Release notes

BrainPy 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

BrainPy 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

BrainPy 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”

BrainPy 0.2.4

API changes

  • Fix bugs

BrainPy 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

BrainPy 0.2.2

API changes

  • Redesign visualization

  • Redesign connectivity

  • Update docs

BrainPy 0.2.1

API changes

  • Fix bugs in numba import

  • Fix bugs in numpy mode with scalar model

BrainPy 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