the default computing mode (mode). All models, arrays, and computations using the default setting will be carried out under the environment setting.

Users can set a default environment through

brainpy.math.set_environment(mode, dt, x64)

However, ones can also construct models or perform computation through a temporal environment context manager, this can be implemented through:

# constructing a HH model with dt=0.1 and x64 precision
with bm.environment(mode, dt=0.1, x64=True):
    hh1 = bp.neurons.HH(1)
    
# constructing a HH model with dt=0.05 and x32 precision
with bm.environment(mode, dt=0.05, x64=False):
    hh2 = bp.neuron.HH(1)

Usually, users construct models for either brain-inspired computing (training mode) or brain simulation (nonbatching mode), therefore, there are shortcut context manager for setting a training environment or batching environment:

with bm.training_environment(dt, x64):
    pass

with bm.batching_environment(dt, x64):
    pass

2. `brainpy.dyn` module#

`brainpy.dyn.transfom` module for transforming a `DynamicalSystem` instance to a callable `BrainPyObject`.#

Specifically, we provide

LoopOverTime for unrolling a dynamical system over time.
NoSharedArg for removing the dependency of shared arguments.

3. Running supports in BrainPy#

All `brainpy.Runner` now are subclasses of `BrainPyObject`#

This means that all brainpy.Runner can be used as a part of the high-level program or transformation.

Enable the continuous running of a differential equation (ODE, SDE, FDE, DDE, etc.) with `IntegratorRunner`.#

For example,

import brainpy as bp

# differential equation
a, b, tau = 0.7, 0.8, 12.5
dV = lambda V, t, w, Iext: V - V * V * V / 3 - w + Iext
dw = lambda w, t, V: (V + a - b * w) / tau
fhn = bp.odeint(bp.JointEq([dV, dw]), method='rk4', dt=0.1)

# differential integrator runner
runner = bp.IntegratorRunner(fhn, monitors=['V', 'w'], inits=[1., 1.])

# run 1
Iext, duration = bp.inputs.section_input([0., 1., 0.5], [200, 200, 200], return_length=True)
runner.run(duration, dyn_args=dict(Iext=Iext))
bp.visualize.line_plot(runner.mon.ts, runner.mon['V'], legend='V')

# run 2
Iext, duration = bp.inputs.section_input([0.5], [200], return_length=True)
runner.run(duration, dyn_args=dict(Iext=Iext))
bp.visualize.line_plot(runner.mon.ts, runner.mon['V'], legend='V-run2', show=True)

Enable call a customized function during fitting of `brainpy.BPTrainer`.#

This customized function (provided through fun_after_report) will be useful to save a checkpoint during the training. For instance,

class CheckPoint:
    def __init__(self, path='path/to/directory/'):
        self.max_acc = 0.
        self.path = path
        
    def __call__(self, idx, metrics, phase):
        if phase == 'test' and metrics['acc'] > self.max_acc:
            self.max_acc = matrics['acc']
            bp.checkpoints.save(self.path, net.state_dict(), idx)

trainer = bp.BPTT()
trainer.fit(..., fun_after_report=CheckPoint())    

Enable data with `data_first_axis` format when predicting or fitting in a `brainpy.DSRunner` and `brainpy.DSTrainer`.#

Previous version of BrainPy only supports data with the batch dimension at the first axis. Currently, brainpy.DSRunner and brainpy.DSTrainer can support the data with the time dimension at the first axis. This can be set through data_first_axis='T' when initializing a runner or trainer.

runner = bp.DSRunner(..., data_first_axis='T')
trainer = bp.DSTrainer(..., data_first_axis='T')

4. Utility in BrainPy#

`brainpy.encoding` module for encoding rate values into spike trains#

Currently, we support

brainpy.encoding.LatencyEncoder
brainpy.encoding.PoissonEncoder
brainpy.encoding.WeightedPhaseEncoder

`brainpy.checkpoints` module for model state serialization.#

This version of BrainPy supports to save a checkpoint of the model into the physical disk. Inspired from the Flax API, we provide the following checkpoint APIs:

brainpy.checkpoints.save() for saving a checkpoint of the model.
brainpy.checkpoints.multiprocess_save() for saving a checkpoint of the model in multi-process environment.
brainpy.checkpoints.load() for loading the last or best checkpoint from the given checkpoint path.
brainpy.checkpoints.load_latest() for retrieval the path of the latest checkpoint in a directory.

Deprecations#

1. Deprecations in the running supports of BrainPy#

`func_monitors` is no longer supported in all `brainpy.Runner` subclasses.#

We will remove its supports since version 2.4.0. Instead, monitoring with a dict of callable functions can be set in monitors. For example,

# old version

runner = bp.DSRunner(model, 
                     monitors={'sps': model.spike, 'vs': model.V},
                     func_monitors={'sp10': model.spike[10]})

# new version
runner = bp.DSRunner(model, 
                     monitors={'sps': model.spike, 
                               'vs': model.V, 
                               'sp10': model.spike[10]})

`func_inputs` is no longer supported in all `brainpy.Runner` subclasses.#

Instead, giving inputs with a callable function should be done with inputs.

# old version

net = EINet()

def f_input(tdi):
    net.E.input += 10.

runner = bp.DSRunner(net, fun_inputs=f_input, inputs=('I.input', 10.))

# new version

def f_input(tdi):
    net.E.input += 10.
    net.I.input += 10.
runner = bp.DSRunner(net, inputs=f_input)

`inputs_are_batching` is deprecated.#

inputs_are_batching is deprecated in predict()/.run() of all brainpy.Runner subclasses.

`args` and `dyn_args` are now deprecated in `IntegratorRunner`.#

Instead, users should specify args and dyn_args when using IntegratorRunner.run() function.

dV = lambda V, t, w, I: V - V * V * V / 3 - w + I
dw = lambda w, t, V, a, b: (V + a - b * w) / 12.5
integral = bp.odeint(bp.JointEq([dV, dw]), method='exp_auto')

# old version
runner = bp.IntegratorRunner(
  integral,
  monitors=['V', 'w'], 
  inits={'V': bm.random.rand(10), 'w': bm.random.normal(size=10)},
  args={'a': 1., 'b': 1.},  # CHANGE
  dyn_args={'I': bp.inputs.ramp_input(0, 4, 100)},  # CHANGE
)
runner.run(100.,)

# new version
runner = bp.IntegratorRunner(
  integral,
  monitors=['V', 'w'], 
  inits={'V': bm.random.rand(10), 'w': bm.random.normal(size=10)},
)
runner.run(100., 
           args={'a': 1., 'b': 1.},
           dyn_args={'I': bp.inputs.ramp_input(0, 4, 100)})

2. Deprecations in `brainpy.math` module#

`ditype()` and `dftype()` are deprecated.#

brainpy.math.ditype() and brainpy.math.dftype() are deprecated. Using brainpy.math.int_ and brainpy.math.float() instead.

`brainpy.modes` module is now moved into `brainpy.math`#

The correspondences are listed as the follows:

brainpy.modes.Mode => brainpy.math.Mode
brainpy.modes.NormalMode => brainpy.math.NonBatchingMode
brainpy.modes.BatchingMode => brainpy.math.BatchingMode
brainpy.modes.TrainingMode => brainpy.math.TrainingMode
brainpy.modes.normal => brainpy.math.nonbatching_mode
brainpy.modes.batching => brainpy.math.batching_mode
brainpy.modes.training => brainpy.math.training_mode

Version 2.3.0#

This branch of releases aims to provide a unified computing framework for brain simulation and brain-inspired computing.

New features#

brainpy.BPTT supports train_data and test_data with general Python iterators. For instance, one can train a model with PyTorch dataloader or TensorFlow datasets.

import torchvision
from torch.utils.data import DataLoader
data = torchvision.datasets.CIFAR10("./CIFAR10", train=False, transform=torchvision.transforms.ToTensor())
loader = DataLoader(dataset=data, batch_size=4, shuffle=True, num_workers=0, drop_last=False)

# any generator can be used for train_data or test_data
trainer = bp.BPTT()
trainer.fit(loader)

Consolidated object-oriented transformation in brainpy.math.object_transform module. All brainpy transformations generate a new BrainPyObject instance so that objects in brainpy can be composed hierarchically. brainpy.math.to_object() transformation transforms a pure Python function into a BrainPyObject.
New documentation is currently online for introducing the consolidated BrainPy concept of object-oriented transformation.
Change brainpy.math.JaxArray to brainpy.math.Array.

Deprecations#

brainpy.datasets module is no longer supported. New APIs will be moved into brainpy-datasets package.
brainpy.train.BPTT no longer support to receive the train data [X, Y]. Instead, users should provide a data generator such like pytorch dataset or tensorflow dataset.
The update function of brainpy.math.TimeDealy does not support receiving a time index. Instead, one can update the new data by directly using TimeDealy.update(data) instead of TimeDealy.update(time, data).
Fix the monitoring error of delay differential equations with brainpy.integrators.IntegratorRunner.

Bug Fixes#

Fix the bug on One2One connection.
Fix the bug in eprop example.
Fix ij2csr transformation error.
Fix test bugs

What’s Changed#

fix eprop example error by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/305
minor updates on API and DOC by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/306
Add new optimizers by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/307
add documentation of for random number generation by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/308
consolidate the concept of OO transformation by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/309
Upgrade documetations by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/310
Ready for publish by @chaoming0625 in https://github.com/brainpy/BrainPy/pull/311

Full Changelog: https://github.com/brainpy/BrainPy/compare/V2.2.4.0…V2.3.0

brainpy 2.2.x#

BrainPy 2.2.x is a complete re-design of the framework, tackling the shortcomings of brainpy 2.1.x generation, effectively bringing it to research needs and standards.

Version 2.2.4#

This release has updated many functionalities and fixed several bugs in BrainPy.

New Features#

More ANN layers, including brainpy.layers.Flatten and brainpy.layers.Activation.
Optimized connection building for brainpy.connect module.
cifar dataset.
Enhanced API and Doc for parallel simulations via brainpy.running.cpu_ordered_parallel, brainpy.running.cpu_unordered_parallel, brainpy.running.jax_vectorize_map and brainpy.running.jax_parallelize_map.

What’s Changed#

add Activation and Flatten class by @LuckyHFC in https://github.com/PKU-NIP-Lab/BrainPy/pull/291
optimizes the connect time when using gpu by @MamieZhu in https://github.com/PKU-NIP-Lab/BrainPy/pull/293
datasets::vision: add cifar dataset by @hbelove in https://github.com/PKU-NIP-Lab/BrainPy/pull/292
fix #294: remove VariableView in dyn_vars of a runner by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/295
update issue template by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/296
add multiprocessing functions for batch running of BrainPy functions by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/298
upgrade connection apis by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/299
fix #300: update parallelization api documentation by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/302
update doc by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/303

New Contributors#

@LuckyHFC made their first contribution in https://github.com/PKU-NIP-Lab/BrainPy/pull/291
@MamieZhu made their first contribution in https://github.com/PKU-NIP-Lab/BrainPy/pull/293
@hbelove made their first contribution in https://github.com/PKU-NIP-Lab/BrainPy/pull/292

Full Changelog: https://github.com/PKU-NIP-Lab/BrainPy/compare/V2.2.3.6…V2.2.4

Version 2.2.1 (2022.09.09)#

This release fixes bugs found in the codebase and improves the usability and functions of BrainPy.

Bug fixes#

Fix the bug of operator customization in brainpy.math.XLACustomOp and brainpy.math.register_op. Now, it supports operator customization by using NumPy and Numba interface. For instance,

import brainpy.math as bm

def abs_eval(events, indices, indptr, post_val, values):
      return post_val

def con_compute(outs, ins):
      post_val = outs
      events, indices, indptr, _, values = ins
      for i in range(events.size):
        if events[i]:
          for j in range(indptr[i], indptr[i + 1]):
            index = indices[j]
            old_value = post_val[index]
            post_val[index] = values + old_value

event_sum = bm.XLACustomOp(eval_shape=abs_eval, con_compute=con_compute)

Fix the bug of brainpy.tools.DotDict. Now, it is compatible with the transformations of JAX. For instance,

import brainpy as bp
from jax import vmap

@vmap
def multiple_run(I):
  hh = bp.neurons.HH(1)
  runner = bp.dyn.DSRunner(hh, inputs=('input', I), numpy_mon_after_run=False)
  runner.run(100.)
  return runner.mon

mon = multiple_run(bp.math.arange(2, 10, 2))

New features#

Add numpy operators brainpy.math.mat, brainpy.math.matrix, brainpy.math.asmatrix.
Improve translation rules of brainpylib operators, improve its running speeds.
Support DSView of DynamicalSystem instance. Now, it supports defining models with a slice view of a DS instance. For example,

import brainpy as bp
import brainpy.math as bm


class EINet_V2(bp.dyn.Network):
  def __init__(self, scale=1.0, method='exp_auto'):
    super(EINet_V2, self).__init__()

    # network size
    num_exc = int(3200 * scale)
    num_inh = int(800 * scale)

    # neurons
    self.N = bp.neurons.LIF(num_exc + num_inh,
                            V_rest=-60., V_th=-50., V_reset=-60., tau=20., tau_ref=5.,
                            method=method, V_initializer=bp.initialize.Normal(-55., 2.))

    # synapses
    we = 0.6 / scale  # excitatory synaptic weight (voltage)
    wi = 6.7 / scale  # inhibitory synaptic weight
    self.Esyn = bp.synapses.Exponential(pre=self.N[:num_exc], post=self.N,
                                        conn=bp.connect.FixedProb(0.02),
                                        g_max=we, tau=5.,
                                        output=bp.synouts.COBA(E=0.),
                                        method=method)
    self.Isyn = bp.synapses.Exponential(pre=self.N[num_exc:], post=self.N,
                                        conn=bp.connect.FixedProb(0.02),
                                        g_max=wi, tau=10.,
                                        output=bp.synouts.COBA(E=-80.),
                                        method=method)

net = EINet_V2(scale=1., method='exp_auto')
# simulation
runner = bp.dyn.DSRunner(
    net,
    monitors={'spikes': net.N.spike},
    inputs=[(net.N.input, 20.)]
  )
runner.run(100.)

# visualization
bp.visualize.raster_plot(runner.mon.ts, runner.mon['spikes'], show=True)

Version 2.2.0 (2022.08.12)#

This release has provided important improvements for BrainPy, including usability, speed, functions, and others.

Backwards Incompatible changes#

brainpy.nn module is no longer supported and has been removed since version 2.2.0. Instead, users should use brainpy.train module for the training of BP algorithms, online learning, or offline learning algorithms, and brainpy.algorithms module for online / offline training algorithms.
The update() function for the model definition has been changed:

>>> # 2.1.x
>>>
>>> import brainpy as bp
>>>
>>> class SomeModel(bp.dyn.DynamicalSystem):
>>>      def __init__(self, ):
>>>            ......
>>>      def update(self, t, dt):
>>>           pass
>>> # 2.2.x
>>>
>>> import brainpy as bp
>>>
>>> class SomeModel(bp.dyn.DynamicalSystem):
>>>      def __init__(self, ):
>>>            ......
>>>      def update(self, tdi):
>>>           t, dt = tdi.t, tdi.dt
>>>           pass

where tdi can be defined with other names, like sha, to represent the shared argument across modules.

Deprecations#

brainpy.dyn.xxx (neurons) and brainpy.dyn.xxx (synapse) are no longer supported. Please use brainpy.neurons, brainpy.synapses modules.
brainpy.running.monitor has been removed.
brainpy.nn module has been removed.

New features#

brainpy.math.Variable receives a batch_axis setting to represent the batch axis of the data.

>>> import brainpy.math as bm
>>> a = bm.Variable(bm.zeros((1, 4, 5)), batch_axis=0)
>>> a.value = bm.zeros((2, 4, 5))  # success
>>> a.value = bm.zeros((1, 2, 5))  # failed
MathError: The shape of the original data is (2, 4, 5), while we got (1, 2, 5) with batch_axis=0.

brainpy.train provides brainpy.train.BPTT for back-propagation algorithms, brainpy.train.Onlinetrainer for online training algorithms, brainpy.train.OfflineTrainer for offline training algorithms.
brainpy.Base class supports _excluded_vars setting to ignore variables when retrieving variables by using Base.vars() method.

>>> class OurModel(bp.Base):
>>>     _excluded_vars = ('a', 'b')
>>>     def __init__(self):
>>>         super(OurModel, self).__init__()
>>>         self.a = bm.Variable(bm.zeros(10))
>>>         self.b = bm.Variable(bm.ones(20))
>>>         self.c = bm.Variable(bm.random.random(10))
>>>
>>> model = OurModel()
>>> model.vars().keys()
dict_keys(['OurModel0.c'])

brainpy.analysis.SlowPointFinder supports directly analyzing an instance of brainpy.dyn.DynamicalSystem.

>>> hh = bp.neurons.HH(1)
>>> finder = bp.analysis.SlowPointFinder(hh, target_vars={'V': hh.V, 'm': hh.m, 'h': hh.h, 'n': hh.n})

brainpy.datasets supports MNIST, FashionMNIST, and other datasets.
Supports defining conductance-based neuron models``.

>>> class HH(bp.dyn.CondNeuGroup):
>>>   def __init__(self, size):
>>>     super(HH, self).__init__(size)
>>>
>>>     self.INa = channels.INa_HH1952(size, )
>>>     self.IK = channels.IK_HH1952(size, )
>>>     self.IL = channels.IL(size, E=-54.387, g_max=0.03)

brainpy.layers module provides commonly used models for DNN and reservoir computing.
Support composable definition of synaptic models by using TwoEndConn, SynOut, SynSTP and SynLTP.

>>> bp.synapses.Exponential(self.E, self.E, bp.conn.FixedProb(prob),
>>>                      g_max=0.03 / scale, tau=5,
>>>                      output=bp.synouts.COBA(E=0.),
>>>                      stp=bp.synplast.STD())

Provide commonly used surrogate gradient function for spiking generation, including
- brainpy.math.spike_with_sigmoid_grad
- brainpy.math.spike_with_linear_grad
- brainpy.math.spike_with_gaussian_grad
- brainpy.math.spike_with_mg_grad
Provide shortcuts for GPU memory management via brainpy.math.disable_gpu_memory_preallocation(), and brainpy.math.clear_buffer_memory().

What’s Changed#

fix #207: synapses update first, then neurons, finally delay variables by @chaoming0625 in #219
docs: add logos by @ztqakita in #218
Add the biological NMDA model by @c-xy17 in #221
docs: fix mathjax problem by @ztqakita in #222
Add the parameter R to the LIF model by @c-xy17 in #224
new version of brainpy: V2.2.0-rc1 by @chaoming0625 in #226
update training apis by @chaoming0625 in #227
Update quickstart and the analysis module by @c-xy17 in #229
Eseential updates for montors, analysis, losses, and examples by @chaoming0625 in #230
add numpy op tests by @ztqakita in #231
Integrated simulation, simulaton and analysis by @chaoming0625 in #232
update docs by @chaoming0625 in #233
unify brainpy.layers with other modules in brainpy.dyn by @chaoming0625 in #234
fix bugs by @chaoming0625 in #235
update apis, docs, examples and others by @chaoming0625 in #236
fixes by @chaoming0625 in #237
fix: add dtype promotion = standard by @ztqakita in #239
updates by @chaoming0625 in #240
update training docs by @chaoming0625 in #241
change doc path/organization by @chaoming0625 in #242
Update advanced docs by @chaoming0625 in #243
update quickstart docs & enable jit error checking by @chaoming0625 in #244
update apis and examples by @chaoming0625 in #245
update apis and tests by @chaoming0625 in #246
Docs update and bugs fixed by @ztqakita in #247
version 2.2.0 by @chaoming0625 in #248
add norm and pooling & fix bugs in operators by @ztqakita in #249

Full Changelog: V2.1.12…V2.2.0

brainpy 2.1.x#

Version 2.1.12 (2022.05.17)#

Highlights#

This release is excellent. We have made important improvements.

We provide dozens of random sampling in NumPy which are not supportted in JAX, such as brainpy.math.random.bernoulli, brainpy.math.random.lognormal, brainpy.math.random.binomial, brainpy.math.random.chisquare, brainpy.math.random.dirichlet, brainpy.math.random.geometric, brainpy.math.random.f, brainpy.math.random.hypergeometric, brainpy.math.random.logseries, brainpy.math.random.multinomial, brainpy.math.random.multivariate_normal, brainpy.math.random.negative_binomial, brainpy.math.random.noncentral_chisquare, brainpy.math.random.noncentral_f, brainpy.math.random.power, brainpy.math.random.rayleigh, brainpy.math.random.triangular, brainpy.math.random.vonmises, brainpy.math.random.wald, brainpy.math.random.weibull
make efficient checking on numerical values. Instead of direct id_tap() checking which has large overhead, currently brainpy.tools.check_erro_in_jit() is highly efficient.
Fix JaxArray operator errors on None
improve oo-to-function transformation speeds
io works: .save_states() and .load_states()

What’s Changed#

support dtype setting in array interchange functions by [@chaoming0625](chaoming0625) in #209
fix #144: operations on None raise errors by [@chaoming0625](chaoming0625) in #210
add tests and new functions for random sampling by [@c-xy17](c-xy17) in #213
feat: fix io for brainpy.Base by [@chaoming0625](chaoming0625) in #211
update advanced tutorial documentation by [@chaoming0625](chaoming0625) in #212
fix #149 (dozens of random samplings in NumPy) and fix JaxArray op errors by [@chaoming0625](chaoming0625) in #216
feat: efficient checking on numerical values by [@chaoming0625](chaoming0625) in #217

Full Changelog: V2.1.11…V2.1.12

Version 2.1.11 (2022.05.15)#

What’s Changed#

fix: cross-correlation bug by @ztqakita in #201
update apis, test and docs of numpy ops by @chaoming0625 in #202
docs: add sphinx_book_theme by @ztqakita in #203
fix: add requirements-doc.txt by @ztqakita in #204
update control flow, integrators, operators, and docs by @chaoming0625 in #205
improve oo-to-function transformation speed by @chaoming0625 in #208

Full Changelog: V2.1.10…V2.1.11

Version 2.1.10 (2022.05.05)#

What’s Changed#

update control flow APIs and Docs by @chaoming0625 in #192
doc: update docs of dynamics simulation by @chaoming0625 in #193
fix #125: add channel models and two-compartment Pinsky-Rinzel model by @chaoming0625 in #194
JIT errors do not change Variable values by @chaoming0625 in #195
fix a bug in math.activations.py by @c-xy17 in #196
Functionalinaty improvements by @chaoming0625 in #197
update rate docs by @chaoming0625 in #198
update brainpy.dyn doc by @chaoming0625 in #199

Full Changelog: V2.1.8…V2.1.10

Version 2.1.8 (2022.04.26)#

What’s Changed#

Fix #120 by @chaoming0625 in #178
feat: brainpy.Collector supports addition and subtraction by @chaoming0625 in #179
feat: delay variables support “indices” and “reset()” function by @chaoming0625 in #180
Support reset functions in neuron and synapse models by @chaoming0625 in #181
update() function on longer need _t and _dt by @chaoming0625 in #183
small updates by @chaoming0625 in #188
feat: easier control flows with brainpy.math.ifelse by @chaoming0625 in #189
feat: update delay couplings of DiffusiveCoupling and AdditiveCouping by @chaoming0625 in #190
update version and changelog by @chaoming0625 in #191

Full Changelog: V2.1.7…V2.1.8

Version 2.1.7 (2022.04.22)#

What’s Changed#

synapse models support heterogeneuos weights by @chaoming0625 in #170
more efficient synapse implementation by @chaoming0625 in #171
fix input models in brainpy.dyn by @chaoming0625 in #172
fix: np array astype by @ztqakita in #173
update README: ‘brain-py’ to ‘brainpy’ by @chaoming0625 in #174
fix: fix the updating rules in the STP model by @c-xy17 in #176
Updates and fixes by @chaoming0625 in #177

Full Changelog: V2.1.5…V2.1.7

Version 2.1.5 (2022.04.18)#

What’s Changed#

brainpy.math.random.shuffle is numpy like by @chaoming0625 in #153
update LICENSE by @chaoming0625 in #155
docs: add m1 warning by @ztqakita in #154
compatible apis of ‘brainpy.math’ with those of ‘jax.numpy’ in most modules by @chaoming0625 in #156
Important updates by @chaoming0625 in #157
Updates by @chaoming0625 in #159
Add LayerNorm, GroupNorm, and InstanceNorm as nn_nodes in normalization.py by @c-xy17 in #162
feat: add conv & pooling nodes by @ztqakita in #161
fix: update setup.py by @ztqakita in #163
update setup.py by @chaoming0625 in #165
fix: change trigger condition by @ztqakita in #166
fix: add build_conn() function by @ztqakita in #164
update synapses by @chaoming0625 in #167
get the deserved name: brainpy by @chaoming0625 in #168
update tests by @chaoming0625 in #169

Full Changelog: V2.1.4…V2.1.5

Version 2.1.4 (2022.04.04)#

What’s Changed#

fix doc parsing bug by @chaoming0625 in #127
Update overview_of_dynamic_model.ipynb by @c-xy17 in #129
Reorganization of brainpylib.custom_op and adding interface in brainpy.math by @ztqakita in #128
Fix: modify register_op and brainpy.math interface by @ztqakita in #130
new features about RNN training and delay differential equations by @chaoming0625 in #132
Fix #123: Add low-level operators docs and modify register_op by @ztqakita in #134
feat: add generate_changelog by @ztqakita in #135
fix #133, support batch size training with offline algorithms by @chaoming0625 in #136
fix #84: support online training algorithms by @chaoming0625 in #137
feat: add the batch normalization node by @c-xy17 in #138
fix: fix shape checking error by @chaoming0625 in #139
solve #131, support efficient synaptic computation for special connection types by @chaoming0625 in #140
feat: update the API and test for batch normalization by @c-xy17 in #142
Node is default trainable by @chaoming0625 in #143
Updates training apis and docs by @chaoming0625 in #145
fix: add dependencies and update version by @ztqakita in #147
update requirements by @chaoming0625 in #146
data pass of the Node is default SingleData by @chaoming0625 in #148

Full Changelog: V2.1.3…V2.1.4

Version 2.1.3 (2022.03.27)#

This release improves the functionality and usability of BrainPy. Core changes include

support customization of low-level operators by using Numba
fix bugs

What’s Changed#

Provide custom operators written in numba for jax jit by @ztqakita in #122
fix DOGDecay bugs, add more features by @chaoming0625 in #124
fix bugs by @chaoming0625 in #126

Full Changelog : V2.1.2…V2.1.3

Version 2.1.2 (2022.03.23)#

This release improves the functionality and usability of BrainPy. Core changes include

support rate-based whole-brain modeling
add more neuron models, including rate neurons/synapses
support Python 3.10
improve delays etc. APIs

What’s Changed#

fix matplotlib dependency on “brainpy.analysis” module by @chaoming0625 in #110
Sync master to brainpy-2.x branch by @ztqakita in #111
add py3.6 test & delete multiple macos env by @ztqakita in #112
Modify ci by @ztqakita in #113
Add py3.10 test by @ztqakita in #115
update python version by @chaoming0625 in #114
add brainpylib mac py3.10 by @ztqakita in #116
Enhance measure/input/brainpylib by @chaoming0625 in #117
fix #105: Add customize connections docs by @ztqakita in #118
fix bugs by @chaoming0625 in #119
Whole brain modeling by @chaoming0625 in #121

Full Changelog: V2.1.1…V2.1.2

Version 2.1.1 (2022.03.18)#

This release continues to update the functionality of BrainPy. Core changes include

numerical solvers for fractional differential equations
more standard brainpy.nn interfaces

New Features#

Numerical solvers for fractional differential equations

brainpy.fde.CaputoEuler

brainpy.fde.CaputoL1Schema
brainpy.fde.GLShortMemory

Fractional neuron models

brainpy.dyn.FractionalFHR

brainpy.dyn.FractionalIzhikevich
support shared_kwargs in [RNNTrainer]{.title-ref} and [RNNRunner]{.title-ref}

Version 2.1.0 (2022.03.14)#

Highlights#

We are excited to announce the release of BrainPy 2.1.0. This release is composed of nearly 270 commits since 2.0.2, made by Chaoming Wang, Xiaoyu Chen, and Tianqiu Zhang .

BrainPy 2.1.0 updates are focused on improving usability, functionality, and stability of BrainPy. Highlights of version 2.1.0 include:

New module brainpy.dyn for dynamics building and simulation. It is composed of many neuron models, synapse models, and others.
New module brainpy.nn for neural network building and training. It supports to define reservoir models, artificial neural networks, ridge regression training, and back-propagation through time training.
New module brainpy.datasets for convenient dataset construction and initialization.
New module brainpy.integrators.dde for numerical integration of delay differential equations.
Add more numpy-like operators in brainpy.math module.
Add automatic continuous integration on Linux, Windows, and MacOS platforms.
Fully update brainpy documentation.
Fix bugs on brainpy.analysis and brainpy.math.autograd

Incompatible changes#

Remove brainpy.math.numpy module.
Remove numba requirements
Remove matplotlib requirements
Remove [steps]{.title-ref} in brainpy.dyn.DynamicalSystem
Remove travis CI

New Features#

brainpy.ddeint for numerical integration of delay differential equations, the supported methods include: - Euler - MidPoint - Heun2 - Ralston2 - RK2 - RK3 - Heun3 - Ralston3 - SSPRK3 - RK4 - Ralston4 - RK4Rule38

set default int/float/complex types

brainpy.math.set_dfloat()

brainpy.math.set_dint()
brainpy.math.set_dcomplex()

Delay variables

brainpy.math.FixedLenDelay

brainpy.math.NeutralDelay

Dedicated operators

brainpy.math.sparse_matmul()

More numpy-like operators
Neural network building brainpy.nn
Dynamics model building and simulation brainpy.dyn

Version 2.0.2 (2022.02.11)#

There are important updates by Chaoming Wang in BrainPy 2.0.2.

provide pre2post_event_prod operator
support array creation from a list/tuple of JaxArray in brainpy.math.asarray and brainpy.math.array
update brainpy.ConstantDelay, add .latest and .oldest attributes
add brainpy.IntegratorRunner support for efficient simulation of brainpy integrators
support auto finding of RandomState when JIT SDE integrators
fix bugs in SDE exponential_euler method
move parallel running APIs into brainpy.simulation
add brainpy.math.syn2post_mean, brainpy.math.syn2post_softmax, brainpy.math.pre2post_mean and brainpy.math.pre2post_softmax operators

Version 2.0.1 (2022.01.31)#

Today we release BrainPy 2.0.1. This release is composed of over 70 commits since 2.0.0, made by Chaoming Wang, Xiaoyu Chen, and Tianqiu Zhang .

BrainPy 2.0.0 updates are focused on improving documentation and operators. Core changes include:

Improve brainpylib operators
Complete documentation for programming system
Add more numpy APIs
Add jaxfwd in autograd module
And other changes

Version 2.0.0.1 (2022.01.05)#

Add progress bar in brainpy.StructRunner

Version 2.0.0 (2021.12.31)#

Start a new version of BrainPy.

Highlight#

We are excited to announce the release of BrainPy 2.0.0. This release is composed of over 260 commits since 1.1.7, made by Chaoming Wang, Xiaoyu Chen, and Tianqiu Zhang .

BrainPy 2.0.0 updates are focused on improving performance, usability and consistence of BrainPy. All the computations are migrated into JAX. Model building, simulation, training and analysis are all based on JAX. Highlights of version 2.0.0 include:

brainpylib are provided to dedicated operators for brain dynamics programming
Connection APIs in brainpy.conn module are more efficient.
Update analysis tools for low-dimensional and high-dimensional systems in brainpy.analysis module.
Support more general Exponential Euler methods based on automatic differentiation.
Improve the usability and consistence of brainpy.math module.
Remove JIT compilation based on Numba.
Separate brain building with brain simulation.

Incompatible changes#

remove brainpy.math.use_backend()
remove brainpy.math.numpy module
no longer support .run() in brainpy.DynamicalSystem (see New Features)
remove brainpy.analysis.PhasePlane (see New Features)
remove brainpy.analysis.Bifurcation (see New Features)
remove brainpy.analysis.FastSlowBifurcation (see New Features)

New Features#

Exponential Euler method based on automatic differentiation

brainpy.ode.ExpEulerAuto

Numerical optimization based low-dimensional analyzers:

brainpy.analysis.PhasePlane1D

brainpy.analysis.PhasePlane2D
brainpy.analysis.Bifurcation1D
brainpy.analysis.Bifurcation2D
brainpy.analysis.FastSlow1D
brainpy.analysis.FastSlow2D

Numerical optimization based high-dimensional analyzer:

brainpy.analysis.SlowPointFinder

Dedicated operators in brainpy.math module:

brainpy.math.pre2post_event_sum

brainpy.math.pre2post_sum
brainpy.math.pre2post_prod
brainpy.math.pre2post_max
brainpy.math.pre2post_min
brainpy.math.pre2syn
brainpy.math.syn2post
brainpy.math.syn2post_prod
brainpy.math.syn2post_max
brainpy.math.syn2post_min

Conversion APIs in brainpy.math module:

brainpy.math.as_device_array()

brainpy.math.as_variable()
brainpy.math.as_jaxarray()

New autograd APIs in brainpy.math module:

brainpy.math.vector_grad()

Simulation runners:

brainpy.ReportRunner

brainpy.StructRunner
brainpy.NumpyRunner

Commonly used models in brainpy.models module

brainpy.models.LIF

brainpy.models.Izhikevich
brainpy.models.AdExIF
brainpy.models.SpikeTimeInput
brainpy.models.PoissonInput
brainpy.models.DeltaSynapse
brainpy.models.ExpCUBA
brainpy.models.ExpCOBA
brainpy.models.AMPA
brainpy.models.GABAa
Naming cache clean: brainpy.clear_name_cache
add safe in-place operations of update() method and .value assignment for JaxArray

Documentation#

Complete tutorials for quickstart
Complete tutorials for dynamics building
Complete tutorials for dynamics simulation
Complete tutorials for dynamics training
Complete tutorials for dynamics analysis
Complete tutorials for API documentation

brainpy 1.1.x#

If you are using brainpy==1.x, you can find documentation, examples, and models through the following links:

Documentation: https://brainpy.readthedocs.io/en/brainpy-1.x/
Examples from papers: https://brainpy-examples.readthedocs.io/en/brainpy-1.x/
Canonical brain models: https://brainmodels.readthedocs.io/en/brainpy-1.x/

Version 1.1.7 (2021.12.13)#

fix bugs on numpy_array() conversion in [brainpy.math.utils]{.title-ref} module

Version 1.1.5 (2021.11.17)#

API changes:

fix bugs on ndarray import in [brainpy.base.function.py]{.title-ref}
convenient ‘get_param’ interface [brainpy.simulation.layers]{.title-ref}
add more weight initialization methods

Doc changes:

add more examples in README

Version 1.1.4#

API changes:

add .struct_run() in DynamicalSystem
add numpy_array() conversion in [brainpy.math.utils]{.title-ref} module
add Adagrad, Adadelta, RMSProp optimizers
remove [setting]{.title-ref} methods in [brainpy.math.jax]{.title-ref} module
remove import jax in [brainpy.__init__.py]{.title-ref} and enable jax setting, including
- enable_x64()
- set_platform()
- set_host_device_count()
enable b=None as no bias in [brainpy.simulation.layers]{.title-ref}
set [int_]{.title-ref} and [float_]{.title-ref} as default 32 bits
remove dtype setting in Initializer constructor

Doc changes:

add optimizer in “Math Foundation”
add dynamics training docs
improve others

Version 1.1.3#

fix bugs of JAX parallel API imports
fix bugs of [post_slice]{.title-ref} structure construction
update docs

Version 1.1.2#

add pre2syn and syn2post operators
add [verbose]{.title-ref} and [check]{.title-ref} option to Base.load_states()
fix bugs on JIT DynamicalSystem (numpy backend)

Version 1.1.1#

fix bugs on symbolic analysis: model trajectory
change [absolute]{.title-ref} access in the variable saving and loading to the [relative]{.title-ref} access
add UnexpectedTracerError hints in JAX transformation functions

Version 1.1.0 (2021.11.08)#

This package releases a new version of BrainPy.

Highlights of core changes:

`math` module#

support numpy backend
support JAX backend
support jit, vmap and pmap on class objects on JAX backend
support grad, jacobian, hessian on class objects on JAX backend
support make_loop, make_while, and make_cond on JAX backend
support jit (based on numba) on class objects on numpy backend
unified numpy-like ndarray operation APIs
numpy-like random sampling APIs
FFT functions
gradient descent optimizers
activation functions
loss function
backend settings

`base` module#

Base for whole Version ecosystem
Function to wrap functions
Collector and TensorCollector to collect variables, integrators, nodes and others

`integrators` module#

class integrators for ODE numerical methods
class integrators for SDE numerical methods

`simulation` module#

support modular and composable programming
support multi-scale modeling
support large-scale modeling
support simulation on GPUs
fix bugs on firing_rate()
remove _i in update() function, replace _i with _dt, meaning the dynamic system has the canonic equation form of \(dx/dt = f(x, t, dt)\)
reimplement the input_step and monitor_step in a more intuitive way
support to set [dt]{.title-ref} in the single object level (i.e., single instance of DynamicSystem)
common used DNN layers
weight initializations
refine synaptic connections

brainpy 1.0.x#

Version 1.0.3 (2021.08.18)#

Fix bugs on

firing rate measurement
stability analysis

Version 1.0.2#

This release continues to improve the user-friendliness.

Highlights of core changes:

Remove support for Numba-CUDA backend
Super initialization [super(XXX, self).__init__()]{.title-ref} can be done at anywhere (not required to add at the bottom of the [__init__()]{.title-ref} function).
Add the output message of the step function running error.
More powerful support for Monitoring
More powerful support for running order scheduling
Remove [unsqueeze()]{.title-ref} and [squeeze()]{.title-ref} operations in brainpy.ops
Add [reshape()]{.title-ref} operation in brainpy.ops
Improve docs for numerical solvers
Improve tests for numerical solvers
Add keywords checking in ODE numerical solvers
Add more unified operations in brainpy.ops
Support “@every” in steps and monitor functions
Fix ODE solver bugs for class bounded function
Add build phase in Monitor

Version 1.0.1#

Fix bugs

Version 1.0.0#

NEW VERSION OF BRAINPY
Change the coding style into the object-oriented programming
Systematically improve the documentation

brainpy 0.x#

Version 0.3.5#

Add ‘timeout’ in sympy solver in neuron dynamics analysis
Reconstruct and generalize phase plane analysis
Generalize the repeat mode of Network to different running duration between two runs
Update benchmarks
Update detailed documentation

Version 0.3.1#

Add a more flexible way for NeuState/SynState initialization
Fix bugs of “is_multi_return”
Add “hand_overs”, “requires” and “satisfies”.
Update documentation
Auto-transform [range]{.title-ref} to [numba.prange]{.title-ref}
Support [_obj_i]{.title-ref}, [_pre_i]{.title-ref}, [_post_i]{.title-ref} for more flexible operation in scalar-based models

Version 0.3.0#

Computation API#

Rename “brainpy.numpy” to “brainpy.backend”
Delete “pytorch”, “tensorflow” backends
Add “numba” requirement
Add GPU support

Profile setting#

Delete “backend” profile setting, add “jit”

Core systems#

Delete “autopepe8” requirement
Delete the format code prefix
Change keywords “_t, _dt, _i” to “_t, _dt, _i”
Change the “ST” declaration out of “requires”
Add “repeat” mode run in Network
Change “vector-based” to “mode” in NeuType and SynType definition

Package installation#

Remove “pypi” installation, installation now only rely on “conda”

Version 0.2.4#

API changes#

Fix bugs

Version 0.2.3#

API changes#

Add “animate_1D” in visualization module
Add “PoissonInput”, “SpikeTimeInput” and “FreqInput” in inputs module
Update phase_portrait_analyzer.py

Models and examples#

Add CANN examples

Version 0.2.2#

API changes#

Redesign visualization
Redesign connectivity
Update docs

Version 0.2.1#

API changes#

Fix bugs in [numba import]{.title-ref}
Fix bugs in [numpy]{.title-ref} mode with [scalar]{.title-ref} model

Version 0.2.0#

API changes#

For computation: numpy, numba
For model definition: NeuType, SynConn
For model running: Network, NeuGroup, SynConn, Runner
For numerical integration: integrate, Integrator, DiffEquation
For connectivity: One2One, All2All, GridFour, grid_four, GridEight, grid_eight, GridN, FixedPostNum, FixedPreNum, FixedProb, GaussianProb, GaussianWeight, DOG
For visualization: plot_value, plot_potential, plot_raster, animation_potential
For measurement: cross_correlation, voltage_fluctuation, raster_plot, firing_rate
For inputs: constant_current, spike_current, ramp_current.

Models and examples#

Neuron models: HH model, LIF model, Izhikevich model
Synapse models: AMPA, GABA, NMDA, STP, GapJunction
Network models: gamma oscillation

Release notes (brainpy)

Contents

Release notes (brainpy)#

brainpy>2.3.x#

Version 2.5.0#

New Features#

What’s Changed#

New Contributors#

Version 2.4.6#

New Features#

1. surrogate gradient functions are more transparent.#

2. Add brainpy.math.eval_shape for evaluating the all dynamical variables used in the target function.#

3. Generalize tools and interfaces for state managements.#

4. Unified brain simulation and brain-inspired computing interface through automatic membrane scaling.#

5. New apis for operator customization on CPU and GPU devices through brainpy.math.XLACustomOp.#

6. Generalized STDP models which are compatible with diverse synapse models.#

What’s Changed#

New Contributors#

Version 2.4.5#

New Features#

What’s Changed#

New Contributors#

Version 2.4.4#

What’s Changed#

New Contributors#

Version 2.4.3#

New Features#

What’s Changed#

New Contributors#

Version 2.4.2#

New Features#

What’s Changed#

New Contributors#

Version 2.4.1#

New Features#

What’s Changed#

New Contributors#

Version 2.4.0#

What’s New#

What’s Changed#

Version 2.3.8#

New Features#

What’s Changed#

Version 2.3.7#

Version 2.3.6#

New Features#

More flexible customization of surrogate gradient functions.#

Fix bugs#

Version 2.3.5#

New Features#

1. brainpy.share for sharing data across submodules#

2. brainpy.Delay for delay processing#

3. brainpy.checkpoints.save_pytree and brainpy.checkpoints.load_pytree for saving/loading target from the filename#

4. More ANN layers#

5. More compatible dense operators#

Others#

Version 2.3.4#

Version 2.3.3#

Version 2.3.2#

New Features#

1. New package structure for stable API release#

2. New schedulers#

3. Others#

Version 2.3.1#

Backwards Incompatible Changes#

1. Error: module ‘brainpy’ has no attribute ‘datasets’#

2. Error: DSTrainer must receive an instance with BatchingMode#

3. Error: inputs_are_batching is no longer supported.#

New Features#

1. brainpy.math module upgrade#

brainpy.math.surrogate module for surrogate gradient functions.#

New transformation function brainpy.math.to_dynsys#

Default data types#

Environment context manager#

2. brainpy.dyn module#

brainpy.dyn.transfom module for transforming a DynamicalSystem instance to a callable BrainPyObject.#

3. Running supports in BrainPy#

All brainpy.Runner now are subclasses of BrainPyObject#

Enable the continuous running of a differential equation (ODE, SDE, FDE, DDE, etc.) with IntegratorRunner.#

Enable call a customized function during fitting of brainpy.BPTrainer.#

Release notes (`brainpy`)#

2. Add `brainpy.math.eval_shape` for evaluating the all dynamical variables used in the target function.#

5. New apis for operator customization on CPU and GPU devices through `brainpy.math.XLACustomOp`.#

1. `brainpy.share` for sharing data across submodules#

2. `brainpy.Delay` for delay processing#

3. `brainpy.checkpoints.save_pytree` and `brainpy.checkpoints.load_pytree` for saving/loading target from the filename#

1. `brainpy.math` module upgrade#

`brainpy.math.surrogate` module for surrogate gradient functions.#

New transformation function `brainpy.math.to_dynsys`#

2. `brainpy.dyn` module#

`brainpy.dyn.transfom` module for transforming a `DynamicalSystem` instance to a callable `BrainPyObject`.#

All `brainpy.Runner` now are subclasses of `BrainPyObject`#

Enable the continuous running of a differential equation (ODE, SDE, FDE, DDE, etc.) with `IntegratorRunner`.#

Enable call a customized function during fitting of `brainpy.BPTrainer`.#

Enable data with `data_first_axis` format when predicting or fitting in a `brainpy.DSRunner` and `brainpy.DSTrainer`.#

`brainpy.encoding` module for encoding rate values into spike trains#

`brainpy.checkpoints` module for model state serialization.#

`func_monitors` is no longer supported in all `brainpy.Runner` subclasses.#

`func_inputs` is no longer supported in all `brainpy.Runner` subclasses.#

`inputs_are_batching` is deprecated.#

`args` and `dyn_args` are now deprecated in `IntegratorRunner`.#

2. Deprecations in `brainpy.math` module#

`ditype()` and `dftype()` are deprecated.#

`brainpy.modes` module is now moved into `brainpy.math`#