.. module:: pynn_brainscales
.. toctree:: :maxdepth: 2 :hidden: :caption: Content hx_neuron calibration
BrainScaleS-2 allows users to use the PyNN API to define experiments of spiking neural network. This documentation provides details to the BrainScaleS-2 implementation of PyNN and highlights differences to the standard PyNN interface. More details to the PyNN API can be found in the corresponding documentation.
BrainScaleS-2 is an accelerated, mixed-signal neuromorphic chip; its analog circuits implement the dynamics of the adaptive exponential integrate-and-fire neuron model. The custom cell type :py:class:`~brainscales2.standardmodels.cells.HXNeuron` allows to set the "hardware parameters" of neuron circuits directly. For more information about the :py:class:`~pynn_brainscales.brainscales2.standardmodels.cells.HXNeuron` see :doc:`the corresponding documentation <hx_neuron>`.
In addition the cell types :py:class:`~brainscales2.standardmodels.cells.SpikeSourceArray` and :py:class:`~brainscales2.standardmodels.cells.SpikeSourcePoisson` are available to inject external spikes into the network. Just like in standard PyNN, populations of neurons and projections between populations are used to define the network architecture. A good starting point to get familiar with BrainScales2 and its PyNN interface are the :doc:`demos and tutorials</brainscales2-demos/index>`.
Before the network is emulated on the BrainSacaleS-2 system, the abstract network description has to be translated to a valid hardware configuration. This mapping is performed by :doc:`grenade </api_grenade>`
Recording and receiving of observables such as spikes and membrane voltages work as in standard PyNN. For a list of recordable observables refer to the :doc:`HX neuron documentation<hx_neuron>`.
An overview over the full API can be found in :doc:`/api_pynn-brainscales2`.