Merge branch 'main' into continue_better_viewing

jaxleyverse · Oct 24, 2024 · d922f3d · d922f3d
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diff --git a/docs/tutorials/05_channel_and_synapse_models.ipynb b/docs/tutorials/05_channel_and_synapse_models.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "c19e95c6",
+   "id": "84d5d597",
    "metadata": {},
    "source": [
     "# Building and using ion channel models\n",
@@ -17,7 +17,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "11544a58",
+   "id": "a9a16457",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e141cff7-ffc1-4e4a-bfe5-2b87792de748",
+   "id": "1a91e1c7",
    "metadata": {},
    "source": [
     "First, we define a cell as you saw in the [previous tutorial](https://jaxleyverse.github.io/jaxley/latest/tutorial/01_morph_neurons/):"
@@ -45,7 +45,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "b308ed28-193c-4c15-be6c-019d81c03883",
+   "id": "082c1774",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -56,7 +56,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4257adb3-df24-4a03-982c-0ae0983067b1",
+   "id": "9c181374",
    "metadata": {},
    "source": [
     "You have also already learned how to insert preconfigured channels into `Jaxley` models:\n",
@@ -71,7 +71,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b4521def",
+   "id": "fc631157",
    "metadata": {},
    "source": [
     "### Your own channel\n",
@@ -81,7 +81,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "6edc6581",
+   "id": "e8d96197",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -97,6 +97,7 @@
     "    \"\"\"Potassium channel.\"\"\"\n",
     "\n",
     "    def __init__(self, name = None):\n",
+    "        self.current_is_in_mA_per_cm2 = True\n",
     "        super().__init__(name)\n",
     "        self.channel_params = {\"gK_new\": 1e-4}\n",
     "        self.channel_states = {\"n_new\": 0.0}\n",
@@ -113,9 +114,7 @@
     "    def compute_current(self, states, v, params):\n",
     "        \"\"\"Return current.\"\"\"\n",
     "        ns = states[\"n_new\"]\n",
-    "        \n",
-    "        # Multiply with 1000 to convert Siemens to milli Siemens.\n",
-    "        kd_conds = params[\"gK_new\"] * ns**4 * 1000  # mS/cm^2\n",
+    "        kd_conds = params[\"gK_new\"] * ns**4  # S/cm^2\n",
     "\n",
     "        e_kd = -77.0        \n",
     "        return kd_conds * (v - e_kd)\n",
@@ -128,7 +127,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "204afb5b",
+   "id": "c7812bf1",
    "metadata": {},
    "source": [
     "Let's look at each part of this in detail. \n",
@@ -139,12 +138,13 @@
     "    return x / (jnp.exp(x / y) - 1.0)\n",
     "```\n",
     "\n",
-    "Next, we define our channel as a class. It should inherit from the `Channel` class and define `channel_params`, `channel_states`, and `current_name`.\n",
+    "Next, we define our channel as a class. It should inherit from the `Channel` class and define `channel_params`, `channel_states`, and `current_name`. You also need to set `self.current_is_in_mA_per_cm2=True` as the first line on your `__init__()` method. This is to acknowledge that your current is returned in `mA/cm2` (not in `uA/cm2`, as would have been required in Jaxley versions 0.4.0 or older).\n",
     "```python\n",
     "class Potassium(Channel):\n",
     "    \"\"\"Potassium channel.\"\"\"\n",
     "\n",
     "    def __init__(self, name=None):\n",
+    "        self.current_is_in_mA_per_cm2 = True\n",
     "        super().__init__(name)\n",
     "        self.channel_params = {\"gK_new\": 1e-4}\n",
     "        self.channel_states = {\"n_new\": 0.0}\n",
@@ -175,7 +175,7 @@
     "        ns = states[\"n_new\"]\n",
     "        \n",
     "        # Multiply with 1000 to convert Siemens to milli Siemens.\n",
-    "        kd_conds = params[\"gK_new\"] * ns**4 * 1000  # mS/cm^2\n",
+    "        kd_conds = params[\"gK_new\"] * ns**4  # S/cm^2\n",
     "\n",
     "        e_kd = -77.0        \n",
     "        current = kd_conds * (v - e_kd)\n",
@@ -187,7 +187,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c6c17ea2",
+   "id": "794f6279",
    "metadata": {},
    "source": [
     "Alright, done! We can now insert this channel into any `jx.Module` such as our cell:"
@@ -196,7 +196,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "8bc79fc5",
+   "id": "94c270e3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -206,7 +206,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "23447b7e-7554-4e82-b702-db0cb6b517ca",
+   "id": "313ee72f",
    "metadata": {},
    "outputs": [
     {
@@ -230,7 +230,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "f4c678f2",
+   "id": "08a81449",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -240,7 +240,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "1a10abc0",
+   "id": "4318f1bc",
    "metadata": {},
    "outputs": [
     {
@@ -264,7 +264,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "43c9f1ea",
+   "id": "e302590d",
    "metadata": {},
    "source": [
     "### Your own synapse\n",
@@ -277,7 +277,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "62c719f9",
+   "id": "5c68fee6",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -308,7 +308,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0de23892-4147-4ca2-a39d-97e4985c8e27",
+   "id": "d608b85c",
    "metadata": {},
    "source": [
     "As you can see above, synapses follow closely how channels are defined. The main difference is that the `compute_current` method takes two voltages: the pre-synaptic voltage (a `jnp.ndarray` of shape `()`) and the post-synaptic voltage (a `jnp.ndarray` of shape `()`)."
@@ -317,7 +317,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "97ee3c49",
+   "id": "0652ace1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -327,7 +327,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "a8c9428c-d0dc-4892-966e-11a43aea216d",
+   "id": "69dde19f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -341,7 +341,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "fc6fd5a2",
+   "id": "4f19d456",
    "metadata": {},
    "outputs": [
     {
@@ -364,7 +364,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "867f4f38",
+   "id": "3e955552",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -374,7 +374,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "01782d18",
+   "id": "9cfc8f33",
    "metadata": {},
    "outputs": [
     {
@@ -398,7 +398,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5b944bee-bf80-4f6b-a5d2-5cd119e6d4a4",
+   "id": "e3f88051",
    "metadata": {},
    "source": [
     "That's it! You are now ready to build your own custom simulations and equip them with channel and synapse models!\n",