Release notes

Version 1.1.5

API changes:

• fix bugs on ndarray import in brainpy.base.function.py

• 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 brainpy.__init__.py 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

• NEW VERSION OF BRAINPY

• 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 phase_portrait_analyzer.py

Models and examples

• Add CANN examples

Version 0.2.2

API changes

• Redesign visualization

• Redesign connectivity

• Update docs

Version 0.2.1

API changes

• Fix bugs in numba import

• 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