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

New Contributors

• @CloudyDory made their first contribution in https://github.com/brainpy/BrainPy/pull/632

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

- Python
Published by chaoming0625 about 2 years ago

brainpy - 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/tutorialadvanced/operatorcustomwithtaichi.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

- Python
Published by chaoming0625 over 2 years ago

brainpy - 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:

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

forward function

spk = fun(membrane_potential)

backward function

dV = fun.surrogategrad(1., membranepotential)

surrogate forward function

surrospk = fun.surrogatefun(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:

```python 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.

```python import brainpy as bp

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

def resetstate(self, batchormode=None): # define variables self.a = bp.init.variable(bm.zeros, (self.num,), batchormode)

def loadstate(self, statedict): # load states from an external dict self.a.value = bm.asjax(statedict['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,

```python class EINet(bp.DynSysGroup): def init(self): super().init() self.N = bp.dyn.LifRefLTC(4000, Vrest=-60., Vth=-50., Vreset=-60., tau=20., tauref=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:

```python

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

@nb.njit def numbacpufun(a, b, outa, outb): outa[:] = a outb[:] = b

@ti.kernel def taichigpufun(a, b, outa, outb): for i in range(a.size): outa[i] = a[i] for i in range(b.size): outb[i] = b[i]

prim = bm.XLACustomOp(cpukernel=numbacpufun, gpukernel=taichigpufun) 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/testSTDP.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 libtaichicapi.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

- Python
Published by chaoming0625 over 2 years ago

brainpy - 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.biomodels 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

- Python
Published by chaoming0625 over 2 years ago

brainpy - 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

- Python
Published by chaoming0625 almost 3 years ago

brainpy - 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,

```python

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.

```python

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 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

- Python
Published by chaoming0625 almost 3 years ago

brainpy - 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

- Python
Published by ztqakita almost 3 years ago

brainpy - 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 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

- Python
Published by chaoming0625 about 3 years ago

brainpy - 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

1. Update documentation
2. 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

- Python
Published by chaoming0625 about 3 years ago

brainpy - 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 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

- Python
Published by chaoming0625 about 3 years ago

brainpy - 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

- Python
Published by chaoming0625 about 3 years ago

brainpy - 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

- Python
Published by chaoming0625 about 3 years ago

brainpy - 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.

```python import brainpy as bp

class EINet(bp.DynamicalSystemNS): def init(self, scale=1.0, einput=20., iinput=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): espike = self.delayE.at('E') ispike = self.delayI.at('I') einp = self.E2E(espike, self.E.V) + self.I2E(ispike, self.E.V) + self.bgexc iinp = self.I2I(ispike, self.I.V) + self.E2I(espike, self.I.V) + self.bginh self.delayE(self.E(einp)) self.delayI(self.I(iinp))

```

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.reduceeuclideannorm
• brainpy.math.unsortedsegmentsqrt_n
• brainpy.math.segment_mean
• brainpy.math.unsortedsegmentsum
• brainpy.math.unsortedsegmentprod
• brainpy.math.unsortedsegmentmax
• brainpy.math.unsortedsegmentmin
• brainpy.math.unsortedsegmentmean
• brainpy.math.segment_sum
• brainpy.math.segment_prod
• brainpy.math.segment_max
• brainpy.math.segment_min
• brainpy.math.clipbyvalue
• brainpy.math.cast

Others

• Remove the hard requirements of brainpylib and numba.

- Python
Published by chaoming0625 about 3 years ago

brainpy - 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

- Python
Published by chaoming0625 over 3 years ago

brainpy - Version 2.3.3

Improve backward compatibility:

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

- Python
Published by chaoming0625 over 3 years ago

brainpy - 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

- Python
Published by chaoming0625 over 3 years ago

brainpy - Version 2.3.1

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

python 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

python bp.datasets.xxxxx

to

```python import brainpydatasets as bpdata

bpdata.chaos.XXX bpdata.vision.XXX ```

For a chaotic data series,

```python

old version

data = bp.datasets.doublescrollseries(twarmup + ttrain + ttest, dt=dt) xvar = data['x'] yvar = data['y'] zvar = data['z']

new version

data = bd.chaos.DoubleScrollEq(twarmup + ttrain + ttest, dt=dt) xvar = data.xs yvar = data.ys zvar = data.zs ```

For a vision dataset,

```python

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,

```python

old version

class NGRC(bp.DynamicalSystem): def init(self, numin): super(NGRC, self).init() self.r = bp.layers.NVAR(numin, delay=2, order=3) self.di = bp.layers.Dense(self.r.numout, numin)

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

new version

bm.setenviroment(mode=bm.batchingmode)

class NGRC(bp.DynamicalSystem): def init(self, numin): super(NGRC, self).init() self.r = bp.layers.NVAR(numin, delay=2, order=3) self.di = bp.layers.Dense(self.r.numout, numin, 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,

python bm.set_enviroment(mode=bm.training_mode)

3. Error: inputsarebatching 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.

```python import brainpy.math as bm

hh = bp.neurons.HH(1)

@bm.todynsys(childobjs=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,

```python

set the default integer type

bm.setint(jax.numpy.int64)

get the default integer type

a1 = bm.asarray([1], dtype=bm.int) a2 = bm.asarray([1], dtype=bm.getint()) # equivalent ```

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

python 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

python 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:

```python

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:

```python 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,

```python 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.sectioninput([0., 1., 0.5], [200, 200, 200], returnlength=True) runner.run(duration, dynargs=dict(Iext=Iext)) bp.visualize.lineplot(runner.mon.ts, runner.mon['V'], legend='V')

run 2

Iext, duration = bp.inputs.sectioninput([0.5], [200], returnlength=True) runner.run(duration, dynargs=dict(Iext=Iext)) bp.visualize.lineplot(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,

```python 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(..., funafterreport=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.

python 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,

```python # old version

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

python # 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.

```python

old version

net = EINet()

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

runner = bp.DSRunner(net, funinputs=finput, inputs=('I.input', 10.)) ```

```python

new version

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

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.

```python 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 dynargs={'I': bp.inputs.rampinput(0, 4, 100)}, # CHANGE ) runner.run(100.,)

```

```python

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.}, dynargs={'I': bp.inputs.rampinput(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

- Python
Published by chaoming0625 over 3 years ago

brainpy - 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.

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

any generator can be used for traindata or testdata

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 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

- Python
Published by chaoming0625 over 3 years ago

brainpy - Version 2.2.4.0

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 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

- Python
Published by chaoming0625 over 3 years ago

brainpy - Version 2.2.3.6

• fix bifurcation analysis bug
• fix synaptic delay bug

- Python
Published by chaoming0625 over 3 years ago

brainpy - Version 2.2.3.5

- Python
Published by chaoming0625 over 3 years ago

brainpy - Version 2.2.3.4

New features

1. This release removes the extensions package, and deploys it as a standalone repository as brainpylib.
2. Initializing brainpy.math.random.RandomState with seed_or_key, rather than seed.
3. APIs in brainpy.measure supports loop and vmap methods, the former is memory-efficient, and the later is faster.
4. DNN layers are revised and are all useable.
5. Upgrade operators to match brainpylib>=0.1.1
6. brainpy.math.pre2post_event_sum supports atuodiff (including JVP, VJP), it can be used for SNN training.

Full Changelog: https://github.com/PKU-NIP-Lab/BrainPy/compare/V2.2.3.3...V2.2.3.4

- Python
Published by chaoming0625 over 3 years ago

brainpy - Version 2.2.3.3

- Python
Published by chaoming0625 over 3 years ago

brainpy - Version 2.2.3.2

This release continuously improves the functionality of BrainPy

New features

1. Add brainpy.measure.unitary_LFP() for calculating LFP from neuronal spikes

```python

import brainpy as bp runner = bp.DSRunner() runner.run(100) lfp = bp.measure.unitaryLFP(runner.mon.ts, runner.mon['exc.spike'], 'exc') lfp += bp.measure.unitaryLFP(runner.mon.ts, runner.mon['inh.spike'], 'inh') ```

1. Add brainpy.synapses.PoissonInput model

```python

bp.synapse.PoissonInput(targetvariable, numinput, freq, weight) ```

1. Upgrade brainpy connection methods, improving its speeds. New customization of brainpy Connector can be implemented through

```python

class YourConnector(bp.conn.TwoEndConnector): def build_csr(self): pass

def build_coo(self): pass

def build_mat(self): pass ```

Improvements

1. Support transformation contexts for JaxArray, and improve the error checking of JaxArray updating in a JIT function.

2. Speedup delay retrieval by reversing delay variable data.

3. Improve the operator customization methods by using Numba functions.

4. Fix bugs in GPU operators in brainpylib.

What's Changed

• Docs: add compile_brainpylib documentation by @ztqakita in https://github.com/PKU-NIP-Lab/BrainPy/pull/270
• add PoissonInput model and unitary_LFP() method by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/271
• organize brainpylib for future extensions by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/272
• Update lowdim analyzer by @ztqakita in https://github.com/PKU-NIP-Lab/BrainPy/pull/273
• speedup connections in One2One, All2All, GridFour, GridEight, and others by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/274
• consistent brainpylib with brainpy operators by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/275
• Fix test bugs by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/276
• Fixed setup mac script by @ztqakita in https://github.com/PKU-NIP-Lab/BrainPy/pull/278
• JaxArray transformation context by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/277
• speedup delay retrieval by reversing delay variable data by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/279
• Updating apis for connections and operation registeration by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/280

Full Changelog: https://github.com/PKU-NIP-Lab/BrainPy/compare/V2.2.3.1...V2.2.3.2

- Python
Published by chaoming0625 over 3 years ago

brainpy - Version 2.2.3.1

This release fixes the installation on Windows systems and improves the installation guides in the official documentation and installation process.

The following example shows how to install jaxlib after users install and import brainpy:

```python

import brainpy Traceback (most recent call last): File "", line 1, in File "C:\Users\adadu\miniconda3\envs\py3test\lib\site-packages\brainpy_init_.py", line 10, in raise ModuleNotFoundError(

BrainPy needs jaxlib, please install jaxlib.

1. If you are using Windows system, install jaxlib through

pip install jaxlib -f https://whls.blob.core.windows.net/unstable/index.html

1. If you are using macOS platform, install jaxlib through

pip install jaxlib -f https://storage.googleapis.com/jax-releases/jax_releases.html

1. If you are using Linux platform, install jaxlib through

pip install jaxlib -f https://storage.googleapis.com/jax-releases/jax_releases.html

1. If you are using Linux + CUDA platform, install jaxlib through

pip install jaxlib -f https://storage.googleapis.com/jax-releases/jaxcudareleases.html

Note that the versions of "jax" and "jaxlib" should be consistent, like "jax=0.3.14", "jaxlib=0.3.14".

More detail installation instruction, please see https://brainpy.readthedocs.io/en/latest/quickstart/installation.html#dependency-2-jax

```

Hope this information may help the installation of BrainPy much easiler.

- Python
Published by chaoming0625 over 3 years ago

brainpy - Version 2.2.3

This release continues to improve the usability of BrainPy.

New Features

1. Operations among a JaxArray and a NumPy ndarray in a JIT function no longer cause errors. ```python

   import numpy as np import brainpy.math as bm f = bm.jit(lambda: bm.random.random(3) + np.ones(1)) f JaxArray([1.2022058, 1.683937 , 1.3586301], dtype=float32) ```

2. Initializing a brainpy.math.Variable according to the data shape.

```python

bm.Variable(10, dtype=bm.float32) Variable([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32) ```

1. LengthDelay supports a new method called "concatenate" which is compatible with BP training.

```python

delay = bm.LengthDelay(bm.ones(3), 10, update_method='concatenate') delay.update(bm.random.random(3)) delay.retrieve(0) DeviceArray([0.17887115, 0.6738142 , 0.75816643], dtype=float32) delay.retrieve(10) DeviceArray([0., 0., 0.], dtype=float32) ```

Note that compared with the default updating method "rotation", this method can be used to train delay models with BP algorithms. However, "concatenate" has a slower speed for delay processing.

1. Support customizing the plotting styles of fixed points. However, there is still work to support flexible plotting of analyzed results.

```python

from brainpy.analysis import plotstyle, stability plotstyle.setplotschema(stability.SADDLE_NODE, marker='*', markersize=15) ```

Full Changelog: https://github.com/PKU-NIP-Lab/BrainPy/compare/V2.2.2...V2.2.3

What's Changed

• Update installation info and delay apis by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/263
• Support initializing a Variable by data shape by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/265
• operations with JaxArray and numpy ndarray do not cause errors by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/266
• Update VariableView and analysis plotting apis by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/268

Full Changelog: https://github.com/PKU-NIP-Lab/BrainPy/compare/V2.2.2...V2.2.3

- Python
Published by chaoming0625 over 3 years ago

brainpy - Version 2.2.2

Bug Fixes

This release fixes several bugs in the BrainPy system, including:

• The jitted functions in brainpy.measure module no longer exists when they are cleared by brainpy.math.clear_memory_buffer().
• The bug for clear_input() function.
• The bug for the monitor in brainpy.integrators.IntegratorRunner

What's Changed

• update loop docs and apis by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/261
• fix some bugs by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/262

Full Changelog: https://github.com/PKU-NIP-Lab/BrainPy/compare/V2.2.1...V2.2.2

- Python
Published by chaoming0625 over 3 years ago

brainpy - Version 2.2.1

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, ```python import brainpy.math as bm

def abseval(events, indices, indptr, postval, values): return post_val

def concompute(outs, ins): postval = 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] oldvalue = postval[index] postval[index] = values + old_value

eventsum = bm.XLACustomOp(evalshape=abseval, concompute=con_compute) ```

1. Fix the bug of brainpy.tools.DotDict. Now, it is compatible with the transformations of JAX. For instance, ```python import brainpy as bp from jax import vmap

@vmap def multiplerun(I): hh = bp.neurons.HH(1) runner = bp.dyn.DSRunner(hh, inputs=('input', I), numpymonafterrun=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, ```python import brainpy as bp import brainpy.math as bm

class EINetV2(bp.dyn.Network): def _init(self, scale=1.0, method='expauto'): super(EINetV2, 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 = EINetV2(scale=1., method='expauto')

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) ```

- Python
Published by chaoming0625 over 3 years ago

brainpy - Version 2.2.0

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:

```python

2.1.x

import brainpy as bp

class SomeModel(bp.dyn.DynamicalSystem): def init(self, ): ...... def update(self, t, dt): pass ```

```python

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. ```python

   import brainpy.math as bm a = bm.Variable(bm.zeros((1, 4, 5)), batchaxis=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 batchaxis=0. ```

2. brainpy.train provides brainpy.train.BPTT for back-propagation algorithms, brainpy.train.Onlinetrainer for online training algorithms, brainpy.train.OfflineTrainer for offline training algorithms.

3. brainpy.Base class supports _excluded_vars setting to ignore variables when retrieving variables by using Base.vars() method. ```python

   class OurModel(bp.Base): excludedvars = ('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']) ```

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

hh = bp.neurons.HH(1) finder = bp.analysis.SlowPointFinder(hh, targetvars={'V': hh.V, 'm': hh.m, 'h': hh.h, 'n': hh.n}) 5. ``brainpy.datasets`` supports MNIST, FashionMNIST, and other datasets. 6. Supports defining conductance-based neuron models``. python 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)

7. ``brainpy.layers`` module provides commonly used models for DNN and reservoir computing. 8. Support composable definition of synaptic models by using ``TwoEndConn``, ``SynOut``, ``SynSTP`` and ``SynLTP``. python

bp.synapses.Exponential(self.E, self.E, bp.conn.FixedProb(prob), gmax=0.03 / scale, tau=5, output=bp.synouts.COBA(E=0.), stp=bp.synplast.STD()) `` 9. Provide commonly used surrogate gradient function for spiking generation, including -brainpy.math.spikewithsigmoidgrad -brainpy.math.spikewithlineargrad -brainpy.math.spikewithgaussiangrad -brainpy.math.spikewithmggrad 10. Provide shortcuts for GPU memory management via`brainpy.math.disablegpumemorypreallocation(), andbrainpy.math.clearbuffermemory()``.

What's Changed

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

Full Changelog: https://github.com/PKU-NIP-Lab/BrainPy/compare/V2.1.12...V2.2.0

- Python
Published by chaoming0625 almost 4 years ago

brainpy - Version 2.1.12

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

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

Full Changelog: https://github.com/PKU-NIP-Lab/BrainPy/compare/V2.1.11...V2.1.12

- Python
Published by chaoming0625 about 4 years ago

brainpy - Version 2.1.11

What's Changed

• fix: cross-correlation bug by @ztqakita in https://github.com/PKU-NIP-Lab/BrainPy/pull/201
• update apis, test and docs of numpy ops by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/202
• docs: add sphinxbooktheme by @ztqakita in https://github.com/PKU-NIP-Lab/BrainPy/pull/203
• fix: add requirements-doc.txt by @ztqakita in https://github.com/PKU-NIP-Lab/BrainPy/pull/204
• update contro flow, integrators, operators, and docs by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/205
• improve oo-to-function transformation speed by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/208

Full Changelog: https://github.com/PKU-NIP-Lab/BrainPy/compare/V2.1.10...V2.1.11

- Python
Published by chaoming0625 about 4 years ago

brainpy - Version 2.1.10

• Fix bugs on synapse delay

Full Changelog: https://github.com/PKU-NIP-Lab/BrainPy/compare/V2.1.9...V2.1.10

- Python
Published by chaoming0625 about 4 years ago

brainpy - Version 2.1.9

What's Changed

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

Full Changelog: https://github.com/PKU-NIP-Lab/BrainPy/compare/V2.1.8...V2.1.9

- Python
Published by chaoming0625 about 4 years ago

brainpy - Version 2.1.8

What's Changed

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

Full Changelog: https://github.com/PKU-NIP-Lab/BrainPy/compare/V2.1.7...V2.1.8

- Python
Published by chaoming0625 about 4 years ago

brainpy - Version 2.1.7

What's Changed

• synapse models support heterogeneuos weights by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/170
• more efficient synapse implementation by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/171
• fix input models in brainpy.dyn by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/172
• fix: np array astype by @ztqakita in https://github.com/PKU-NIP-Lab/BrainPy/pull/173
• update README: 'brain-py' to 'brainpy' by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/174
• fix: fix the updating rules in the STP model by @c-xy17 in https://github.com/PKU-NIP-Lab/BrainPy/pull/176
• Updates and fixes by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/177

Full Changelog: https://github.com/PKU-NIP-Lab/BrainPy/compare/V2.1.5...V2.1.7

- Python
Published by chaoming0625 about 4 years ago

brainpy - Version 2.1.5

What's Changed

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

Full Changelog: https://github.com/PKU-NIP-Lab/BrainPy/compare/V2.1.4...V2.1.5

- Python
Published by chaoming0625 about 4 years ago

brainpy - Version 2.1.4

What's Changed

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

Full Changelog: https://github.com/PKU-NIP-Lab/BrainPy/compare/V2.1.3...V2.1.4

- Python
Published by ztqakita about 4 years ago

brainpy - Version 2.1.3

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 https://github.com/PKU-NIP-Lab/BrainPy/pull/122
• fix DOGDecay bugs; add more features by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/124
• fix bugs by @chaoming0625 in https://github.com/PKU-NIP-Lab/BrainPy/pull/126

Full Changelog: https://github.com/PKU-NIP-Lab/BrainPy/compare/V2.1.2...V2.1.3

- Python
Published by chaoming0625 about 4 years ago

brainpy - Version 2.1.2

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

What's Changed

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

Full Changelog: https://github.com/PKU-NIP-Lab/BrainPy/compare/V2.1.1...V2.1.2

- Python
Published by chaoming0625 about 4 years ago

brainpy - Version 2.1.1

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 and RNNRunner

- Python
Published by chaoming0625 about 4 years ago