| Name | Jaxley JSON |
| Version |
0.5.0
JSON |
| download |
| home_page | None |
| Summary | Differentiable neuron simulations. |
| upload_time | 2024-11-25 10:55:09 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | None |
| keywords |
neuroscience
biophysics
simulator
jax
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<p align="center">
<img src="https://raw.githubusercontent.com/jaxleyverse/jaxley/main/docs/logo.png" width="360">
</p>
<h1 align="center">Differentiable neuron simulations on CPU, GPU, or TPU</h1>
[](https://badge.fury.io/py/jaxley)
[](https://github.com/jaxleyverse/jaxley/blob/main/CONTRIBUTING.md)
[](https://github.com/jaxleyverse/jaxley/actions)
[](https://github.com/jaxleyverse/jaxley/blob/main/LICENSE)
[**Documentation**](https://jaxley.readthedocs.io/en/latest/)
| [**Getting Started**](https://jaxley.readthedocs.io/en/latest/tutorials/01_morph_neurons.html)
| [**Install guide**](https://jaxley.readthedocs.io/en/latest/installation.html)
| [**Reference docs**](https://jaxley.readthedocs.io/en/latest/jaxley.html)
| [**FAQ**](https://jaxley.readthedocs.io/en/latest/faq.html)
## What is Jaxley?
`Jaxley` is a differentiable simulator for [biophysical neuron models](https://jaxley.readthedocs.io/en/latest/faq/question_03.html), written in the Python library [JAX](https://github.com/google/jax). Its key features are:
- automatic differentiation, allowing gradient-based optimization of thousands of parameters
- support for CPU, GPU, or TPU without any changes to the code
- `jit`-compilation, making it as fast as other packages while being fully written in python
- support for multicompartment neurons
- elegant mechanisms for parameter sharing
## Getting started
`Jaxley` allows to simulate biophysical neuron models on CPU, GPU, or TPU:
```python
import matplotlib.pyplot as plt
from jax import config
import jaxley as jx
from jaxley.channels import HH
config.update("jax_platform_name", "cpu") # Or "gpu" / "tpu".
cell = jx.Cell() # Define cell.
cell.insert(HH()) # Insert channels.
current = jx.step_current(i_delay=1.0, i_dur=1.0, i_amp=0.1, delta_t=0.025, t_max=10.0)
cell.stimulate(current) # Stimulate with step current.
cell.record("v") # Record voltage.
v = jx.integrate(cell) # Run simulation.
plt.plot(v.T) # Plot voltage trace.
```
[Here](https://jaxley.readthedocs.io/en/latest/faq/question_03.html) you can find an overview of what kinds of models can be implemented in `Jaxley`. If you want to learn more, we recommend you to check out our tutorials on how to:
- [get started with `Jaxley`](https://jaxley.readthedocs.io/en/latest/tutorials/01_morph_neurons.html)
- [simulate networks of neurons](https://jaxley.readthedocs.io/en/latest/tutorials/02_small_network.html)
- [speed up simulations with GPUs and `jit`](https://jaxley.readthedocs.io/en/latest/tutorials/04_jit_and_vmap.html)
- [define your own channels and synapses](https://jaxley.readthedocs.io/en/latest/tutorials/05_channel_and_synapse_models.html)
- [compute the gradient and train biophysical models](https://jaxley.readthedocs.io/en/latest/tutorials/07_gradient_descent.html)
## Installation
`Jaxley` is available on [`PyPI`](https://pypi.org/project/jaxley/):
```sh
pip install jaxley
```
This will install `Jaxley` with CPU support. If you want GPU support, follow the instructions on the [`JAX` Github repository](https://github.com/google/jax) to install `JAX` with GPU support (in addition to installing `Jaxley`). For example, for NVIDIA GPUs, run
```sh
pip install -U "jax[cuda12]"
```
## Feedback and Contributions
We welcome any feedback on how Jaxley is working for your neuron models and are happy to receive bug reports, pull requests and other feedback (see [contribute](https://github.com/jaxleyverse/jaxley/blob/main/CONTRIBUTING.md)). We wish to maintain a positive community, please read our [Code of Conduct](https://github.com/jaxleyverse/jaxley/blob/main/CODE_OF_CONDUCT.md).
## License
[Apache License Version 2.0 (Apache-2.0)](https://github.com/jaxleyverse/jaxley/blob/main/LICENSE)
## Citation
If you use `Jaxley`, consider citing the [corresponding paper](https://www.biorxiv.org/content/10.1101/2024.08.21.608979):
```
@article{deistler2024differentiable,
doi = {10.1101/2024.08.21.608979},
year = {2024},
publisher = {Cold Spring Harbor Laboratory},
author = {Deistler, Michael and Kadhim, Kyra L. and Pals, Matthijs and Beck, Jonas and Huang, Ziwei and Gloeckler, Manuel and Lappalainen, Janne K. and Schr{\"o}der, Cornelius and Berens, Philipp and Gon{\c c}alves, Pedro J. and Macke, Jakob H.},
title = {Differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics},
journal = {bioRxiv}
}
```
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"description": "<p align=\"center\">\n <img src=\"https://raw.githubusercontent.com/jaxleyverse/jaxley/main/docs/logo.png\" width=\"360\">\n</p>\n\n<h1 align=\"center\">Differentiable neuron simulations on CPU, GPU, or TPU</h1>\n\n[](https://badge.fury.io/py/jaxley)\n[](https://github.com/jaxleyverse/jaxley/blob/main/CONTRIBUTING.md)\n[](https://github.com/jaxleyverse/jaxley/actions)\n[](https://github.com/jaxleyverse/jaxley/blob/main/LICENSE)\n\n[**Documentation**](https://jaxley.readthedocs.io/en/latest/)\n | [**Getting Started**](https://jaxley.readthedocs.io/en/latest/tutorials/01_morph_neurons.html)\n | [**Install guide**](https://jaxley.readthedocs.io/en/latest/installation.html)\n | [**Reference docs**](https://jaxley.readthedocs.io/en/latest/jaxley.html)\n | [**FAQ**](https://jaxley.readthedocs.io/en/latest/faq.html)\n\n\n## What is Jaxley?\n\n`Jaxley` is a differentiable simulator for [biophysical neuron models](https://jaxley.readthedocs.io/en/latest/faq/question_03.html), written in the Python library [JAX](https://github.com/google/jax). Its key features are:\n\n- automatic differentiation, allowing gradient-based optimization of thousands of parameters \n- support for CPU, GPU, or TPU without any changes to the code \n- `jit`-compilation, making it as fast as other packages while being fully written in python \n- support for multicompartment neurons \n- elegant mechanisms for parameter sharing \n\n\n## Getting started\n\n`Jaxley` allows to simulate biophysical neuron models on CPU, GPU, or TPU:\n```python\nimport matplotlib.pyplot as plt\nfrom jax import config\n\nimport jaxley as jx\nfrom jaxley.channels import HH\n\nconfig.update(\"jax_platform_name\", \"cpu\") # Or \"gpu\" / \"tpu\".\n\ncell = jx.Cell() # Define cell.\ncell.insert(HH()) # Insert channels.\n\ncurrent = jx.step_current(i_delay=1.0, i_dur=1.0, i_amp=0.1, delta_t=0.025, t_max=10.0)\ncell.stimulate(current) # Stimulate with step current.\ncell.record(\"v\") # Record voltage.\n\nv = jx.integrate(cell) # Run simulation.\nplt.plot(v.T) # Plot voltage trace.\n```\n\n[Here](https://jaxley.readthedocs.io/en/latest/faq/question_03.html) you can find an overview of what kinds of models can be implemented in `Jaxley`. If you want to learn more, we recommend you to check out our tutorials on how to:\n\n- [get started with `Jaxley`](https://jaxley.readthedocs.io/en/latest/tutorials/01_morph_neurons.html)\n- [simulate networks of neurons](https://jaxley.readthedocs.io/en/latest/tutorials/02_small_network.html)\n- [speed up simulations with GPUs and `jit`](https://jaxley.readthedocs.io/en/latest/tutorials/04_jit_and_vmap.html)\n- [define your own channels and synapses](https://jaxley.readthedocs.io/en/latest/tutorials/05_channel_and_synapse_models.html)\n- [compute the gradient and train biophysical models](https://jaxley.readthedocs.io/en/latest/tutorials/07_gradient_descent.html)\n\n\n## Installation\n\n`Jaxley` is available on [`PyPI`](https://pypi.org/project/jaxley/):\n```sh\npip install jaxley\n```\nThis will install `Jaxley` with CPU support. If you want GPU support, follow the instructions on the [`JAX` Github repository](https://github.com/google/jax) to install `JAX` with GPU support (in addition to installing `Jaxley`). For example, for NVIDIA GPUs, run\n```sh\npip install -U \"jax[cuda12]\"\n```\n\n\n## Feedback and Contributions\n\nWe welcome any feedback on how Jaxley is working for your neuron models and are happy to receive bug reports, pull requests and other feedback (see [contribute](https://github.com/jaxleyverse/jaxley/blob/main/CONTRIBUTING.md)). We wish to maintain a positive community, please read our [Code of Conduct](https://github.com/jaxleyverse/jaxley/blob/main/CODE_OF_CONDUCT.md).\n\n\n## License\n\n[Apache License Version 2.0 (Apache-2.0)](https://github.com/jaxleyverse/jaxley/blob/main/LICENSE)\n\n\n## Citation\n\nIf you use `Jaxley`, consider citing the [corresponding paper](https://www.biorxiv.org/content/10.1101/2024.08.21.608979):\n\n```\n@article{deistler2024differentiable,\n doi = {10.1101/2024.08.21.608979},\n year = {2024},\n publisher = {Cold Spring Harbor Laboratory},\n author = {Deistler, Michael and Kadhim, Kyra L. and Pals, Matthijs and Beck, Jonas and Huang, Ziwei and Gloeckler, Manuel and Lappalainen, Janne K. and Schr{\\\"o}der, Cornelius and Berens, Philipp and Gon{\\c c}alves, Pedro J. and Macke, Jakob H.},\n title = {Differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics},\n journal = {bioRxiv}\n}\n```\n",
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