.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/create_network.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_create_network.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_create_network.py:


Creating a Network
==================

This example walks through how to create a simple network and run it in the software simulator

.. image:: ../../../webexamples/images/example-network.svg

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.. code-block:: Python

    # sphinx_gallery_thumbnail_path = '_static/example_network.png'



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Importing the necessary libraries
---------------------------------
First import the CRI network class from the the hs_api library and import the leaky integrate and fire neuron model from the neuron models collection.

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.. code-block:: Python


    from hs_api.api import CRI_network
    from hs_api.neuron_models import LIF_neuron


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Defining a neuron model
------------------------
HiAER-Spike supports specifying different models for different neurons. A set of model classes are provided. Currently only variants of the integrate and fire and memoryless integrate and fire model provided by the LIF_neuron class and ANN_neuron class respectively are supported
LIF_neuron models have 3 parameters

 * theta(:math:`\theta`): determines the membrane potential at which the neuron spikes and the potential is reset to zero
 * nu(:math:`\nu`): controls the magnitude of random noise added to the membrane potential at each step.
 * lambda(:math:`\lambda`): controls the voltage leakage that occurs during each timestep. :math:`v=v-v/2^{leak}`

Neurons a and b belong to neuron model N1, neuron c belongs to neuron model N2, and neuron d belongs to neuron model N3.

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.. code-block:: Python


    N1 = LIF_neuron(theta = 3, nu = -17, Lambda = (2**6)-1)
    N2 = LIF_neuron(theta = 4, nu = -17, Lambda = 2)
    N3 = ANN_neuron(theta = 5, nu = 2)


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Defining the axons dictionary
-----------------------------
Axons represent incoming synapses to the network. Each axon has one or more postsynaptic neurons. Users can manually send spikes over axons at each timestep

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.. code-block:: Python


    axons = {'alpha': [('a', 3),('c', 2)],
                'beta': [('b', 3)]}

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Defining the neurons dictionary
-------------------------------
The neurons dictionary defines the neurons in the network and the synapses between them. Each neuron may have synapses to zero, one, or many postsynaptic neurons and must have a model specified by providing a neuron model object. Keys in the connections and axons dictionaries must be mutually exclusive

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.. code-block:: Python


    neurons = {'a': ([('b', 1), ('d', 2)], N1),
               'b': ([], N1),
               'c': ([], N2),
               'd': ([('c', 1)], N3)}


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Defining the outputs List
-------------------------
The outputs list defines the neurons in the network that the user wishes to monitor for spikes. Each element in the list is the key of a neuron in the connections dicitonary

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.. code-block:: Python


    outputs = ['a', 'b']


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Initializing a Network
----------------------
A CRI network object must be contructed using the prevoisly defined dictionaries and list.

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.. code-block:: Python


    network = CRI_network(axons=axons,connections=connections,outputs=outputs)


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Running a timestep
----------------------
The step method executes a single timestep of the network. Inputs may be provided for each timestep in the form of a list of axons to send spikes over dat the given timestep. A list of spikes is always returned. Optionally the membrane potential parameter can be set to true to return membrane potentials for all neurons in the network.

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.. code-block:: Python


    inputs = ['alpha','beta']
    currSpikes = network.step(inputs)
    # Alternative
    # potentials, currSpikes = network.step(inputs, membranePotential=True)
    print(currSpikes)


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Updating Synapses
----------------------
Network topologies are frozen at object creation, but the weights of existing synapses may be altered.

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.. code-block:: Python


    currWeight = network.read_synapse('a', 'b')
    network.write_synapse('a', 'b', 2)


.. _sphx_glr_download_auto_examples_create_network.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: create_network.ipynb <create_network.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: create_network.py <create_network.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: create_network.zip <create_network.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_