Release notes

Version 1.1.5

API changes:

  • fix bugs on ndarray import in

  • convenient ‘get_param’ interface brainpy.simulation.layers

  • 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 module

  • add Adagrad, Adadelta, RMSProp optimizers

  • remove setting methods in brainpy.math.jax module

  • remove import jax in and enable jax setting, including

    • enable_x64()

    • set_platform()

    • set_host_device_count()

  • enable b=None as no bias in brainpy.simulation.layers

  • set int_ and float_ 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 structure construction

  • update docs

Version 1.1.2

  • add pre2syn and syn2post operators

  • add verbose and check 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 access in the variable saving and loading to the relative access

  • add UnexpectedTracerError hints in JAX transformation functions

Version 1.1.0

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 in the single object level (i.e., single instance of DynamicSystem)

  • common used DNN layers

  • weight initializations

  • refine synaptic connections

Version 1.0.3

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__() can be done at anywhere (not required to add at the bottom of the __init__() 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() and squeeze() operations in brainpy.ops

  • Add reshape() 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


  • Change the coding style into the object-oriented programming

  • Systematically improve the documentation

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 to numba.prange

  • Support _obj_i, _pre_i, _post_i 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

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

  • Fix bugs in numpy mode with scalar 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