Changelog

Changelog#

All notable changes to this project are documented in this file.

The format follows Keep a Changelog conventions, and this project adheres to Semantic Versioning.

Unreleased#

Changed — JIT-safe parameter validation for NEST neurons#

  • Added brainpy_state._nest._utils.cond_any, a shared tracer-aware reduction helper: it returns False when its condition is a JAX tracer (so if validation checks are skipped during jit/vmap/grad tracing) and bool(np.any(...)) otherwise. All NESTNeuron parameter-validation checks (if np.any(...) / if u.math.any(...)) now route through it.

  • erfc_neuron and ginzburg_neuron: removed a Python if bool(any(...)) branch inside update() that broke under jax.jit; the per-neuron update is now always computed and masked with where, making both models JIT-compatible.

  • Added brainpy_state/_nest/jit_compat_test.py verifying every public NESTNeuron subclass traces under jit (57 models), with architecturally NumPy-scalar models (precise-spiking, mean-field, delay-queue) documented.

Added — Network API for NEST-style models#

  • brainpy.state.Network — brainstate.nn.Module subclass with projection-first update() traversal and JIT-wrapped simulate(duration, monitor=...).

  • brainpy.state.Builder — imperative subclass exposing add() and connect(); produces the same underlying module tree as a subclassed Network.

  • Rule-based projections: OneToOneProj, AllToAllProj, PairwiseBernoulliProj, SymmetricPairwiseBernoulliProj, FixedIndegreeProj, FixedOutdegreeProj, FixedTotalNumberProj, PairwisePoissonProj. Uniform constructor (pre, post, *, weight, delay=None, syn, out, allow_autapses, allow_multapses, seed, **rule_kwargs). delay= support is deferred to a follow-up — v1 accepts delay=None only.

  • brainpy.state.Recorder — helper that wires a passive NESTDevice recorder to a source population (string attribute or callable).

  • brainpy.state.dist.{Normal, LogNormal, Uniform} — distribution objects sampled once at projection __init__.

  • Brunel flagship example at examples/brunel.py.

See docs/superpowers/specs/2026-05-12-nest-network-api-design.md for the design and docs/superpowers/plans/2026-05-12-nest-network-api.md for the implementation plan.

0.0.4 – 2025-02-21#

Highlights#

Version 0.0.4 is a major feature release that introduces a comprehensive library of NEST-compatible neural models (initial version), reorganizes the public API into dedicated _brainpy and _nest submodules, and transitions the project to the Apache 2.0 license.

Added#

NEST-Compatible Model Library#

A complete port of the NEST simulator model catalogue, covering more than 250 models across all major categories:

Neuron Models

  • Integrate-and-Fire (IAF): iaf_psc_alpha, iaf_psc_alpha_multisynapse, iaf_psc_alpha_ps, iaf_psc_delta, iaf_psc_delta_ps, iaf_psc_exp, iaf_psc_exp_htum, iaf_psc_exp_multisynapse, iaf_psc_exp_ps, iaf_psc_exp_ps_lossless, iaf_cond_alpha, iaf_cond_alpha_mc, iaf_cond_beta, iaf_cond_exp, iaf_cond_exp_sfa_rr, iaf_bw_2001, iaf_bw_2001_exact, iaf_chs_2007, iaf_chxk_2008, iaf_tum_2000

  • Adaptive Exponential IF (AdEx / aeif): aeif_cond_alpha, aeif_cond_alpha_astro, aeif_cond_alpha_multisynapse, aeif_cond_beta_multisynapse, aeif_cond_exp, aeif_psc_alpha, aeif_psc_delta, aeif_psc_delta_clopath, aeif_psc_exp

  • Generalized IF (GIF): gif_cond_exp, gif_cond_exp_multisynapse, gif_pop_psc_exp, gif_psc_exp, gif_psc_exp_multisynapse

  • Multi-timescale Adaptive Threshold (MAT): amat2_psc_exp, mat2_psc_exp

  • Generalized LIF (GLIF): glif_cond, glif_psc, glif_psc_double_alpha

  • Hodgkin-Huxley family: hh_cond_beta_gap_traub, hh_cond_exp_traub, hh_psc_alpha, hh_psc_alpha_clopath, hh_psc_alpha_gap, ht_neuron

  • Izhikevich: izhikevich

  • Point-process neurons: pp_cond_exp_mc_urbanczik, pp_psc_delta

  • Binary neurons: erfc_neuron, ginzburg_neuron, mcculloch_pitts_neuron

  • Rate neurons: gauss_rate_ipn, lin_rate_ipn, lin_rate_opn, rate_neuron_ipn, rate_neuron_opn, rate_transformer_node, siegert_neuron, sigmoid_rate_ipn, sigmoid_rate_gg_1998_ipn, tanh_rate_ipn, tanh_rate_opn, threshold_lin_rate_ipn, threshold_lin_rate_opn

  • Miscellaneous: ignore_and_fire

Synapse Models

  • Static: static_synapse, static_synapse_hom_w, cont_delay_synapse, bernoulli_synapse

  • Short-term plasticity: tsodyks_synapse, tsodyks_synapse_hom, tsodyks2_synapse, quantal_stp_synapse

  • STDP: stdp_synapse, stdp_synapse_hom, stdp_dopamine_synapse, stdp_facetshw_synapse_hom, stdp_nn_pre_centered_synapse, stdp_nn_restr_synapse, stdp_nn_symm_synapse, stdp_pl_synapse_hom, stdp_triplet_synapse

  • Voltage-based / specialized: clopath_synapse, ht_synapse, jonke_synapse, urbanczik_synapse, vogels_sprekeler_synapse

  • Structural connections: diffusion_connection, gap_junction, rate_connection_delayed, rate_connection_instantaneous, sic_connection

Stimulation Devices

  • Current generators: ac_generator, dc_generator, noise_generator, step_current_generator, step_rate_generator

  • Spike generators: spike_generator, spike_train_injector, spike_dilutor

  • Poisson generators: poisson_generator, poisson_generator_ps, inhomogeneous_poisson_generator, sinusoidal_poisson_generator

  • Other generators: gamma_sup_generator, mip_generator, ppd_sup_generator, pulsepacket_generator, sinusoidal_gamma_generator

Recording Devices

  • correlation_detector, correlomatrix_detector, correlospinmatrix_detector, multimeter, spike_recorder, spin_detector, volume_transmitter, weight_recorder

Specialised Models

  • astrocyte_lr_1994: Leaky integrator astrocyte model

  • cm_default: Multi-compartment neuron model

NEST Base Infrastructure#

  • NESTNeuron, NESTSynapse, NESTDevice: abstract base classes for all NEST-compatible models, providing shared parameter management and state initialisation utilities (_nest/_base.py, _nest/_utils.py)

BrainPy-style Model Enhancements#

  • SpikeTime: added weight parameter and time-rounding option

  • AlignPostProj, DeltaProj, CurrentProj: new projection variants

  • align_pre_projection, align_post_projection: projection utility functions

  • SymmetryGapJunction, AsymmetryGapJunction: gap junction projection types

  • PoissonEncoder, PoissonInput, poisson_input: additional input generators

  • LeakyRateReadout, LeakySpikeReadout: renamed and expanded readout classes

Changed#

  • API layout: public models reorganised into brainpy_state._brainpy (BrainPy-style models) and brainpy_state._nest (NEST-compatible models) subpackages; all symbols remain importable from the top-level namespace

  • License: changed from GNU GPLv3 to Apache License 2.0

  • Data types: model state variables now use brainstate.environ.dftype() for consistent default floating-point precision across the ecosystem

  • Dependency: minimum brainpy requirement raised to >= 2.7.6

  • NEST models: all NEST models refactored onto shared base classes and utility helpers, eliminating duplicated boilerplate across model files

  • aeif_cond_alpha: streamlined initialisation of integration_step and I_stim fields

  • Documentation: mathematical equations and parameter descriptions expanded and standardised across all BrainPy-style and NEST-compatible model files

Fixed#

  • Documentation URLs updated in CONTRIBUTING.md, config.yml, and pyproject.toml to point to the correct hosted locations

  • Ecosystem cross-references in README.md and index.rst corrected

0.0.3 – 2025-01-01#

Highlights#

Version 0.0.3 consolidates the package rename from brainpy.state to brainpy_state, adds brainpy as a declared runtime dependency, and tightens internal state initialisation.

Added#

  • brainpy added as an explicit runtime dependency in requirements.txt and pyproject.toml

Changed#

  • Package renamed from brainpy.state to brainpy_state; all public import paths updated accordingly

  • HiddenState initialisation refactored for correctness and clarity

  • Function names updated and a simulation example added to the main script

  • Directory structure reorganised in preparation for the _brainpy / _nest split introduced in 0.0.4

  • Minimum brainpy requirement formalised

Fixed#

  • Import statements that still referenced the old brainpy.state namespace corrected throughout the codebase

0.0.1 – 2024-12-01#

Initial release of brainpy_state.

brainpy_state modernises the BrainPy spiking neural network simulator by adopting the state-based programming model introduced in brainstate.

Added#

Neuron Models#

  • Integrate-and-Fire (LIF) family

    • IF: basic integrate-and-fire neuron

    • LIF, LIFRef: leaky integrate-and-fire (with optional refractory period)

    • ExpIF, ExpIFRef: exponential integrate-and-fire

    • AdExIF, AdExIFRef: adaptive exponential integrate-and-fire

    • ALIF: adaptive leaky integrate-and-fire

    • QuaIF: quadratic integrate-and-fire

    • AdQuaIF, AdQuaIFRef: adaptive quadratic integrate-and-fire

    • Gif, GifRef: generalized integrate-and-fire

  • Hodgkin-Huxley family

    • HH: classic Hodgkin-Huxley conductance-based neuron

    • MorrisLecar: Morris-Lecar neuron

    • WangBuzsakiHH: Wang-Buzsaki modified Hodgkin-Huxley neuron

  • Izhikevich family

    • Izhikevich, IzhikevichRef: Izhikevich neuron (with optional refractory period)

Synapse Models#

  • Exponential synapses: Expon (single exponential decay), DualExpon (dual exponential rise-and-decay)

  • Receptor-based synapses: Alpha, AMPA, GABAa, BioNMDA

  • Short-term plasticity: STP (facilitation and depression), STD (pure depression)

Infrastructure#

  • Neuron, Synapse, Dynamics: abstract base classes for custom model development

  • Projection, AlignPostProj: network projection utilities

  • COBA, CUBA, MgBlock: synaptic output current handlers

  • SpikeTime, PoissonSpike: spike-train input generators

  • Readout, LeakyReadout, WeightedReadout: readout layer implementations

  • Runtime compatibility check: raises an informative error when an incompatible brainpy version (< 2.7.4) is detected

Dependencies#

Package

Minimum version

Python

3.10

jax

latest

brainstate

0.2.0

saiunit

latest

brainevent

0.0.4

braintools

0.0.9

numpy

1.15