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Explictly import Na,K,Leak from pospischil #552

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98 changes: 49 additions & 49 deletions docs/tutorials/00_jaxley_api.ipynb
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94 changes: 47 additions & 47 deletions docs/tutorials/01_morph_neurons.ipynb
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78 changes: 39 additions & 39 deletions docs/tutorials/02_small_network.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -2,15 +2,15 @@
"cells": [
{
"cell_type": "markdown",
"id": "2ec9dafe",
"id": "b81099a7",
"metadata": {},
"source": [
"# Network simulations in Jaxley"
]
},
{
"cell_type": "markdown",
"id": "55ee1cd1",
"id": "db86d64f",
"metadata": {},
"source": [
"In this tutorial, you will learn how to:\n",
Expand Down Expand Up @@ -48,7 +48,7 @@
},
{
"cell_type": "markdown",
"id": "8523c3de",
"id": "0d06fdbf",
"metadata": {},
"source": [
"In the previous tutorial, you learned how to build single cells with morphological detail, how to insert stimuli and recordings, and how to run a first simulation. In this tutorial, we will define networks of multiple cells and connect them with synapses. Let's get started:"
Expand All @@ -57,7 +57,7 @@
{
"cell_type": "code",
"execution_count": 1,
"id": "de136490",
"id": "6be2c63c",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -71,14 +71,14 @@
"from jax import jit\n",
"\n",
"import jaxley as jx\n",
"from jaxley.channels import Na, K, Leak\n",
"from jaxley.channels.pospischil import Na, K, Leak\n",
"from jaxley.synapses import IonotropicSynapse\n",
"from jaxley.connect import fully_connect, connect"
]
},
{
"cell_type": "markdown",
"id": "cf137e23",
"id": "d1d8cbcb",
"metadata": {},
"source": [
"### Define the network\n",
Expand All @@ -89,7 +89,7 @@
{
"cell_type": "code",
"execution_count": 2,
"id": "35258917",
"id": "86658cc6",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -100,7 +100,7 @@
},
{
"cell_type": "markdown",
"id": "fe0920be",
"id": "de32deb1",
"metadata": {},
"source": [
"We can assemble multiple cells into a network by using `jx.Network`, which takes a list of `jx.Cell`s. Here, we assemble 11 cells into a network:"
Expand All @@ -109,7 +109,7 @@
{
"cell_type": "code",
"execution_count": 3,
"id": "6a3c676e",
"id": "9d6687b5",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -119,7 +119,7 @@
},
{
"cell_type": "markdown",
"id": "90445c64",
"id": "47430928",
"metadata": {},
"source": [
"At this point, we can already visualize this network:"
Expand All @@ -128,7 +128,7 @@
{
"cell_type": "code",
"execution_count": 5,
"id": "86ea234a",
"id": "b8544018",
"metadata": {},
"outputs": [
{
Expand All @@ -153,15 +153,15 @@
},
{
"cell_type": "markdown",
"id": "d7d9fc7d",
"id": "3974b1f6",
"metadata": {},
"source": [
"_Note: you can use `move_to` to have more control over the location of cells, e.g.: `network.cell(i).move_to(x=0, y=200)`._"
]
},
{
"cell_type": "markdown",
"id": "7201bb0d",
"id": "add93e88",
"metadata": {},
"source": [
"As you can see, the neurons are not connected yet. Let's fix this by connecting neurons with synapses. We will build a network consisting of two layers: 10 neurons in the input layer and 1 neuron in the output layer.\n",
Expand All @@ -172,7 +172,7 @@
{
"cell_type": "code",
"execution_count": 6,
"id": "429054e3",
"id": "bd2d4fe2",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -183,7 +183,7 @@
},
{
"cell_type": "markdown",
"id": "99ea65dd",
"id": "d81cf79c",
"metadata": {},
"source": [
"Let's visualize this again:"
Expand All @@ -192,7 +192,7 @@
{
"cell_type": "code",
"execution_count": 9,
"id": "28f133ed",
"id": "b41bef87",
"metadata": {},
"outputs": [
{
Expand All @@ -213,7 +213,7 @@
},
{
"cell_type": "markdown",
"id": "843ffa0b",
"id": "624549bf",
"metadata": {},
"source": [
"As you can see, the `full_connect` method inserted one synapse (in blue) from every neuron in the first layer to the output neuron. The `fully_connect` method builds this synapse from the zero-eth compartment and zero-eth branch of the presynaptic neuron onto a random branch of the postsynaptic neuron. If you want more control over the pre- and post-synaptic branches, you can use the `connect` method:"
Expand All @@ -222,7 +222,7 @@
{
"cell_type": "code",
"execution_count": 10,
"id": "2508510c",
"id": "63a8b1b3",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -234,7 +234,7 @@
{
"cell_type": "code",
"execution_count": 11,
"id": "60790ead",
"id": "19bcadcc",
"metadata": {},
"outputs": [
{
Expand All @@ -255,15 +255,15 @@
},
{
"cell_type": "markdown",
"id": "08422604",
"id": "79841345",
"metadata": {},
"source": [
"### Inspecting and changing synaptic parameters"
]
},
{
"cell_type": "markdown",
"id": "8515ca40",
"id": "bfa8b06d",
"metadata": {},
"source": [
"You can inspect synaptic parameters via the `.edges` attribute:"
Expand All @@ -272,7 +272,7 @@
{
"cell_type": "code",
"execution_count": 12,
"id": "b8f05aa0",
"id": "0b2c4040",
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -545,7 +545,7 @@
},
{
"cell_type": "markdown",
"id": "4c57d8f2",
"id": "791c9ca9",
"metadata": {},
"source": [
"To modify a parameter of all synapses you can again use `.set()`:"
Expand All @@ -554,7 +554,7 @@
{
"cell_type": "code",
"execution_count": 13,
"id": "7d295f14",
"id": "a83d92a5",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -563,7 +563,7 @@
},
{
"cell_type": "markdown",
"id": "d74dafa0",
"id": "799a169b",
"metadata": {},
"source": [
"To modify individual syanptic parameters, use the `.select()` method. Below, we change the values of the first two synapses:"
Expand All @@ -572,7 +572,7 @@
{
"cell_type": "code",
"execution_count": 14,
"id": "f7ba4d28",
"id": "ca7e2ec9",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -581,23 +581,23 @@
},
{
"cell_type": "markdown",
"id": "a9d4f9fc",
"id": "652f56b4",
"metadata": {},
"source": [
"For more details on how to flexibly set synaptic parameters (e.g., by cell type, or by pre-synaptic cell index,...), see [this tutorial](https://jaxley.readthedocs.io/en/latest/tutorials/09_advanced_indexing.html)."
]
},
{
"cell_type": "markdown",
"id": "fa3826a7",
"id": "447b23fa",
"metadata": {},
"source": [
"### Stimulating, recording, and simulating the network"
]
},
{
"cell_type": "markdown",
"id": "4cdff397",
"id": "3a084fd7",
"metadata": {},
"source": [
"We will now set up a simulation of the network. This works exactly as it does for single neurons:"
Expand All @@ -606,7 +606,7 @@
{
"cell_type": "code",
"execution_count": 15,
"id": "70d3f34a",
"id": "b937dc28",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -623,7 +623,7 @@
{
"cell_type": "code",
"execution_count": 16,
"id": "4eeb188d",
"id": "69f7e3ba",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -632,7 +632,7 @@
},
{
"cell_type": "markdown",
"id": "544cdeef",
"id": "53f074d1",
"metadata": {},
"source": [
"As a simple example, we insert sodium, potassium, and leak into every compartment of every cell of the network."
Expand All @@ -641,7 +641,7 @@
{
"cell_type": "code",
"execution_count": 17,
"id": "22a17a74",
"id": "4d1a2844",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -652,7 +652,7 @@
},
{
"cell_type": "markdown",
"id": "e969f523",
"id": "f06115d7",
"metadata": {},
"source": [
"We stimulate every neuron in the input layer and record the voltage from the output neuron:"
Expand All @@ -661,7 +661,7 @@
{
"cell_type": "code",
"execution_count": 18,
"id": "e66b5f02",
"id": "7a0f4924",
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -694,7 +694,7 @@
},
{
"cell_type": "markdown",
"id": "e8ae006c",
"id": "0e72be62",
"metadata": {},
"source": [
"Finally, we can again run the network simulation and plot the result:"
Expand All @@ -703,7 +703,7 @@
{
"cell_type": "code",
"execution_count": 19,
"id": "71146fc4",
"id": "9ebd5787",
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -713,7 +713,7 @@
{
"cell_type": "code",
"execution_count": 20,
"id": "b6905f50",
"id": "fee2609d",
"metadata": {},
"outputs": [
{
Expand All @@ -734,7 +734,7 @@
},
{
"cell_type": "markdown",
"id": "57faa9ca",
"id": "262493ff",
"metadata": {},
"source": [
"That's it! You now know how to simulate networks of morphologically detailed neurons. We recommend that you now have a look at how you can [speed up your simulation](https://jaxley.readthedocs.io/en/latest/tutorials/04_jit_and_vmap.html). To learn more about handling synaptic parameters, we recommend to check out [this tutorial](https://jaxley.readthedocs.io/en/latest/tutorials/09_advanced_indexing.html)."
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