Add funding, remove acknowledgements for README

jaxleyverse · Aug 22, 2024 · 68dc736 · 68dc736
1 parent caa0804
commit 68dc736
Show file tree

Hide file tree

Showing 8 changed files with 141 additions and 115 deletions.
diff --git a/README.md b/README.md
@@ -18,26 +18,41 @@
 - elegant mechanisms for parameter sharing
 
 
-### Tutorials
+## Getting started
 
-Tutorials are available [on our website](https://jaxleyverse.github.io/jaxley/). We currently have tutorials on how to:
 
-- [simulate morphologically detailed neurons](https://jaxleyverse.github.io/jaxley/tutorial/01_morph_neurons/)
-- [simulate networks of such neurons](https://jaxleyverse.github.io/jaxley/tutorial/02_small_network/)
-- [set parameters of cells and networks](https://jaxleyverse.github.io/jaxley/tutorial/03_setting_parameters/)
-- [speed up simulations with jit and vmap](https://jaxleyverse.github.io/jaxley/tutorial/04_jit_and_vmap/)
-- [define your own channels and synapses](https://jaxleyverse.github.io/jaxley/tutorial/05_channel_and_synapse_models/)
-- [define groups](https://jaxleyverse.github.io/jaxley/tutorial/06_groups/)
-- [read and handle SWC files](https://jaxleyverse.github.io/jaxley/tutorial/08_importing_morphologies/)
-- [train biophysical models](https://jaxleyverse.github.io/jaxley/tutorial/07_gradient_descent/)
+`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.
 
-### Units
+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.
 
-`Jaxley` uses the same [units as `NEURON`](https://www.neuron.yale.edu/neuron/static/docs/units/unitchart.html).
+v = jx.integrate(cell)  # Run simulation.
+plt.plot(v.T)  # Plot voltage trace.
+```
+
+If you want to learn more, we recommend you to check out our tutorials on how to:
 
+- [get started with `Jaxley`](https://jaxleyverse.github.io/jaxley/tutorial/01_morph_neurons/)
+- [simulate networks of neurons](https://jaxleyverse.github.io/jaxley/tutorial/02_small_network/)
+- [speed up simulations with GPUs and `jit`](https://jaxleyverse.github.io/jaxley/tutorial/04_jit_and_vmap/)
+- [define your own channels and synapses](https://jaxleyverse.github.io/jaxley/tutorial/05_channel_and_synapse_models/)
+- [compute the gradient and train biophysical models](https://jaxleyverse.github.io/jaxley/tutorial/07_gradient_descent/)
+
+
+## Installation
 
-### Installation
 `Jaxley` is available on [`PyPI`](https://pypi.org/project/jaxley/):
 ```sh
 pip install jaxley
@@ -48,22 +63,17 @@ pip install -U "jax[cuda12]"
 ```
 
 
-### Feedback and Contributions
+## 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).
 
 
-### Acknowledgements
-
-We greatly benefited from previous toolboxes for simulating multicompartment neurons, in particular [NEURON](https://github.com/neuronsimulator/nrn).
-
-
-### License
+## License
 
-[Apache License](https://github.com/jaxleyverse/jaxley/blob/main/LICENSE)
+[Apache License Version 2.0 (Apache-2.0)](https://github.com/jaxleyverse/jaxley/blob/main/LICENSE)
 
 
-### Citation
+## Citation
 
 If you use `Jaxley`, consider citing the [corresponding paper](https://www.biorxiv.org/content/10.1101/2024.08.21.608979):
 

diff --git a/docs/docs/credits.md b/docs/docs/credits.md
@@ -4,11 +4,16 @@
 
 ## License
 
-`Jaxley` is licensed under the [Apache License](https://github.com/jaxleyverse/jaxley/blob/main/LICENSE) and
+`Jaxley` is licensed under the [Apache License Version 2.0 (Apache-2.0)](https://github.com/jaxleyverse/jaxley/blob/main/LICENSE) and
 
 > Copyright (C) 2024 Michael Deistler, Jakob H. Macke, Pedro J. Goncalves, Philipp Berens.
 
 
 ## Important dependencies and prior art
 
-* We greatly benefited from previous toolboxes for simulating multicompartment neurons, in particular [NEURON](https://github.com/neuronsimulator/nrn)
+* We greatly benefited from previous toolboxes for simulating multicompartment neurons, in particular [NEURON](https://github.com/neuronsimulator/nrn).
+
+
+## Funding
+
+This work was supported by the German Research Foundation (DFG) through Germany's Excellence Strategy (EXC 2064 – Project number 390727645) and the CRC 1233 "Robust Vision", the German Federal Ministry of Education and Research (Tübingen AI Center, FKZ: 01IS18039A), the 'Certification and Foundations of Safe Machine Learning Systems in Healthcare' project funded by the Carl Zeiss Foundation, and the European Union (ERC, "DeepCoMechTome", ref. 101089288, "NextMechMod", ref. 101039115).
diff --git a/docs/docs/faq.md b/docs/docs/faq.md
@@ -1,3 +1,8 @@
 # Frequently asked questions
 
-[How can I save and load cells and networks?](faq/question_01.md)
+- [What units does `Jaxley` use?](faq/question_01.md)  
+- [How can I save and load cells and networks?](faq/question_02.md)  
+
+See also the [discussion page](https://github.com/jaxleyverse/jaxley/discussions) and the [issue
+tracker](https://github.com/jaxleyverse/jaxley/issues) on the `Jaxley` GitHub repository for
+recent questions and problems.
diff --git a/docs/docs/faq/question_01.md b/docs/docs/faq/question_01.md
@@ -1,18 +1,3 @@
-# How can I save and load cells and networks?
+# What units does `Jaxley` use?
 
-All `module`s (i.e., compartments, branches, cells, and networks) in `Jaxley` can be saved and loaded with pickle:
-```python
-import jaxley as jx
-import pickle
-
-# ... define network, cell, etc.
-network = jx.Network([cell1, cell2])
-
-# Save.
-with open("path/to/file.pkl", "wb") as handle:
-    pickle.dump(network, handle)
-
-# Load.
-with open("path/to/file.pkl", "rb") as handle:
-    network = pickle.load(handle)
-```
+`Jaxley` uses the same units as the `NEURON` simulator, which are listed [here](https://www.neuron.yale.edu/neuron/static/docs/units/unitchart.html).
diff --git a/docs/docs/faq/question_02.md b/docs/docs/faq/question_02.md
@@ -0,0 +1,18 @@
+# How can I save and load cells and networks?
+
+All `module`s (i.e., compartments, branches, cells, and networks) in `Jaxley` can be saved and loaded with pickle:
+```python
+import jaxley as jx
+import pickle
+
+# ... define network, cell, etc.
+network = jx.Network([cell1, cell2])
+
+# Save.
+with open("path/to/file.pkl", "wb") as handle:
+    pickle.dump(network, handle)
+
+# Load.
+with open("path/to/file.pkl", "rb") as handle:
+    network = pickle.load(handle)
+```
diff --git a/docs/docs/index.md b/docs/docs/index.md
@@ -12,26 +12,43 @@
 - elegant mechanisms for parameter sharing
 
 
-## Tutorials
+## Getting started
 
-We currently have tutorials on how to:
+`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.
+```
+
+If you want to learn more, we have tutorials on how to:
 
 - [simulate morphologically detailed neurons](https://jaxleyverse.github.io/jaxley/tutorial/01_morph_neurons/)
 - [simulate networks of such neurons](https://jaxleyverse.github.io/jaxley/tutorial/02_small_network/)
 - [set parameters of cells and networks](https://jaxleyverse.github.io/jaxley/tutorial/03_setting_parameters/)
-- [speed up simulations with jit and vmap](https://jaxleyverse.github.io/jaxley/tutorial/04_jit_and_vmap/)
+- [speed up simulations with GPUs and jit](https://jaxleyverse.github.io/jaxley/tutorial/04_jit_and_vmap/)
 - [define your own channels and synapses](https://jaxleyverse.github.io/jaxley/tutorial/05_channel_and_synapse_models/)
 - [define groups](https://jaxleyverse.github.io/jaxley/tutorial/06_groups/)
 - [read and handle SWC files](https://jaxleyverse.github.io/jaxley/tutorial/08_importing_morphologies/)
-- [train biophysical models](https://jaxleyverse.github.io/jaxley/tutorial/07_gradient_descent/)
-
-
-## Units
-
-`Jaxley` uses the same [units as `NEURON`](https://www.neuron.yale.edu/neuron/static/docs/units/unitchart.html).
+- [compute the gradient and train biophysical models](https://jaxleyverse.github.io/jaxley/tutorial/07_gradient_descent/)
 
 
 ## Installation
+
 `Jaxley` is available on [`pypi`](https://pypi.org/project/jaxley/):
 ```sh
 pip install jaxley
@@ -47,17 +64,12 @@ pip install -U "jax[cuda12]"
 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).
 
 
-## Acknowledgements
-
-We greatly benefited from previous toolboxes for simulating multicompartment neurons, in particular [NEURON](https://github.com/neuronsimulator/nrn).
-
-
 ## License
 
-[Apache License](https://github.com/jaxleyverse/jaxley/blob/main/LICENSE)
+[Apache License Version 2.0 (Apache-2.0)](https://github.com/jaxleyverse/jaxley/blob/main/LICENSE)
 
 
-### Citation
+## Citation
 
 If you use `Jaxley`, consider citing the [corresponding paper](https://www.biorxiv.org/content/10.1101/2024.08.21.608979):
 

diff --git a/tutorials/01_morph_neurons.ipynb b/tutorials/01_morph_neurons.ipynb
@@ -28,7 +28,7 @@
     "import matplotlib.pyplot as plt\n",
     "\n",
     "\n",
-    "# Build the network.\n",
+    "# Build the cell.\n",
     "comp = jx.Compartment()\n",
     "branch = jx.Branch(comp, nseg=4)\n",
     "cell = jx.Cell(branch, parents=[-1, 0, 0, 1, 1])\n",
@@ -44,14 +44,14 @@
     "cell.vis(ax=ax)\n",
     "\n",
     "# Stimulate.\n",
-    "current = jx.step_current(i_delay=1.0, i_dur=1.0, i_amp=0.1, dt=0.025, t_max=10.0)\n",
+    "current = jx.step_current(i_delay=1.0, i_dur=1.0, i_amp=0.1, delta_t=0.025, t_max=10.0)\n",
     "cell.branch(0).loc(0.0).stimulate(current)\n",
     "\n",
     "# Record.\n",
     "cell.branch(0).loc(0.0).record(\"v\")\n",
     "\n",
     "# Simulate and plot.\n",
-    "v = jx.integrate(net)\n",
+    "v = jx.integrate(cell)\n",
     "plt.plot(v.T)\n",
     "```"
    ]
@@ -247,7 +247,7 @@
    "source": [
     "### Stimulate the cell\n",
     "\n",
-    "We next stimulate one of the compartments with a step current. For this, we first define the step current:"
+    "We next stimulate one of the compartments with a step current. For this, we first define the step current (all units are the same as for the `NEURON` simulator, which are listed [here](https://www.neuron.yale.edu/neuron/static/docs/units/unitchart.html)):"
    ]
   },
   {