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Release notes (brainpy)

brainpy>2.3.x

Version 2.6.1

Breaking Changes

  • Fixing compatibility issues between numpy and jax

What's Changed

  • [doc] Add Chinese version of operator_custom_with_cupy.ipynb and Rename it's title by @Routhleck in #659
  • Fix "amsgrad" is used before being defined when initializing the AdamW optimizer by @CloudyDory in #660
  • fix issue #661 by @chaoming0625 in #662
  • fix flax RNN interoperation, fix #663 by @chaoming0625 in #665
  • [fix] Replace jax.experimental.host_callback with jax.pure_callback by @Routhleck in #670
  • [math] Update CustomOpByNumba to support JAX version >= 0.4.24 by @Routhleck in #669
  • [math] Fix CustomOpByNumba on multiple_results=True by @Routhleck in #671
  • [math] Implementing event-driven sparse matrix @ matrix operators by @Routhleck in #613
  • [math] Add getting JIT connect matrix method for brainpy.dnn.linear by @Routhleck in #672
  • [math] Add get JIT weight matrix methods(Uniform & Normal) for brainpy.dnn.linear by @Routhleck in #673
  • support Integrator.to_math_expr() by @chaoming0625 in #674
  • [bug] Replace collections.Iterable with collections.abc.Iterable by @Routhleck in #677
  • Fix surrogate gradient function and numpy 2.0 compatibility by @chaoming0625 in #679
  • ⬆️ Bump docker/build-push-action from 5 to 6 by @dependabot in #678
  • fix the incorrect verbose of clear_name_cache() by @chaoming0625 in #681
  • [bug] Fix prograss bar is not displayed and updated as expected by @Routhleck in #683
  • Fix autograd by @chaoming0625 in #687

Full Changelog: https://github.com/brainpy/BrainPy/compare/V2.6.0...V2.6.1

Version 2.6.0

New Features

This release provides several new features, including:

  • MLIR registered operator customization interface in brainpy.math.XLACustomOp.
  • Operator customization with CuPy JIT interface.
  • Bug fixes.

What's Changed

  • [doc] Fix the wrong path of more examples of operator customized with taichi.ipynb by @Routhleck in #612
  • [docs] Add colab link for documentation notebooks by @Routhleck in #614
  • Update requirements-doc.txt to fix doc building temporally by @chaoming0625 in #617
  • [math] Rebase operator customization using MLIR registration interface by @chaoming0625 in #618
  • [docs] Add kaggle link for documentation notebooks by @Routhleck in #619
  • update requirements by @chaoming0625 in #620
  • require brainpylib>=0.2.6 for jax>=0.4.24 by @chaoming0625 in #622
  • [tools] add brainpy.tools.compose and brainpy.tools.pipe by @chaoming0625 in #624
  • doc hierarchy update by @chaoming0625 in #630
  • Standardizing and generalizing object-oriented transformations by @chaoming0625 in #628
  • fix #626 by @chaoming0625 in #631
  • Fix delayvar not correct in concat mode by @CloudyDory in #632
  • [dependency] remove hard dependency of taichi and numba by @Routhleck in #635
  • clear_buffer_memory() support clearing array, compilation, and names by @chaoming0625 in #639
  • add brainpy.math.surrogate..Surrogate by @chaoming0625 in #638
  • Enable brainpy object as pytree so that it can be applied with jax.jit etc. directly by @chaoming0625 in #625
  • Fix ci by @chaoming0625 in #640
  • Clean taichi AOT caches by @chaoming0625 in #643
  • [ci] Fix windows pytest fatal exception by @Routhleck in #644
  • [math] Support more than 8 parameters of taichi gpu custom operator definition by @Routhleck in #642
  • Doc for brainpylib>=0.3.0 by @chaoming0625 in #645
  • Find back updates by @chaoming0625 in #646
  • Update installation instruction by @chaoming0625 in #651
  • Fix delay bug by @chaoming0625 in #650
  • update doc by @chaoming0625 in #652
  • [math] Add new customize operators with cupy by @Routhleck in #653
  • [math] Fix taichi custom operator on gpu backend by @Routhleck in #655
  • update cupy operator custom doc by @chaoming0625 in #656
  • version 2.6.0 by @chaoming0625 in #657
  • Upgrade CI by @chaoming0625 in #658

New Contributors

  • @CloudyDory made their first contribution in #632

Full Changelog: https://github.com/brainpy/BrainPy/compare/V2.5.0...V2.6.0

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

  1. Add synapse projection with Delta synapse models through brainpy.dyn.HalfProjDelta and brainpy.dyn.FullProjDelta.
  2. 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.
  3. Add brainpy.reset_level() decorator so that the state resetting order can be customized by users.
  4. Add brainpy.math.ein_rearrange, brainpy.math.ein_reduce, and brainpy.math.ein_repeat functions
  5. Add brainpy.math.scan transformation.
  6. 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.
  7. Many bug fixes.
  8. 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 #545
  • [docs] Optimize tutorial code in operator_custom_with_taichi.ipynb of documentations by @Routhleck in #546
  • [running] fix multiprocessing bugs by @chaoming0625 in #547
  • [docs] Fix typo in docs by @Routhleck in #549
  • ⬆️ Bump conda-incubator/setup-miniconda from 2 to 3 by @dependabot in #551
  • updates by @chaoming0625 in #550
  • brainpy.math.defjvp and brainpy.math.XLACustomOp.defjvp by @chaoming0625 in #554
  • ⬆️ Bump actions/setup-python from 4 to 5 by @dependabot in #555
  • Fix brainpy.math.ifelse bugs by @chaoming0625 in #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 #557
  • Update README by @chaoming0625 in #558
  • [doc] add conductance neuron model tutorial by @chaoming0625 in #559
  • Doc by @chaoming0625 in #560
  • add brainpy.math.functional_vector_grad and brainpy.reset_level() decorator by @chaoming0625 in #561
  • [math] change the internal implementation of surrogate function by @chaoming0625 in #562
  • Math by @chaoming0625 in #563
  • [doc] update citations by @chaoming0625 in #564
  • add support for multi-class margin loss by @charlielam0615 in #566
  • Support for Delta synapse projections by @chaoming0625 in #568
  • [math] Add taichi customized operators(event csrmv, csrmv, jitconn event mv, jitconn mv) by @Routhleck in #553
  • fix doc by @chaoming0625 in #571
  • Fix default math parameter setting bug by @chaoming0625 in #572
  • fix bugs in brainpy.math.random.truncated_normal by @chaoming0625 in #574
  • [doc] fix doc by @chaoming0625 in #576
  • fix bugs in truncated_normal; add TruncatedNormal init. by @charlielam0615 in #575
  • [Dyn] Fix alpha synapse bugs by @ztqakita in #578
  • fix brainpy.math.softplus and brainpy.dnn.SoftPlus by @chaoming0625 in #581
  • add TruncatedNormal to initialize.py by @charlielam0615 in #583
  • Fix _format_shape in random_inits.py by @charlielam0615 in #584
  • fix bugs in truncated_normal by @charlielam0615 in #585
  • [dyn] fix warning of reset_state by @chaoming0625 in #587
  • [math] upgrade variable retrival by @chaoming0625 in #589
  • [math & dnn] add brainpy.math.unflatten and brainpy.dnn.Unflatten by @chaoming0625 in #588
  • [math] add ein_rearrange, ein_reduce, and ein_repeat functions by @chaoming0625 in #590
  • [math] Support taichi customized op with metal cpu backend by @Routhleck in #579
  • Doc fix and standardize Dual Exponential model again by @chaoming0625 in #591
  • update doc, upgrade reset_state, update projection models by @chaoming0625 in #592
  • [taichi] Make taichi caches more transparent and Add clean caches function by @Routhleck in #596
  • [test] remove test skip on macos, since brainpylib supports taichi interface on macos by @chaoming0625 in #597
  • [dyn] add clear_input in the step_run function. by @chaoming0625 in #601
  • [math] Refactor taichi operators by @Routhleck in #598
  • [math] fix brainpy.math.scan by @chaoming0625 in #604
  • disable_ jit support in brainpy.math.scan by @chaoming0625 in #606
  • [math] Remove the logs that taichi.init() print by @Routhleck in #609
  • Version control in Publish.yml CI by @chaoming0625 in #610

New Contributors

  • @charlielam0615 made their first contribution in #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 #508
  • [docs] add low-level op customization by @ztqakita in #507
  • Compatible with jax==0.4.16 by @chaoming0625 in #511
  • updates for parallelization support by @chaoming0625 in #514
  • Upgrade surrogate gradient functions by @chaoming0625 in #516
  • [doc] update operator customization by @chaoming0625 in #517
  • Updates for OO transforma and surrogate functions by @chaoming0625 in #519
  • [dyn] add neuron scaling by @ztqakita in #520
  • State saving, loading, and resetting by @chaoming0625 in #521
  • [delay] rewrite previous delay APIs so that they are compatible with new brainpy version by @chaoming0625 in #522
  • [projection] upgrade projections so that APIs are reused across different models by @chaoming0625 in #523
  • [math] the interface for operator registration by @chaoming0625 in #524
  • FIx bug in Delay by @ztqakita in #525
  • Fix bugs in membrane scaling by @ztqakita in #526
  • [math] Implement taichi op register by @Routhleck in #527
  • Link libtaichi_c_api.so when import brainpylib by @Routhleck in #528
  • update taichi op customization by @chaoming0625 in #529
  • Fix error message by @HoshinoKoji in #530
  • [math] remove the hard requirement of taichi by @chaoming0625 in #531
  • [math] Resolve encoding of source kernel when ti.func is nested in ti… by @Routhleck in #532
  • [math] new abstract function for XLACustomOp, fix its bugs by @chaoming0625 in #534
  • [math] fix numpy array priority by @chaoming0625 in #533
  • [brainpy.share] add category shared info by @chaoming0625 in #535
  • [doc] update documentations by @chaoming0625 in #536
  • [doc] update doc by @chaoming0625 in #537
  • [dyn] add brainpy.reset_state() and brainpy.clear_input() for more consistent and flexible state managements by @chaoming0625 in #538
  • [math] simplify the taichi AOT operator customization interface by @chaoming0625 in #540
  • [dyn] add save_state, load_state, reset_state, and clear_input helpers by @chaoming0625 in #542
  • [dyn] update STDP APIs on CPUs and fix bugs by @chaoming0625 in #543

New Contributors

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

New Contributors

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

New Contributors

  • @AkitsuFaye made their first contribution in #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

  1. 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
  1. 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']
    ...

  1. Optimize the connection-building process when using brainpy.conn.ScaleFreeBA, brainpy.conn.ScaleFreeBADual, brainpy.conn.PowerLaw

  2. New dual exponential model brainpy.dyn.DualExponV2 can be aligned with post dimension.

  3. 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

  4. 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 #412
  • [fix] bug of connect.base.py's require function by @Routhleck in #413
  • Many Updates by @chaoming0625 in #414
  • Update docs by @chaoming0625 in #415
  • fix conflict by @yygf123 in #416
  • add a new implementation of Dual Exponential Synapse model which can be aligned post. by @chaoming0625 in #417
  • Enable test when pull requests by @chaoming0625 in #418
  • Add random.seed() by @yygf123 in #419
  • Remove windows CI because it always generates strange errors by @chaoming0625 in #420
  • Recent updates by @chaoming0625 in #421
  • upgrade Runner and Trainer for new style of DynamicalSystem.update() function by @chaoming0625 in #422
  • update docs by @chaoming0625 in #424
  • fix lif model bugs and support two kinds of spike reset: soft and hard by @chaoming0625 in #423
  • rewrite old synapses with decomposed components by @chaoming0625 in #425
  • fix autograd bugs by @chaoming0625 in #426

New Contributors

  • @yygf123 made their first contribution in #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 #379
  • Optimize Gaussian Decay initializer by @Routhleck in #381
  • [update] new loss functions, surrograte base class, Array built-in functions by @chaoming0625 in #383
  • [parallelization] new module of brainpy.pnn for auto parallelization of brain models by @chaoming0625 in #385
  • [fix] fix the bug of loading states by @chaoming0625 in #388
  • [math] support jax.disable_jit() for debugging by @chaoming0625 in #389
  • [initialize] speed up brainpy.init.DOGDecay by @chaoming0625 in #390
  • [doc] fix doc build by @chaoming0625 in #391
  • Add deprecations for deprecated APIs or functions by @chaoming0625 in #393
  • [math] enable debugging for new style of transformations in BrainPy by @chaoming0625 in #392
  • [math] flow control updates by @chaoming0625 in #396
  • Test of rates by @shangyangli in #386
  • Add math docs: NumPy-like operations and Dedicated operators by @c-xy17 in #395
  • [doc] documentation about how to debug and common gotchas by @chaoming0625 in #397
  • Update requirements-doc.txt by @chaoming0625 in #399
  • debug (images not displayed) by @c-xy17 in #400
  • Decouple dynamics and comminucations by @chaoming0625 in #401
  • [fix] bugs of control flows by @chaoming0625 in #404
  • Test for channels, neurons and synapses. by @ztqakita in #403
  • Implement function to visualize connection matrix by @Routhleck in #405
  • Optimize GaussianProb by @Routhleck in #406
  • [dyn] add reduce models, HH-type models and channels by @ztqakita in #408
  • [dnn] add various linear layers by @chaoming0625 in #407
  • [delay] VariableDelay and DataDelay by @chaoming0625 in #409
  • [dyn] add COBA examples using the interface of new brainpy.dyn module by @chaoming0625 in #410
  • [dyn] Update dyn.neurons docs and fix several bugs by @ztqakita in #411

New Contributors

  • @shangyangli made their first contribution in #386

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

Version 2.4.1

New Features

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

What's Changed

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

New Contributors

  • @Routhleck made their first contribution in #365
  • @grysgreat made their first contribution in #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

  1. 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

  2. Update documentation

  3. Fix several bugs

What's Changed

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

  1. 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.
  2. Clip transformation for brainpy initializers.
  3. 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
  4. Enable monitoring GPU models on CPU when setting DSRunner(..., memory_efficient=True). This setting can usually reduce so much memory usage.
  5. 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 #345
  • add CI for testing various models by @chaoming0625 in #346
  • Update docs and tests by @chaoming0625 in #347
  • Fix `Runner(jit=False)`` bug by @chaoming0625 in #348
  • Compatible with jax>=0.4.7 by @chaoming0625 in #349
  • Updates by @chaoming0625 in #350
  • reconstruct BrainPy by merging brainpylib by @ztqakita in #351
  • Intergate brainpylib operators into brainpy by @chaoming0625 in #352
  • fix brainpylib call bug by @chaoming0625 in #354
  • Enable memory-efficient DSRunner by @chaoming0625 in #355
  • fix Array transform bug by @chaoming0625 in #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

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 constitutes the support that enables us 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.

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:

  1. Fix Jax import error when jax>=0.4.2
  2. Backward compatibility of brainpy.dyn module
  3. 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

  1. 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)

  1. 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.

  2. New documentation is currently online for introducing the consolidated BrainPy concept of object-oriented transformation.

  3. Change brainpy.math.JaxArray to brainpy.math.Array.

Deprecations

  1. brainpy.datasets module is no longer supported. New APIs will be moved into brainpy-datasets package.
  2. 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.
  3. 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).
  4. Fix the monitoring error of delay differential equations with brainpy.integrators.IntegratorRunner.

Bug Fixes

  1. Fix the bug on One2One connection.
  2. Fix the bug in eprop example.
  3. Fix ij2csr transformation error.
  4. Fix test bugs

What's Changed

  • fix eprop example error by @chaoming0625 in #305
  • minor updates on API and DOC by @chaoming0625 in #306
  • Add new optimizers by @chaoming0625 in #307
  • add documentation of for random number generation by @chaoming0625 in #308
  • consolidate the concept of OO transformation by @chaoming0625 in #309
  • Upgrade documetations by @chaoming0625 in #310
  • Ready for publish by @chaoming0625 in #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

  1. More ANN layers, including brainpy.layers.Flatten and brainpy.layers.Activation.
  2. Optimized connection building for brainpy.connect module.
  3. cifar dataset.
  4. 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 #291
  • optimizes the connect time when using gpu by @MamieZhu in #293
  • datasets::vision: add cifar dataset by @hbelove in #292
  • fix #294: remove VariableView in dyn_vars of a runner by @chaoming0625 in #295
  • update issue template by @chaoming0625 in #296
  • add multiprocessing functions for batch running of BrainPy functions by @chaoming0625 in #298
  • upgrade connection apis by @chaoming0625 in #299
  • fix #300: update parallelization api documentation by @chaoming0625 in #302
  • update doc by @chaoming0625 in #303

New Contributors

  • @LuckyHFC made their first contribution in #291
  • @MamieZhu made their first contribution in #293
  • @hbelove made their first contribution in #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

  1. 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)
  1. 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

  1. Add numpy operators brainpy.math.mat, brainpy.math.matrix, brainpy.math.asmatrix.
  2. Improve translation rules of brainpylib operators, improve its running speeds.
  3. 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

  1. 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.
  2. 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

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

New features

  1. 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.
  1. brainpy.train provides brainpy.train.BPTT for back-propagation algorithms, brainpy.train.Onlinetrainer for online training algorithms, brainpy.train.OfflineTrainer for offline training algorithms.
  2. 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'])
  1. 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})
  1. brainpy.datasets supports MNIST, FashionMNIST, and other datasets.
  2. 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)
  1. brainpy.layers module provides commonly used models for DNN and reservoir computing.
  2. 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())
  1. 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
  2. Provide shortcuts for GPU memory management via brainpy.math.disable_gpu_memory_preallocation(), and brainpy.math.clear_buffer_memory().

What's Changed

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.

  1. 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
  2. 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.
  3. Fix JaxArray operator errors on None
  4. improve oo-to-function transformation speeds
  5. io works: .save_states() and .load_states()

What's Changed

Full Changelog: V2.1.11...V2.1.12

Version 2.1.11 (2022.05.15)

What's Changed

Full Changelog: V2.1.10...V2.1.11

Version 2.1.10 (2022.05.05)

What's Changed

Full Changelog: V2.1.8...V2.1.10

Version 2.1.8 (2022.04.26)

What's Changed

Full Changelog: V2.1.7...V2.1.8

Version 2.1.7 (2022.04.22)

What's Changed

Full Changelog: V2.1.5...V2.1.7

Version 2.1.5 (2022.04.18)

What's Changed

Full Changelog: V2.1.4...V2.1.5

Version 2.1.4 (2022.04.04)

What's Changed

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

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

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:

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