NEST-Compatible Examples
========================

A **Rosetta map** of PyNEST examples ported to ``brainpy.state``. Each entry
keeps NEST's model and parameter names, so a NEST user can find the familiar
example on the left and the ``brainpy.state`` equivalent on the right. All
scripts live in the `examples/nest_like/
<https://github.com/chaobrain/brainpy.state/tree/main/examples/nest_like>`_
directory and run end to end through the :class:`~brainpy.state.Simulator` (one
compiled ``for_loop`` — no Python step loop).

Every port is backed by a live-NEST parity test where a sample-for-sample
comparison is meaningful; see :doc:`/nest-style/validation-status` for the
per-model parity evidence and tolerance bands, and the
:doc:`/nest-style/index` hub for the build-your-network guides.

.. admonition:: Reading the map
   :class: note

   Each line reads ``script.py`` — *NEST upstream* → ``brainpy.state`` model or
   feature — one-line summary. Where an upstream mechanism is NEST-specific
   (e.g. the Connection Set Algebra), the port re-expresses the *connectivity it
   describes* natively; those entries are marked.


First steps: single & two neurons
---------------------------------

The PyNEST "Part 1-2" first-contact examples — drive one or two neurons and
record them.

.. grid:: 2
   :gutter: 3

   .. grid-item-card:: one_neuron.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/one_neuron.py

      NEST ``one_neuron`` → ``iaf_psc_alpha`` + ``voltmeter``. A single neuron
      driven by a constant ``I_e``, observed by a reverse-connected voltmeter.

   .. grid-item-card:: one_neuron_with_noise.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/one_neuron_with_noise.py

      NEST ``one_neuron_with_noise`` → ``iaf_psc_alpha`` + 2-channel
      ``poisson_generator``. Excitatory/inhibitory Poisson drive with signed
      per-channel weights.

   .. grid-item-card:: twoneurons.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/twoneurons.py

      NEST ``twoneurons`` → two ``iaf_psc_alpha`` + ``static_synapse``. A driven
      presynaptic neuron connected to a postsynaptic neuron, each with its own
      voltmeter.

   .. grid-item-card:: testiaf.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/testiaf.py

      NEST ``testiaf`` → ``iaf_psc_alpha`` swept over ``dt`` ∈ {0.1, 0.5, 1.0} ms.
      Constant-current charge / spike / refractory / recovery (rebuild-per-trial).

   .. grid-item-card:: if_curve.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/if_curve.py

      NEST ``if_curve`` → ``aeif_cond_exp`` + ``noise_generator``. Population firing
      rate across an ``(I_mean, I_std)`` grid — the neuron's transfer function.

   .. grid-item-card:: vinit_example.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/vinit_example.py

      NEST ``vinit_example`` → ``iaf_cond_exp_sfa_rr``. Passive relaxation toward
      ``E_L`` from several initial membrane voltages (rebuild-per-trial).

   .. grid-item-card:: balancedneuron.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/balancedneuron.py

      NEST ``balancedneuron`` → ``iaf_psc_alpha`` + 2-channel ``poisson_generator``.
      Root-find the inhibitory rate that balances the neuron's output rate
      (SciPy ``bisect``, rebuild-per-trial).

   .. grid-item-card:: izhikevich.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/izhikevich.py

      NEST ``izhikevich`` → ``izhikevich`` neuron. The canonical Izhikevich (2003)
      firing regimes.

   .. grid-item-card:: hh_psc_alpha.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/hh_psc_alpha.py

      NEST ``hh_psc_alpha`` → ``hh_psc_alpha`` + ``multimeter`` + ``spike_recorder``.
      Hodgkin-Huxley F-I curve from a constant-current sweep.

   .. grid-item-card:: hh_phaseplane.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/hh_phaseplane.py

      NEST ``hh_phaseplane`` → ``hh_psc_alpha`` (analysis). V-n phase-plane vector
      field, nullclines, and a limit cycle — a pedagogical analysis demo.


Neuron models
-------------

Single-neuron ports exercising specific neuron families — adaptive exponential,
generalized-LIF, multi-timescale, and multi-compartment models.

.. grid:: 2
   :gutter: 3

   .. grid-item-card:: aeif_cond_beta_multisynapse.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/aeif_cond_beta_multisynapse.py

      NEST ``aeif_cond_beta_multisynapse`` → same model. One spike fans out to four
      distinct beta-function conductance receptors via ``connect(receptor_type=k)``.

   .. grid-item-card:: brette_gerstner_fig_2c.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/brette_gerstner_fig_2c.py

      NEST ``brette_gerstner_fig_2c`` → ``aeif_cond_alpha``. AdEx spike-frequency
      adaptation (Brette & Gerstner 2005, Fig 2C).

   .. grid-item-card:: brette_gerstner_fig_3d.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/brette_gerstner_fig_3d.py

      NEST ``brette_gerstner_fig_3d`` → ``aeif_cond_exp``. AdEx post-inhibitory
      rebound burst (Fig 3D).

   .. grid-item-card:: gif_cond_exp_multisynapse.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/gif_cond_exp_multisynapse.py

      NEST ``gif_cond_exp_multisynapse`` → same model. Generalized-IAF neuron with
      two exponential-conductance receptors.

   .. grid-item-card:: glif_cond_neuron.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/glif_cond_neuron.py

      NEST ``glif_cond_neuron`` → ``glif_cond``. Conductance-based Allen-Institute
      GLIF at its five mechanism levels (RKF45 conductance synapses).

   .. grid-item-card:: glif_psc_neuron.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/glif_psc_neuron.py

      NEST ``glif_psc_neuron`` → ``glif_psc``. Current-based GLIF at five mechanism
      levels, advanced with exact propagator matrices.

   .. grid-item-card:: glif_psc_double_alpha_neuron.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/glif_psc_double_alpha_neuron.py

      NEST ``glif_psc_double_alpha_neuron`` → ``glif_psc_double_alpha``. Double-alpha
      post-synaptic currents across three receptor ports.

   .. grid-item-card:: mat_psc_exp.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/mat_psc_exp.py

      NEST ``mat_psc_exp`` → ``mat2_psc_exp`` / ``amat2_psc_exp``. Multi-timescale
      adaptive-threshold neurons whose membrane never resets.

   .. grid-item-card:: mc_neuron.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/mc_neuron.py

      NEST ``mc_neuron`` → ``iaf_cond_alpha_mc``. Three-compartment neuron with nine
      receptors selecting compartment and channel.

   .. grid-item-card:: two_comps.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/two_comps.py

      NEST compartmental ``two_comps`` → ``cm_default``. Two-compartment models with
      active vs passive dendrites and attached AMPA/GABA/NMDA receptors.

   .. grid-item-card:: receptors_and_current.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/receptors_and_current.py

      NEST compartmental ``receptors_and_current`` → ``cm_default``. A soma + two
      dendrites with a different receptor per compartment plus a current injection.

   .. grid-item-card:: wang_decision_making.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/wang_decision_making.py

      NEST ``wang_decision_making`` → ``iaf_bw_2001``. Wang (2002) spiking
      decision-making network with AMPA/GABA/recurrent-NMDA receptors.


Balanced & Brunel networks
--------------------------

Random balanced networks — the Brunel family and related E/I benchmarks.

.. grid:: 2
   :gutter: 3

   .. grid-item-card:: brunel_alpha.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/brunel_alpha.py

      NEST ``brunel_alpha_nest`` → ``iaf_psc_alpha`` + ``poisson_generator`` +
      ``spike_recorder``. Brunel (2000) balanced random network with alpha synapses.

   .. grid-item-card:: brunel_delta.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/brunel_delta.py

      NEST ``brunel_delta_nest`` → ``iaf_psc_delta``. Brunel network with delta
      synapses (instantaneous voltage jumps; weights in mV).

   .. grid-item-card:: brunel_exp_multisynapse.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/brunel_exp_multisynapse.py

      NEST ``brunel_exp_multisynapse_nest`` → ``iaf_psc_exp_multisynapse``. Brunel
      network with per-connection uniformly-drawn receptor ports.

   .. grid-item-card:: brunel_alpha_evolution_strategies.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/brunel_alpha_evolution_strategies.py

      NEST ``brunel_alpha_evolution_strategies`` → ``iaf_psc_alpha`` (Brunel alpha).
      Natural-evolution-strategies tuning of ``eta`` and ``g`` to target statistics.

   .. grid-item-card:: brunel_siegert.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/brunel_siegert.py

      NEST ``brunel_siegert_nest`` → ``siegert_neuron`` + ``diffusion_connection``.
      Mean-field (Siegert) rates for the Brunel network via pseudo-time relaxation.

   .. grid-item-card:: brette_et_al_2007.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/brette_et_al_2007.py

      NEST ``brette_et_al_2007/{coba,cuba}`` → ``iaf_cond_exp`` / ``iaf_psc_exp``.
      The Vogels-Abbott COBA + CUBA FACETS review benchmarks.

   .. grid-item-card:: ei_clustered_network.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/ei_clustered_network.py

      NEST ``EI_clustered_network`` → ``iaf_psc_exp``. Clustered random balanced
      network with potentiated in-cluster / depressed out-cluster weights.

   .. grid-item-card:: sensitivity_to_perturbation.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/sensitivity_to_perturbation.py

      NEST ``sensitivity_to_perturbation`` → ``iaf_psc_delta`` (Brunel-style).
      Two trials differing by one spike show chaotic decorrelation.

   .. grid-item-card:: artificial_synchrony.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/artificial_synchrony.py

      NEST ``artificial_synchrony`` (grid branch) → ``iaf_psc_alpha``. Grid-induced
      artificial synchrony measured by the Golomb-Rinzel statistic.


Devices: generators, recorders & detectors
------------------------------------------

The device family — stimulation generators, recorders/multimeters, and
correlation detectors (see :doc:`/nest-style/devices`).

.. grid:: 2
   :gutter: 3

   .. grid-item-card:: multimeter_file.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/multimeter_file.py

      NEST ``multimeter_file`` → ``multimeter`` (in-memory). Recording several
      analog variables; read back with ``res.trace(mm, name)``.

   .. grid-item-card:: recording_demo.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/recording_demo.py

      NEST ``recording_demo`` → ``poisson_generator`` + ``iaf_psc_exp`` +
      recorders. A tour of the recording API.

   .. grid-item-card:: repeated_stimulation.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/repeated_stimulation.py

      NEST ``repeated_stimulation`` → ``poisson_generator`` gated to a
      ``[start, stop]`` window, repeated across trials.

   .. grid-item-card:: BrodyHopfield.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/BrodyHopfield.py

      NEST ``BrodyHopfield`` → ``iaf_psc_alpha`` + ``ac_generator`` +
      ``noise_generator``. Spike synchronization to a weak subthreshold oscillation.

   .. grid-item-card:: sinusoidal_poisson_generator.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/sinusoidal_poisson_generator.py

      NEST ``sinusoidal_poisson_generator`` → same device. An inhomogeneous Poisson
      train with a sinusoidally-modulated instantaneous rate.

   .. grid-item-card:: sinusoidal_gamma_generator.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/sinusoidal_gamma_generator.py

      NEST ``sinusoidal_gamma_generator`` → same device. A sinusoidally-modulated
      gamma-process drive (regularity controlled by the shape parameter).

   .. grid-item-card:: pulsepacket.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/pulsepacket.py

      NEST ``pulsepacket`` → ``pulsepacket_generator``. Gaussian-profile synchronous
      spike volleys and their effect on a target neuron.

   .. grid-item-card:: precise_spiking.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/precise_spiking.py

      NEST ``precise_spiking`` → ``iaf_psc_exp`` vs ``iaf_psc_exp_ps``. Grid vs
      precise (off-grid) spike timing across resolutions.

   .. grid-item-card:: correlospinmatrix_detector_two_neuron.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/correlospinmatrix_detector_two_neuron.py

      NEST ``correlospinmatrix_detector_two_neuron`` → ``ginzburg_neuron`` +
      ``mcculloch_pitts_neuron`` + ``correlospinmatrix_detector``. Binary
      auto-/cross-covariances.

   .. grid-item-card:: cross_check_mip_corrdet.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/cross_check_mip_corrdet.py

      NEST ``cross_check_mip_corrdet`` → ``mip_generator`` + ``correlation_detector``.
      A cross-correlogram cross-checked against a hand-written reference.

   .. grid-item-card:: CampbellSiegert.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/CampbellSiegert.py

      NEST ``CampbellSiegert`` → ``iaf_psc_alpha`` (analytic cross-check). Campbell's
      theorem + Siegert's approximation vs a simulated population.


Plasticity: STP, STDP & dendritic rules
---------------------------------------

Short-term plasticity, spike-timing-dependent plasticity, and dendritic
prediction-error learning (see :doc:`/nest-style/divergences/stdp`).

.. grid:: 2
   :gutter: 3

   .. grid-item-card:: evaluate_tsodyks2_synapse.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/evaluate_tsodyks2_synapse.py

      NEST ``evaluate_tsodyks2_synapse`` → ``tsodyks2_synapse``. Depressing and
      facilitating short-term-plasticity envelopes read off a linear post neuron.

   .. grid-item-card:: evaluate_quantal_stp_synapse.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/evaluate_quantal_stp_synapse.py

      NEST ``evaluate_quantal_stp_synapse`` → ``quantal_stp_synapse``. The
      stochastic (multi-release-site) Tsodyks-Markram variant converging to the
      deterministic envelope.

   .. grid-item-card:: iaf_tum_2000_short_term_depression.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/iaf_tum_2000_short_term_depression.py

      NEST ``iaf_tum_2000_short_term_depression`` → ``iaf_tum_2000``. Presynaptic
      short-term depression (Tsodyks et al. 1998, Fig 1A).

   .. grid-item-card:: iaf_tum_2000_short_term_facilitation.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/iaf_tum_2000_short_term_facilitation.py

      NEST ``iaf_tum_2000_short_term_facilitation`` → ``iaf_tum_2000``. Presynaptic
      short-term facilitation (Fig 1B).

   .. grid-item-card:: clopath_synapse_spike_pairing.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/clopath_synapse_spike_pairing.py

      NEST ``clopath_synapse_spike_pairing`` → ``aeif_psc_delta_clopath`` +
      ``clopath_synapse``. Voltage-based STDP weight change vs pairing frequency.

   .. grid-item-card:: clopath_synapse_small_network.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/clopath_synapse_small_network.py

      NEST ``clopath_synapse_small_network`` → ``aeif_psc_delta_clopath`` +
      ``clopath_synapse``. Clopath rule establishing directional structure
      (made deterministic).

   .. grid-item-card:: urbanczik_synapse_example.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/urbanczik_synapse_example.py

      NEST ``urbanczik_synapse_example`` → ``pp_cond_exp_mc_urbanczik`` +
      ``urbanczik_synapse``. Dendritic prediction-error plasticity (Urbanczik &
      Senn 2014).


Gap junctions
-------------

Electrical coupling via the one-step-lagged difference-current scheme.

.. grid:: 2
   :gutter: 3

   .. grid-item-card:: gap_junctions_two_neurons.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/gap_junctions_two_neurons.py

      NEST ``gap_junctions_two_neurons`` → ``hh_psc_alpha_gap`` + ``gap_junction``.
      Two cells synchronizing through a single symmetric electrical coupling.

   .. grid-item-card:: gap_junctions_inhibitory_network.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/gap_junctions_inhibitory_network.py

      NEST ``gap_junctions_inhibitory_network`` → ``hh_psc_alpha_gap``. An
      inhibitory network whose synchrony rises with the gap-junction weight
      (Hahne et al. 2015).


Astrocytes & tripartite networks
--------------------------------

Neuron-astrocyte (tripartite) models using ``tripartite_connect`` and
``sic_connection``.

.. grid:: 2
   :gutter: 3

   .. grid-item-card:: astrocyte_single.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/astrocyte_single.py

      NEST ``astrocyte_single`` → ``astrocyte_lr_1994`` (+ readout neuron).
      A single Poisson-driven astrocyte's IP3/calcium dynamics.

   .. grid-item-card:: astrocyte_interaction.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/astrocyte_interaction.py

      NEST ``astrocyte_interaction`` → ``aeif_cond_alpha_astro`` +
      ``astrocyte_lr_1994`` + ``sic_connection``. The two-neuron + one-astrocyte
      tripartite loop.

   .. grid-item-card:: astrocyte_small_network.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/astrocyte_small_network.py

      NEST ``astrocyte_small_network`` → tripartite ``pairwise_bernoulli`` +
      ``third_factor_bernoulli_with_pool``. A small neuron-astrocyte network.

   .. grid-item-card:: astrocyte_brunel_bernoulli.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/astrocyte_brunel_bernoulli.py

      NEST ``astrocyte_brunel_bernoulli`` → Brunel + astrocytes, Bernoulli primary
      wiring with a random astrocyte pool.

   .. grid-item-card:: astrocyte_brunel_fixed_indegree.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/astrocyte_brunel_fixed_indegree.py

      NEST ``astrocyte_brunel_fixed_indegree`` → Brunel + astrocytes, fixed-indegree
      primary wiring (shares the Bernoulli assembly).


Rate models & mean-field
------------------------

Rate-based neurons and mesoscopic population-rate models.

.. grid:: 2
   :gutter: 3

   .. grid-item-card:: lin_rate_ipn_network.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/lin_rate_ipn_network.py

      NEST ``lin_rate_ipn_network`` → ``lin_rate_ipn``. Linear rate-neuron network
      with delayed excitation and instantaneous inhibition.

   .. grid-item-card:: rate_neuron_dm.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/rate_neuron_dm.py

      NEST ``rate_neuron_dm`` → ``lin_rate_ipn``. A two-unit rectified winner-take-all
      rate decision circuit.

   .. grid-item-card:: gif_pop_psc_exp.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/gif_pop_psc_exp.py

      NEST ``gif_pop_psc_exp`` → ``gif_pop_psc_exp`` vs microscopic ``gif_psc_exp``.
      Mesoscopic population-rate model against its spiking realization.

   .. grid-item-card:: gif_population.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/gif_population.py

      NEST ``gif_population`` → ``gif_psc_exp``. An adaptation-driven oscillating GIF
      population under Poisson drive.


Spatially-structured networks
-----------------------------

Distance-dependent connectivity from the ``brainpy.state.spatial`` family (see
:doc:`/nest-style/spatial`).

.. grid:: 2
   :gutter: 3

   .. grid-item-card:: spatial_grid_iaf.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/spatial_grid_iaf.py

      NEST ``spatial/grid_iaf`` → ``spatial.grid`` + ``iaf_psc_alpha``. A 4x3 grid
      layer; coordinates read back with ``get_position``.

   .. grid-item-card:: spatial_gaussex.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/spatial_gaussex.py

      NEST ``spatial/gaussex`` → distance-dependent pairwise-Bernoulli with a
      Gaussian kernel between two grid populations.

   .. grid-item-card:: spatial_3d_gauss.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/spatial_3d_gauss.py

      NEST ``spatial/test_3d_gauss`` → ``spatial.free`` 3D layer + Gaussian kernel
      clipped to a cubic mask.

   .. grid-item-card:: spatial_gabor.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/spatial_gabor.py

      NEST-like anisotropic kernel → ``gabor`` distance distribution + rotated
      ``elliptical`` mask (per-axis ``distance.x`` / ``distance.y``).

   .. grid-item-card:: spatial_csa.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/spatial_csa.py

      NEST ``csa_spatial_example`` → native distance-dependent pairwise-Bernoulli.
      The Connection Set Algebra expression re-expressed without ``libneurosim``.

   .. grid-item-card:: csa_example.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/csa_example.py

      NEST ``csa_example`` → native ``pairwise_bernoulli`` equivalent. The CSA /
      ``conngen`` mechanism is intentionally not ported; the connectivity it
      describes is shown natively.


Connectivity & introspection
----------------------------

Building, retrieving, and inspecting connections (see
:doc:`/nest-style/connectivity`).

.. grid:: 2
   :gutter: 3

   .. grid-item-card:: synapsecollection.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/synapsecollection.py

      NEST ``synapsecollection`` → ``Simulator.get_connections``. Connecting under
      several rules/synapse models and reading/setting ``.source`` / ``.target`` /
      ``.weight``.

   .. grid-item-card:: plot_weight_matrices.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/plot_weight_matrices.py

      NEST ``plot_weight_matrices`` → connection introspection. Extract every
      realized weight and assemble the EE/EI/IE/II matrices for visualization.


Applications: learning & large networks
---------------------------------------

Larger, application-flavored networks — reinforcement learning and constraint
solving. These are the "large networks" entries featured on the
:doc:`/nest-style/index` hub.

.. grid:: 2
   :gutter: 3

   .. grid-item-card:: pong.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/pong.py

      NEST ``pong/pong`` → pure-Python game logic. The Pong playing field, ball,
      and paddles used by the spiking learners.

   .. grid-item-card:: pong_networks.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/pong_networks.py

      NEST ``pong/networks`` → ``iaf_psc_exp`` learners. Reward-modulated STDP
      (R-STDP) and dopaminergic spiking networks that learn the input→output map.

   .. grid-item-card:: pong_run.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/pong_run.py

      NEST ``pong/run_simulations`` → harness. Trains the two spiking learners
      against each other turn by turn with ``Simulator.cont``.

   .. grid-item-card:: sudoku.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/sudoku.py

      NEST ``sudoku/sudoku_solver`` → harness. Relaxes a Sudoku puzzle with a
      spiking winner-take-all network, reading out per-cell winners each chunk.

   .. grid-item-card:: sudoku_net.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/sudoku_net.py

      NEST ``sudoku/sudoku_net`` → ``iaf_psc_exp`` WTA network. Inhibitory
      connectivity encoding the row/column/box/cell Sudoku constraints.

   .. grid-item-card:: sudoku_puzzles.py
      :link: https://github.com/chaobrain/brainpy.state/blob/main/examples/nest_like/sudoku_puzzles.py

      NEST ``sudoku/helpers_sudoku`` → pure-Python puzzle bank + solution validator
      (NumPy only).


See Also
--------

- :doc:`/nest-style/index` — the NEST-compatible hub (tutorials, build-your-network
  guides, porting & fidelity).
- :doc:`/nest-style/validation-status` — per-model live-NEST parity evidence.
- :doc:`/examples/brainpy-gallery` — the BrainPy-style example gallery.