.. _nest-style:
==============================
NEST-Compatible Models
==============================
JAX re-implementations of `NEST simulator `_
neuron, synapse, plasticity, and device models — preserving NEST's parameter
names, defaults, and unit conventions so a researcher coming from NEST can port a
model with minimal friction. The models are built on
`brainstate `_, use
`brainunit `_ for physical units, compile
through JAX to CPU, GPU, and TPU, and are **differentiable** where the dynamics
allow it.
They are numerically validated against a **live NEST install** within documented
tolerance bands — see :doc:`validation-status` for the per-family parity evidence
and the A–E tolerance categories.
You build NEST-compatible networks with the explicit :class:`Simulator` API, which
mirrors NEST's vocabulary — ``create`` populations and devices, ``connect`` them
with NEST-like rules, ``simulate``:
.. code-block:: python
import brainunit as u
from brainpy import state as bp
sim = bp.Simulator(dt=0.1 * u.ms)
neuron = sim.create(bp.iaf_psc_alpha, 1, I_e=376. * u.pA)
vm = sim.create(bp.voltmeter)
sim.connect(vm, neuron) # the voltmeter observes the neuron
res = sim.simulate(1000. * u.ms)
v = res.trace(vm, "V_m") # (T, 1) membrane-potential trace
This hub is self-contained: it has its own first-steps tutorials, build-your-own-
network guides, and the porting / fidelity material — and it links *up* into the
shared :doc:`/concepts/index` spine (State, physical units, the ``transform``
loops) where the deeper ideas live. We do **not** duplicate NEST's model
*physiology* here — for the biophysics of any model, follow the link out to the
upstream `NEST documentation `_; our pages
cover the ``brainpy.state`` API, porting parity, semantic divergences, and the
validation showcase.
.. card:: Scaling up: large networks
:link: /examples/nest-gallery
:link-type: doc
Looking for full-scale reference networks — Brunel random balanced networks,
EI-clustered networks, astrocyte–neuron networks? The
:doc:`NEST example gallery ` catalogues 75 ported
scripts as a NEST → ``brainpy.state`` Rosetta map, each backed by a live-NEST
parity test.
First steps
===========
Start here. Four short tutorials in the ``brainpy.state`` idiom (State, units,
and the :class:`Simulator` API) — mirroring PyNEST Parts 1–4.
.. grid:: 1 2 2 4
:gutter: 3
.. grid-item-card:: 1 · One neuron
:link: tutorials/01-one-neuron
:link-type: doc
Create one ``iaf_psc_alpha``, drive it with a current and Poisson noise,
and record its membrane potential and spikes.
.. grid-item-card:: 2 · Populations & devices
:link: tutorials/02-populations
:link-type: doc
Build populations, combine and slice them with ``NodeView`` algebra, and
attach stimulation and recording devices.
.. grid-item-card:: 3 · Connect a network
:link: tutorials/03-connect-network
:link-type: doc
Wire populations with NEST-like connection rules and assemble a small
Brunel random balanced network.
.. grid-item-card:: 4 · Record & analyze
:link: tutorials/04-record-and-analyze
:link-type: doc
Use recorders and detectors, read traces back, and analyze firing rate,
raster, and ISI statistics.
Build your own network
======================
Reference guides for the building blocks: models, connectivity, devices, and
spatial structure.
.. grid:: 1 2 2 3
:gutter: 3
.. grid-item-card:: Models
:link: models
:link-type: doc
The model directory by family (IAF, AdEx, GIF/GLIF, HH, rate, …), linking
to the API reference and out to NEST for physiology.
.. grid-item-card:: Connectivity
:link: connectivity
:link-type: doc
Connection rules and the synapse spec — weights, delays, static and
plastic synapses, population algebra.
.. grid-item-card:: Devices
:link: devices
:link-type: doc
Generators, recorders, and detectors — 27 device models and how to read
their data back.
.. grid-item-card:: Spatial networks
:link: spatial
:link-type: doc
Spatially-structured networks: grid layouts, distance-dependent
connectivity, and position queries.
.. grid-item-card:: Example gallery
:link: /examples/nest-gallery
:link-type: doc
All 75 ported ``nest_like`` scripts as a NEST → ``brainpy.state`` map.
Porting & fidelity
==================
The distinctive material: a side-by-side port, the semantic-divergence catalog,
the validation showcase, and how the integrators work under the hood.
.. grid:: 1 2 2 2
:gutter: 3
.. grid-item-card:: Porting walkthrough
:link: porting-walkthrough
:link-type: doc
Brunel side by side — NEST vs ``brainpy.state`` — with the parameter map
and the divergences a port must account for.
.. grid-item-card:: Semantic divergences
:link: divergences/index
:link-type: doc
Where state lives, parameter-location maps, numerical bands, and pairing
conventions — every entry backed by a live-NEST parity test.
.. grid-item-card:: Validation status
:link: validation-status
:link-type: doc
Per-family live-NEST parity, the A–E tolerance categories, and how to run
the parity harness yourself.
.. grid-item-card:: Integration categories
:link: integration-categories
:link-type: doc
Under the hood: RKF45, analytic propagators, vectorized rate updates, and
no-ODE devices.
.. toctree::
:hidden:
:caption: First steps
tutorials/01-one-neuron
tutorials/02-populations
tutorials/03-connect-network
tutorials/04-record-and-analyze
.. toctree::
:hidden:
:caption: Build your own network
models
connectivity
devices
spatial
.. toctree::
:hidden:
:caption: Porting & fidelity
porting-walkthrough
divergences/index
divergences/stdp
validation-status
integration-categories