Dendrify
========
*Introducing dendrites to spiking neural networks*
.. image:: https://img.shields.io/pypi/v/Dendrify.svg
:target: https://pypi.python.org/pypi/Dendrify
.. image:: https://readthedocs.org/projects/dendrify/badge/?version=latest
:target: https://dendrify.readthedocs.io/en/stable/?badge=stable
:alt: Documentation Status
.. image:: https://img.shields.io/badge/Contributor%20Covenant-v1.4%20adopted-ff69b4.svg
:target: CODE_OF_CONDUCT.md
:alt: Contributor Covenant
Although neuronal dendrites play a crucial role in shaping how individual
neurons process synaptic information, their contribution to network-level
functions has remained largely unexplored. Current spiking neural networks
(SNNs) often oversimplify dendritic properties or overlook their essential
functions. On the other hand, circuit models with morphologically detailed
neuron representations are computationally intensive, making them impractical
for simulating large networks.
In an effort to bridge this gap, we present Dendrify—a freely available,
open-source Python package that seamlessly integrates with the
`Brian 2 simulator <https://brian2.readthedocs.io/en/stable/>`_. Dendrify,
through simple commands, automatically generates reduced compartmental neuron
models with simplified yet biologically relevant dendritic and synaptic
integrative properties. These models offer a well-rounded compromise between
flexibility, performance, and biological accuracy, enabling us to investigate
the impact of dendrites on network-level functions.
.. image:: https://github.com/Poirazi-Lab/dendrify/assets/30598350/b6db9876-6de4-458a-b27e-61d4edd360db
:width: 70 %
:align: center
If you use Dendrify for your published research, we kindly ask you to cite our article:
Pagkalos, M., Chavlis, S., & Poirazi, P. (2023). Introducing the Dendrify framework
for incorporating dendrites to spiking neural networks.
Nature Communications, 14(1), 131. https://doi.org/10.1038/s41467-022-35747-8
Documentation for Dendrify can be found at https://dendrify.readthedocs.io/en/latest/
The project presentation for the INCF/OCNS Software Working Group is available
`on google drive <https://docs.google.com/presentation/d/1LUUh2ja3YSHcmByU0Vyn7vcDEnDq6fWfVxFfuK8FzE0/edit?usp=sharing>`_.
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"description": "Dendrify\n========\n\n*Introducing dendrites to spiking neural networks*\n\n.. image:: https://img.shields.io/pypi/v/Dendrify.svg\n :target: https://pypi.python.org/pypi/Dendrify\n\n.. image:: https://readthedocs.org/projects/dendrify/badge/?version=latest\n :target: https://dendrify.readthedocs.io/en/stable/?badge=stable\n :alt: Documentation Status\n\n.. image:: https://img.shields.io/badge/Contributor%20Covenant-v1.4%20adopted-ff69b4.svg\n :target: CODE_OF_CONDUCT.md\n :alt: Contributor Covenant\n\n\nAlthough neuronal dendrites play a crucial role in shaping how individual \nneurons process synaptic information, their contribution to network-level \nfunctions has remained largely unexplored. Current spiking neural networks \n(SNNs) often oversimplify dendritic properties or overlook their essential \nfunctions. On the other hand, circuit models with morphologically detailed \nneuron representations are computationally intensive, making them impractical \nfor simulating large networks.\n\nIn an effort to bridge this gap, we present Dendrify\u2014a freely available,\nopen-source Python package that seamlessly integrates with the\n`Brian 2 simulator <https://brian2.readthedocs.io/en/stable/>`_. Dendrify,\nthrough simple commands, automatically generates reduced compartmental neuron\nmodels with simplified yet biologically relevant dendritic and synaptic\nintegrative properties. These models offer a well-rounded compromise between\nflexibility, performance, and biological accuracy, enabling us to investigate\nthe impact of dendrites on network-level functions.\n\n.. image:: https://github.com/Poirazi-Lab/dendrify/assets/30598350/b6db9876-6de4-458a-b27e-61d4edd360db\n :width: 70 %\n :align: center\n\nIf you use Dendrify for your published research, we kindly ask you to cite our article:\n\nPagkalos, M., Chavlis, S., & Poirazi, P. (2023). Introducing the Dendrify framework\nfor incorporating dendrites to spiking neural networks.\nNature Communications, 14(1), 131. https://doi.org/10.1038/s41467-022-35747-8\n\n\nDocumentation for Dendrify can be found at https://dendrify.readthedocs.io/en/latest/\n\n\nThe project presentation for the INCF/OCNS Software Working Group is available \n`on google drive <https://docs.google.com/presentation/d/1LUUh2ja3YSHcmByU0Vyn7vcDEnDq6fWfVxFfuK8FzE0/edit?usp=sharing>`_.\n",
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