diff --git a/docs/tutorials/00_jaxley_api.ipynb b/docs/tutorials/00_jaxley_api.ipynb index cbe6399a..0be21db2 100644 --- a/docs/tutorials/00_jaxley_api.ipynb +++ b/docs/tutorials/00_jaxley_api.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "89896082", + "id": "3c1e649a", "metadata": {}, "source": [ "# Key concepts in Jaxley" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "a0404fbc", + "id": "b568368e", "metadata": {}, "source": [ "In this tutorial, we will introduce you to the basic concepts of Jaxley.\n", @@ -62,7 +62,7 @@ }, { "cell_type": "markdown", - "id": "371479f9", + "id": "e75fa02c", "metadata": {}, "source": [ "First, we import the relevant libraries:" @@ -71,7 +71,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "08ded085", + "id": "5d339435", "metadata": {}, "outputs": [], "source": [ @@ -89,7 +89,7 @@ }, { "cell_type": "markdown", - "id": "1676c025", + "id": "bcdc4ee6", "metadata": {}, "source": [ "## Modules\n", @@ -106,7 +106,7 @@ }, { "cell_type": "markdown", - "id": "a4f282da", + "id": "4a48a634", "metadata": {}, "source": [ "`Compartment`s are the atoms of biophysical models in Jaxley. All mechanisms and synaptic connections live on the level of `Compartment`s and can already be simulated using `jx.integrate` on their own. Everything you do in Jaxley starts with a `Compartment`." @@ -115,7 +115,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "e971f15c", + "id": "d91f58b9", "metadata": {}, "outputs": [], "source": [ @@ -124,7 +124,7 @@ }, { "cell_type": "markdown", - "id": "da4eac1d", + "id": "5af852c6", "metadata": {}, "source": [ "Mutliple `Compartments` can be connected together to form longer, linear cables, which we call `Branch`es and are equivalent to sections in `NEURON`." @@ -133,7 +133,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "ec10bf01", + "id": "cccb78dd", "metadata": {}, "outputs": [], "source": [ @@ -143,7 +143,7 @@ }, { "cell_type": "markdown", - "id": "9b299579", + "id": "d2c7e12e", "metadata": {}, "source": [ "In order to construct cell morphologies in Jaxley, multiple `Branches` can to be connected together as a `Cell`:" @@ -152,7 +152,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "ded94f2d", + "id": "73648241", "metadata": {}, "outputs": [], "source": [ @@ -164,7 +164,7 @@ }, { "cell_type": "markdown", - "id": "717fee25", + "id": "0f79bbb6", "metadata": {}, "source": [ "Finally, several `Cell`s can be grouped together to form a `Network`, which can than be connected together using `Synpase`s." @@ -173,7 +173,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "1944ddc9", + "id": "27e53ea3", "metadata": {}, "outputs": [ { @@ -196,7 +196,7 @@ }, { "cell_type": "markdown", - "id": "a4cdb4c1", + "id": "79d70cc7", "metadata": {}, "source": [ "Every module tracks information about its current state and parameters in two Dataframes called `nodes` and `edges`.\n", @@ -208,7 +208,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "f5a13fb0", + "id": "310be603", "metadata": {}, "outputs": [ { @@ -703,7 +703,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "fa4e353c", + "id": "ed173a54", "metadata": {}, "outputs": [ { @@ -728,8 +728,8 @@ " \n", " \n", " global_edge_index\n", - " global_pre_comp_index\n", - " global_post_comp_index\n", + " pre_global_comp_index\n", + " post_global_comp_index\n", " pre_locs\n", " post_locs\n", " type\n", @@ -743,7 +743,7 @@ ], "text/plain": [ "Empty DataFrame\n", - "Columns: [global_edge_index, global_pre_comp_index, global_post_comp_index, pre_locs, post_locs, type, type_ind]\n", + "Columns: [global_edge_index, pre_global_comp_index, post_global_comp_index, pre_locs, post_locs, type, type_ind]\n", "Index: []" ] }, @@ -758,7 +758,7 @@ }, { "cell_type": "markdown", - "id": "43c42d43", + "id": "332874fa", "metadata": {}, "source": [ "## Views" @@ -766,7 +766,7 @@ }, { "cell_type": "markdown", - "id": "942ecf64", + "id": "7bdaf569", "metadata": {}, "source": [ "Since these `Module`s can become very complex, Jaxley utilizes so called `View`s to make working with `Module`s easy and intuitive. \n", @@ -777,7 +777,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "3885678c", + "id": "1da735ab", "metadata": {}, "outputs": [ { @@ -797,7 +797,7 @@ }, { "cell_type": "markdown", - "id": "82357af7", + "id": "220266a0", "metadata": {}, "source": [ "Views behave very similarly to `Module`s, i.e. the `cell(0)` (the 0th cell of the network) behaves like the `cell` we instantiated earlier. As such, `cell(0)` also has a `nodes` attribute, which keeps track of it's part of the network:" @@ -806,7 +806,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "c272cecb", + "id": "dfbbeb88", "metadata": {}, "outputs": [ { @@ -1084,7 +1084,7 @@ }, { "cell_type": "markdown", - "id": "083f8351", + "id": "f1460cad", "metadata": {}, "source": [ "Let's use `View`s to visualize only parts of the `Network`. Before we do that, we create x, y, and z coordinates for the `Network`:" @@ -1093,7 +1093,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "268e253a", + "id": "6cea4177", "metadata": {}, "outputs": [], "source": [ @@ -1106,7 +1106,7 @@ }, { "cell_type": "markdown", - "id": "7fda5d83", + "id": "ac86b8d4", "metadata": {}, "source": [ "We can now visualize the entire `net` (i.e., the entire `Module`) with the `.vis()` method..." @@ -1115,7 +1115,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "632192d3", + "id": "19c81286", "metadata": {}, "outputs": [ { @@ -1147,7 +1147,7 @@ }, { "cell_type": "markdown", - "id": "37fafc71", + "id": "5f9d3494", "metadata": {}, "source": [ "...but we can also create a `View` to visualize only parts of the `net`:" @@ -1155,8 +1155,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "14a4e51a", + "execution_count": 13, + "id": "9c9e7efd", "metadata": {}, "outputs": [ { @@ -1165,13 +1165,13 @@ "" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1183,16 +1183,16 @@ "source": [ "# ... and Views\n", "fig, ax = plt.subplots(1,1, figsize=(3,3))\n", - "net.cell(0).vis(ax=ax, col=\"blue\") # View of the 0th cell of the network\n", - "net.cell(1).vis(ax=ax, col=\"red\") # View of the 1st cell of the network\n", + "net.cell(0).vis(ax=ax, color=\"blue\") # View of the 0th cell of the network\n", + "net.cell(1).vis(ax=ax, color=\"red\") # View of the 1st cell of the network\n", "\n", - "net.cell(0).branch(0).vis(ax=ax, col=\"green\") # View of the 1st branch of the 0th cell of the network\n", - "net.cell(1).branch(1).comp(1).vis(ax=ax, col=\"black\", type=\"scatter\") # View of the 0th comp of the 1st branch of the 0th cell of the network" + "net.cell(0).branch(0).vis(ax=ax, color=\"green\") # View of the 1st branch of the 0th cell of the network\n", + "net.cell(1).branch(1).comp(1).vis(ax=ax, color=\"black\", type=\"line\") # View of the 0th comp of the 1st branch of the 0th cell of the network" ] }, { "cell_type": "markdown", - "id": "1d20882d", + "id": "d82f4768", "metadata": {}, "source": [ "### How to create `View`s" @@ -1200,7 +1200,7 @@ }, { "cell_type": "markdown", - "id": "857c2def", + "id": "12b6fda3", "metadata": {}, "source": [ "Above, we used `net.cell(0)` to generate a `View` of the 0-eth cell. `Jaxley` supports many ways of performing such indexing:" @@ -1208,8 +1208,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "id": "728f6eb0", + "execution_count": 14, + "id": "4785cbe2", "metadata": {}, "outputs": [ { @@ -1218,7 +1218,7 @@ "View with 0 different channels. Use `.nodes` for details." ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1237,8 +1237,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "id": "fe4dda8e", + "execution_count": 15, + "id": "aa62bd42", "metadata": {}, "outputs": [ { @@ -1270,9 +1270,6 @@ " axial_resistivity\n", " capacitance\n", " v\n", - " x\n", - " y\n", - " z\n", " global_cell_index\n", " global_branch_index\n", " global_comp_index\n", @@ -1290,9 +1287,6 @@ " 5000.0\n", " 1.0\n", " -70.0\n", - " 5.000000\n", - " 30.000000\n", - " 0.0\n", " 0\n", " 0\n", " 0\n", @@ -1308,9 +1302,6 @@ " 5000.0\n", " 1.0\n", " -70.0\n", - " 15.000000\n", - " 30.000000\n", - " 0.0\n", " 0\n", " 0\n", " 1\n", @@ -1326,9 +1317,6 @@ " 5000.0\n", " 1.0\n", " -70.0\n", - " 25.000000\n", - " 30.000000\n", - " 0.0\n", " 0\n", " 0\n", " 2\n", @@ -1344,9 +1332,6 @@ " 5000.0\n", " 1.0\n", " -70.0\n", - " 35.000000\n", - " 30.000000\n", - " 0.0\n", " 0\n", " 0\n", " 3\n", @@ -1362,9 +1347,6 @@ " 5000.0\n", " 1.0\n", " -70.0\n", - " 44.850713\n", - " 28.787322\n", - " 0.0\n", " 0\n", " 1\n", " 4\n", @@ -1380,9 +1362,6 @@ " 5000.0\n", " 1.0\n", " -70.0\n", - " 54.552138\n", - " 26.361966\n", - " 0.0\n", " 0\n", " 1\n", " 5\n", @@ -1398,9 +1377,6 @@ " 5000.0\n", " 1.0\n", " -70.0\n", - " 64.253563\n", - " 23.936609\n", - " 0.0\n", " 0\n", " 1\n", " 6\n", @@ -1416,9 +1392,6 @@ " 5000.0\n", " 1.0\n", " -70.0\n", - " 73.954988\n", - " 21.511253\n", - " 0.0\n", " 0\n", " 1\n", " 7\n", @@ -1434,9 +1407,6 @@ " 5000.0\n", " 1.0\n", " -70.0\n", - " 44.850713\n", - " 31.212678\n", - " 0.0\n", " 0\n", " 2\n", " 8\n", @@ -1452,9 +1422,6 @@ " 5000.0\n", " 1.0\n", " -70.0\n", - " 54.552138\n", - " 33.638034\n", - " 0.0\n", " 0\n", " 2\n", " 9\n", @@ -1470,9 +1437,6 @@ " 5000.0\n", " 1.0\n", " -70.0\n", - " 64.253563\n", - " 36.063391\n", - " 0.0\n", " 0\n", " 2\n", " 10\n", @@ -1488,9 +1452,6 @@ " 5000.0\n", " 1.0\n", " -70.0\n", - " 73.954988\n", - " 38.488747\n", - " 0.0\n", " 0\n", " 2\n", " 11\n", @@ -1515,50 +1476,36 @@ "10 0 2 2 10.0 1.0 \n", "11 0 2 3 10.0 1.0 \n", "\n", - " axial_resistivity capacitance v x y z \\\n", - "0 5000.0 1.0 -70.0 5.000000 30.000000 0.0 \n", - "1 5000.0 1.0 -70.0 15.000000 30.000000 0.0 \n", - "2 5000.0 1.0 -70.0 25.000000 30.000000 0.0 \n", - "3 5000.0 1.0 -70.0 35.000000 30.000000 0.0 \n", - "4 5000.0 1.0 -70.0 44.850713 28.787322 0.0 \n", - "5 5000.0 1.0 -70.0 54.552138 26.361966 0.0 \n", - "6 5000.0 1.0 -70.0 64.253563 23.936609 0.0 \n", - "7 5000.0 1.0 -70.0 73.954988 21.511253 0.0 \n", - "8 5000.0 1.0 -70.0 44.850713 31.212678 0.0 \n", - "9 5000.0 1.0 -70.0 54.552138 33.638034 0.0 \n", - "10 5000.0 1.0 -70.0 64.253563 36.063391 0.0 \n", - "11 5000.0 1.0 -70.0 73.954988 38.488747 0.0 \n", - "\n", - " global_cell_index global_branch_index global_comp_index \\\n", - "0 0 0 0 \n", - "1 0 0 1 \n", - "2 0 0 2 \n", - "3 0 0 3 \n", - "4 0 1 4 \n", - "5 0 1 5 \n", - "6 0 1 6 \n", - "7 0 1 7 \n", - "8 0 2 8 \n", - "9 0 2 9 \n", - "10 0 2 10 \n", - "11 0 2 11 \n", + " axial_resistivity capacitance v global_cell_index \\\n", + "0 5000.0 1.0 -70.0 0 \n", + "1 5000.0 1.0 -70.0 0 \n", + "2 5000.0 1.0 -70.0 0 \n", + "3 5000.0 1.0 -70.0 0 \n", + "4 5000.0 1.0 -70.0 0 \n", + "5 5000.0 1.0 -70.0 0 \n", + "6 5000.0 1.0 -70.0 0 \n", + "7 5000.0 1.0 -70.0 0 \n", + "8 5000.0 1.0 -70.0 0 \n", + "9 5000.0 1.0 -70.0 0 \n", + "10 5000.0 1.0 -70.0 0 \n", + "11 5000.0 1.0 -70.0 0 \n", "\n", - " controlled_by_param \n", - "0 0 \n", - "1 0 \n", - "2 0 \n", - "3 0 \n", - "4 0 \n", - "5 0 \n", - "6 0 \n", - "7 0 \n", - "8 0 \n", - "9 0 \n", - "10 0 \n", - "11 0 " + " global_branch_index global_comp_index controlled_by_param \n", + "0 0 0 0 \n", + "1 0 1 0 \n", + "2 0 2 0 \n", + "3 0 3 0 \n", + "4 1 4 0 \n", + "5 1 5 0 \n", + "6 1 6 0 \n", + "7 1 7 0 \n", + "8 2 8 0 \n", + "9 2 9 0 \n", + "10 2 10 0 \n", + "11 2 11 0 " ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1569,8 +1516,8 @@ }, { "cell_type": "code", - "execution_count": 15, - "id": "012b9612", + "execution_count": 16, + "id": "237f7c22", "metadata": {}, "outputs": [ { @@ -1579,7 +1526,7 @@ "(2, 6, 24)" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1590,7 +1537,7 @@ }, { "cell_type": "markdown", - "id": "42d8ffdd", + "id": "4b3e5d58", "metadata": {}, "source": [ "_Note: In case you need even more flexibility in how you select parts of a Module, Jaxley provides a `select` method, to give full control over the exact parts of the `nodes` and `edges` that are part of a `View`. On examples of how this can be used, see the [tutorial on advanced indexing](https://jaxley.readthedocs.io/en/latest/tutorials/09_advanced_indexing.html)._" @@ -1598,7 +1545,7 @@ }, { "cell_type": "markdown", - "id": "cf68baf6", + "id": "c0c28026", "metadata": {}, "source": [ "You can also iterate over networks, cells, and branches:" @@ -1606,8 +1553,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "id": "a78d2a6c", + "execution_count": 17, + "id": "0dbad2b5", "metadata": {}, "outputs": [ { @@ -1638,27 +1585,27 @@ " \n", " \n", " 0\n", - " 0.763057\n", + " 0.537066\n", " 100.0\n", " \n", " \n", " 1\n", - " 0.334882\n", + " 0.050138\n", " 10.0\n", " \n", " \n", " 2\n", - " 0.805696\n", + " 0.913129\n", " 100.0\n", " \n", " \n", " 3\n", - " 0.717921\n", + " 0.874596\n", " 100.0\n", " \n", " \n", " 4\n", - " 0.079569\n", + " 0.048903\n", " 10.0\n", " \n", " \n", @@ -1667,14 +1614,14 @@ ], "text/plain": [ " radius length\n", - "0 0.763057 100.0\n", - "1 0.334882 10.0\n", - "2 0.805696 100.0\n", - "3 0.717921 100.0\n", - "4 0.079569 10.0" + "0 0.537066 100.0\n", + "1 0.050138 10.0\n", + "2 0.913129 100.0\n", + "3 0.874596 100.0\n", + "4 0.048903 10.0" ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1697,7 +1644,7 @@ }, { "cell_type": "markdown", - "id": "96cb79f6", + "id": "9b4e6e85", "metadata": {}, "source": [ "Finally, you can also use `View`s in a context manager:" @@ -1705,8 +1652,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "id": "859e1f6a", + "execution_count": 18, + "id": "f963236f", "metadata": {}, "outputs": [ { @@ -1757,7 +1704,7 @@ " \n", " \n", " 4\n", - " 0.079569\n", + " 0.048903\n", " 10.0\n", " \n", " \n", @@ -1770,10 +1717,10 @@ "1 2.000000 2.5\n", "2 2.000000 2.5\n", "3 2.000000 2.5\n", - "4 0.079569 10.0" + "4 0.048903 10.0" ] }, - "execution_count": 17, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1789,7 +1736,7 @@ }, { "cell_type": "markdown", - "id": "90151ce8", + "id": "bf1d92c1", "metadata": {}, "source": [ "## Channels" @@ -1797,7 +1744,7 @@ }, { "cell_type": "markdown", - "id": "44a31d9f", + "id": "0283f3be", "metadata": {}, "source": [ "The `Module`s that we have created above will not do anything interesting, since by default Jaxley initializes them without any mechanisms in the membrane. To change this, we have to insert channels into the membrane. For this purpose `Jaxley` implements `Channel`s that can be inserted into any compartment using the `insert` method of a `Module` or a `View`:" @@ -1805,8 +1752,8 @@ }, { "cell_type": "code", - "execution_count": 18, - "id": "0d26c451", + "execution_count": 19, + "id": "e1541bfe", "metadata": {}, "outputs": [ { @@ -1842,9 +1789,6 @@ " global_branch_index\n", " global_comp_index\n", " controlled_by_param\n", - " x\n", - " y\n", - " z\n", " Leak\n", " Leak_gLeak\n", " Leak_eLeak\n", @@ -1865,9 +1809,6 @@ " 0\n", " 0\n", " 0\n", - " 5.000000\n", - " 30.000000\n", - " 0.0\n", " True\n", " 0.0001\n", " -70.0\n", @@ -1886,9 +1827,6 @@ " 0\n", " 1\n", " 0\n", - " 15.000000\n", - " 30.000000\n", - " 0.0\n", " True\n", " 0.0001\n", " -70.0\n", @@ -1907,9 +1845,6 @@ " 0\n", " 2\n", " 0\n", - " 25.000000\n", - " 30.000000\n", - " 0.0\n", " True\n", " 0.0001\n", " -70.0\n", @@ -1928,9 +1863,6 @@ " 0\n", " 3\n", " 0\n", - " 35.000000\n", - " 30.000000\n", - " 0.0\n", " True\n", " 0.0001\n", " -70.0\n", @@ -1941,7 +1873,7 @@ " 1\n", " 0\n", " 10.0\n", - " 0.079569\n", + " 0.048903\n", " 5000.0\n", " 1.0\n", " -70.0\n", @@ -1949,9 +1881,6 @@ " 1\n", " 4\n", " 0\n", - " 44.850713\n", - " 28.787322\n", - " 0.0\n", " True\n", " 0.0001\n", " -70.0\n", @@ -1966,7 +1895,7 @@ "1 0 0 1 2.5 2.000000 \n", "2 0 0 2 2.5 2.000000 \n", "3 0 0 3 2.5 2.000000 \n", - "4 0 1 0 10.0 0.079569 \n", + "4 0 1 0 10.0 0.048903 \n", "\n", " axial_resistivity capacitance v global_cell_index \\\n", "0 5000.0 1.0 -70.0 0 \n", @@ -1975,22 +1904,22 @@ "3 5000.0 1.0 -70.0 0 \n", "4 5000.0 1.0 -70.0 0 \n", "\n", - " global_branch_index global_comp_index controlled_by_param x \\\n", - "0 0 0 0 5.000000 \n", - "1 0 1 0 15.000000 \n", - "2 0 2 0 25.000000 \n", - "3 0 3 0 35.000000 \n", - "4 1 4 0 44.850713 \n", + " global_branch_index global_comp_index controlled_by_param Leak \\\n", + "0 0 0 0 True \n", + "1 0 1 0 True \n", + "2 0 2 0 True \n", + "3 0 3 0 True \n", + "4 1 4 0 True \n", "\n", - " y z Leak Leak_gLeak Leak_eLeak \n", - "0 30.000000 0.0 True 0.0001 -70.0 \n", - "1 30.000000 0.0 True 0.0001 -70.0 \n", - "2 30.000000 0.0 True 0.0001 -70.0 \n", - "3 30.000000 0.0 True 0.0001 -70.0 \n", - "4 28.787322 0.0 True 0.0001 -70.0 " + " Leak_gLeak Leak_eLeak \n", + "0 0.0001 -70.0 \n", + "1 0.0001 -70.0 \n", + "2 0.0001 -70.0 \n", + "3 0.0001 -70.0 \n", + "4 0.0001 -70.0 " ] }, - "execution_count": 18, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -2003,7 +1932,7 @@ }, { "cell_type": "markdown", - "id": "ab5acd51", + "id": "21cfe367", "metadata": {}, "source": [ "This is also were `View`s come in handy, as it allows to easily target the insertion of channels to specific compartments." @@ -2011,8 +1940,8 @@ }, { "cell_type": "code", - "execution_count": 19, - "id": "e2a1b17f", + "execution_count": 20, + "id": "cf98c7fb", "metadata": {}, "outputs": [ { @@ -2067,7 +1996,7 @@ "12 1 False False True" ] }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -2088,7 +2017,7 @@ }, { "cell_type": "markdown", - "id": "24ec120a", + "id": "905edb2a", "metadata": {}, "source": [ "## Synapses" @@ -2096,7 +2025,7 @@ }, { "cell_type": "markdown", - "id": "d947ba43", + "id": "521128e0", "metadata": {}, "source": [ "To connect different cells together, Jaxley implements a `connect` method, that can be used to couple 2 compartments together using a `Synapse`. Synapses in Jaxley work only on the compartment level, that means to be able to connect two cells, you need to specify the exact compartments on a given cell to make the connections between. Below is an example of this:" @@ -2104,8 +2033,8 @@ }, { "cell_type": "code", - "execution_count": 20, - "id": "a1eed847", + "execution_count": 21, + "id": "c0932747", "metadata": {}, "outputs": [ { @@ -2130,8 +2059,8 @@ " \n", " \n", " global_edge_index\n", - " global_pre_comp_index\n", - " global_post_comp_index\n", + " pre_global_comp_index\n", + " post_global_comp_index\n", " type\n", " type_ind\n", " pre_locs\n", @@ -2164,7 +2093,7 @@ "" ], "text/plain": [ - " global_edge_index global_pre_comp_index global_post_comp_index \\\n", + " global_edge_index pre_global_comp_index post_global_comp_index \\\n", "0 0 4 12 \n", "\n", " type type_ind pre_locs post_locs IonotropicSynapse_gS \\\n", @@ -2177,7 +2106,7 @@ "0 0 " ] }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -2194,7 +2123,7 @@ }, { "cell_type": "markdown", - "id": "1c603a54", + "id": "9de4a21b", "metadata": {}, "source": [ "As you can see above, now the `edges` dataframe is also updated with the information of the newly added synapse. " @@ -2202,7 +2131,7 @@ }, { "cell_type": "markdown", - "id": "749de44c", + "id": "efcafb2b", "metadata": {}, "source": [ "Congrats! You should now have an intuitive understand of how to use Jaxley's API to construct, navigate and manipulate neuron models." diff --git a/docs/tutorials/01_morph_neurons.ipynb b/docs/tutorials/01_morph_neurons.ipynb index e029e767..dde51ccd 100644 --- a/docs/tutorials/01_morph_neurons.ipynb +++ b/docs/tutorials/01_morph_neurons.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "9f7be2a4", + "id": "8aa155e1", "metadata": {}, "source": [ "# Basics of Jaxley" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "2db89a9f", + "id": "f5f3e2d5", "metadata": {}, "source": [ "In this tutorial, you will learn how to:\n", @@ -61,7 +61,7 @@ }, { "cell_type": "markdown", - "id": "6c8a0eb9", + "id": "c71f01bf", "metadata": {}, "source": [ "First, we import the relevant libraries:" @@ -70,7 +70,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "f8cb454b", + "id": "c5a155e9", "metadata": {}, "outputs": [], "source": [ @@ -91,7 +91,7 @@ }, { "cell_type": "markdown", - "id": "d717ef05", + "id": "27a9fc7f", "metadata": {}, "source": [ "We will now build our first cell in `Jaxley`. You have two options to do this: you can either build a cell bottom-up by defining the morphology yourselve, or you can [load cells from SWC files](https://jaxley.readthedocs.io/en/latest/tutorials/08_importing_morphologies.html).\n" @@ -99,7 +99,7 @@ }, { "cell_type": "markdown", - "id": "3883d5aa", + "id": "7aa13bb2", "metadata": {}, "source": [ "### Define the cell from scratch\n", @@ -109,8 +109,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "1eba83a8", + "execution_count": 2, + "id": "b4328138", "metadata": {}, "outputs": [], "source": [ @@ -120,7 +120,7 @@ }, { "cell_type": "markdown", - "id": "acfbf1ab", + "id": "47228021", "metadata": {}, "source": [ "Next, we can assemble branches into a cell. To do so, we have to define for each branch what its parent branch is. A `-1` entry means that this branch does not have a parent." @@ -128,8 +128,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "4c26d47d", + "execution_count": 3, + "id": "a4593ab3", "metadata": {}, "outputs": [], "source": [ @@ -139,7 +139,7 @@ }, { "cell_type": "markdown", - "id": "efc170cc", + "id": "2169aff5", "metadata": {}, "source": [ "To learn more about `Compartment`s, `Branch`es, and `Cell`s, see [this tutorial](https://jaxley.readthedocs.io/en/latest/tutorials/00_jaxley_api.html)." @@ -147,7 +147,7 @@ }, { "cell_type": "markdown", - "id": "60d62a97", + "id": "f8677174", "metadata": {}, "source": [ "### Read the cell from an SWC file\n", @@ -161,7 +161,7 @@ }, { "cell_type": "markdown", - "id": "c8afc7cf", + "id": "4a11eeb8", "metadata": {}, "source": [ "### Visualize the cells" @@ -169,7 +169,7 @@ }, { "cell_type": "markdown", - "id": "a3fbe809", + "id": "eee45187", "metadata": {}, "source": [ "Cells can be visualized as follows:" @@ -177,8 +177,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "447c99bd", + "execution_count": 5, + "id": "fb397646", "metadata": {}, "outputs": [ { @@ -196,12 +196,12 @@ "cell.compute_xyz() # Only needed for visualization.\n", "\n", "fig, ax = plt.subplots(1, 1, figsize=(4, 2))\n", - "_ = cell.vis(ax=ax, col=\"k\")" + "_ = cell.vis(ax=ax, color=\"k\")" ] }, { "cell_type": "markdown", - "id": "fe86583b", + "id": "b52fc6f7", "metadata": {}, "source": [ "### Insert mechanisms\n", @@ -211,8 +211,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "bdddba0e", + "execution_count": 6, + "id": "28ad0fe7", "metadata": {}, "outputs": [], "source": [ @@ -223,7 +223,7 @@ }, { "cell_type": "markdown", - "id": "dbc08017", + "id": "0e2e96d8", "metadata": {}, "source": [ "Once the cell is created, we can inspect its `.nodes` attribute which lists all properties of the cell:" @@ -231,8 +231,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "eae355bd", + "execution_count": 7, + "id": "631ffca5", "metadata": {}, "outputs": [ { @@ -577,7 +577,7 @@ "[10 rows x 25 columns]" ] }, - "execution_count": 11, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -588,7 +588,7 @@ }, { "cell_type": "markdown", - "id": "a9506866", + "id": "ae2f653e", "metadata": {}, "source": [ "_Note that `Jaxley` uses the same units as the `NEURON` simulator, which are listed [here](https://www.neuron.yale.edu/neuron/static/docs/units/unitchart.html)._\n", @@ -598,8 +598,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "6312e227", + "execution_count": 8, + "id": "a9c14939", "metadata": {}, "outputs": [ { @@ -720,7 +720,7 @@ "[2 rows x 25 columns]" ] }, - "execution_count": 12, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -731,7 +731,7 @@ }, { "cell_type": "markdown", - "id": "e9425ae3", + "id": "1543cda4", "metadata": {}, "source": [ "The easiest way to know which branch is the 1st branch (or, e.g., the zero-eth compartment of the 1st branch) is to plot it in a different color:" @@ -739,13 +739,13 @@ }, { "cell_type": "code", - "execution_count": 14, - "id": "9eefce4d", + "execution_count": 9, + "id": "14c48c56", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -756,14 +756,14 @@ ], "source": [ "fig, ax = plt.subplots(1, 1, figsize=(4, 2))\n", - "_ = cell.vis(ax=ax, col=\"k\")\n", - "_ = cell.branch(1).vis(ax=ax, col=\"r\")\n", - "_ = cell.branch(1).comp(1).vis(ax=ax, col=\"b\")" + "_ = cell.vis(ax=ax, color=\"k\")\n", + "_ = cell.branch(1).vis(ax=ax, color=\"r\")\n", + "_ = cell.branch(1).comp(1).vis(ax=ax, color=\"b\")" ] }, { "cell_type": "markdown", - "id": "8b0459c4", + "id": "8f48a9b6", "metadata": {}, "source": [ "More background and features on indexing as `cell.branch(0)` is in [this tutorial](https://jaxley.readthedocs.io/en/latest/tutorials/00_jaxley_api.html)." @@ -771,7 +771,7 @@ }, { "cell_type": "markdown", - "id": "611aa6fb", + "id": "44e07e81", "metadata": {}, "source": [ "### Change parameters of the cell\n", @@ -781,8 +781,8 @@ }, { "cell_type": "code", - "execution_count": 15, - "id": "d8b8e544", + "execution_count": 10, + "id": "b278b102", "metadata": {}, "outputs": [], "source": [ @@ -791,7 +791,7 @@ }, { "cell_type": "markdown", - "id": "08892ab8", + "id": "ff765231", "metadata": {}, "source": [ "And we can again inspect the `.nodes` to make sure that the axial resistivity indeed changed:" @@ -799,8 +799,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "id": "6d3f14aa", + "execution_count": 11, + "id": "a133e5f6", "metadata": {}, "outputs": [ { @@ -921,7 +921,7 @@ "[2 rows x 25 columns]" ] }, - "execution_count": 16, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -932,7 +932,7 @@ }, { "cell_type": "markdown", - "id": "005f1e20", + "id": "e03c70a3", "metadata": {}, "source": [ "In a similar way, you can modify channel properties or initial states (units are again [here](https://www.neuron.yale.edu/neuron/static/docs/units/unitchart.html)):" @@ -940,8 +940,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "id": "a098f360", + "execution_count": 12, + "id": "529721d6", "metadata": {}, "outputs": [], "source": [ @@ -951,7 +951,7 @@ }, { "cell_type": "markdown", - "id": "a08da8da", + "id": "a9d1874b", "metadata": {}, "source": [ "### Stimulate the cell\n", @@ -961,8 +961,8 @@ }, { "cell_type": "code", - "execution_count": 18, - "id": "90d876b4", + "execution_count": 13, + "id": "68eff096", "metadata": {}, "outputs": [ { @@ -988,7 +988,7 @@ }, { "cell_type": "markdown", - "id": "76534f64", + "id": "0ebcb85e", "metadata": {}, "source": [ "We then stimulate one of the compartments of the cell with this step current:" @@ -996,8 +996,8 @@ }, { "cell_type": "code", - "execution_count": 19, - "id": "472309b3", + "execution_count": 14, + "id": "f12a9752", "metadata": {}, "outputs": [ { @@ -1015,7 +1015,7 @@ }, { "cell_type": "markdown", - "id": "bdbd193f", + "id": "8f33af17", "metadata": {}, "source": [ "### Define recordings" @@ -1023,7 +1023,7 @@ }, { "cell_type": "markdown", - "id": "16881662", + "id": "ef2c3bc8", "metadata": {}, "source": [ "Next, you have to define where to record the voltage. In this case, we will record the voltage at two locations:" @@ -1031,8 +1031,8 @@ }, { "cell_type": "code", - "execution_count": 20, - "id": "46107eb1", + "execution_count": 15, + "id": "601581a6", "metadata": {}, "outputs": [ { @@ -1052,7 +1052,7 @@ }, { "cell_type": "markdown", - "id": "1cd6625b", + "id": "10d7a74c", "metadata": {}, "source": [ "We can again visualize these locations to understand where we inserted recordings:" @@ -1060,13 +1060,13 @@ }, { "cell_type": "code", - "execution_count": 21, - "id": "74cb63b9", + "execution_count": 16, + "id": "cfdfca80", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1078,13 +1078,13 @@ "source": [ "fig, ax = plt.subplots(1, 1, figsize=(4, 2))\n", "_ = cell.vis(ax=ax)\n", - "_ = cell.branch(0).loc(0.0).vis(ax=ax, col=\"b\")\n", - "_ = cell.branch(3).loc(1.0).vis(ax=ax, col=\"g\")" + "_ = cell.branch(0).loc(0.0).vis(ax=ax, color=\"b\")\n", + "_ = cell.branch(3).loc(1.0).vis(ax=ax, color=\"g\")" ] }, { "cell_type": "markdown", - "id": "38f1cf41", + "id": "ca6dfaf3", "metadata": {}, "source": [ "### Simulate the cell response\n", @@ -1094,8 +1094,8 @@ }, { "cell_type": "code", - "execution_count": 22, - "id": "19e7805b", + "execution_count": 17, + "id": "a03b4ef6", "metadata": {}, "outputs": [ { @@ -1113,7 +1113,7 @@ }, { "cell_type": "markdown", - "id": "bb99315b", + "id": "534355d0", "metadata": {}, "source": [ "The `jx.integrate` function returns an array of shape `(num_recordings, num_timepoints)`. In our case, we inserted `2` recordings and we simulated for 10ms at a 0.025 time step, which leads to 402 time steps.\n", @@ -1123,8 +1123,8 @@ }, { "cell_type": "code", - "execution_count": 23, - "id": "721ad2ef", + "execution_count": 18, + "id": "cd27e835", "metadata": {}, "outputs": [ { @@ -1146,7 +1146,7 @@ }, { "cell_type": "markdown", - "id": "e8997a9b", + "id": "447cfa48", "metadata": {}, "source": [ "At the location of the first recording (in blue) the cell spiked, whereas at the second recording, it did not. This makes sense because we only inserted sodium and potassium channels into the first branch, but not in the entire cell." @@ -1154,7 +1154,7 @@ }, { "cell_type": "markdown", - "id": "dfed7c10", + "id": "66e32496", "metadata": {}, "source": [ "Congrats! You have just run your first morphologically detailed neuron simulation in `Jaxley`. We suggest to continue by learning how to [build networks](https://jaxley.readthedocs.io/en/latest/tutorials/02_small_network.html). If you are only interested in single cell simulations, you can directly jump to learning how to [speed up simulations](https://jaxley.readthedocs.io/en/latest/tutorials/04_jit_and_vmap.html). If you want to simulate detailed morphologies from SWC files, checkout our tutorial on [working with detailed morphologies](https://jaxley.readthedocs.io/en/latest/tutorials/08_importing_morphologies.html)." diff --git a/docs/tutorials/02_small_network.ipynb b/docs/tutorials/02_small_network.ipynb index 84b3807e..9b2b6630 100644 --- a/docs/tutorials/02_small_network.ipynb +++ b/docs/tutorials/02_small_network.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "10cb8b05", + "id": "2ec9dafe", "metadata": {}, "source": [ "# Network simulations in Jaxley" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "3149c330", + "id": "55ee1cd1", "metadata": {}, "source": [ "In this tutorial, you will learn how to:\n", @@ -48,7 +48,7 @@ }, { "cell_type": "markdown", - "id": "7dd2ee98", + "id": "8523c3de", "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:" @@ -57,7 +57,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "c08d10cb", + "id": "de136490", "metadata": {}, "outputs": [], "source": [ @@ -78,7 +78,7 @@ }, { "cell_type": "markdown", - "id": "9c39dfef", + "id": "cf137e23", "metadata": {}, "source": [ "### Define the network\n", @@ -89,7 +89,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "3858f198", + "id": "35258917", "metadata": {}, "outputs": [], "source": [ @@ -100,7 +100,7 @@ }, { "cell_type": "markdown", - "id": "9d3e84bc", + "id": "fe0920be", "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:" @@ -109,7 +109,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "a214b164", + "id": "6a3c676e", "metadata": {}, "outputs": [], "source": [ @@ -119,7 +119,7 @@ }, { "cell_type": "markdown", - "id": "d8e091d5", + "id": "90445c64", "metadata": {}, "source": [ "At this point, we can already visualize this network:" @@ -127,8 +127,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "d184c739", + "execution_count": 5, + "id": "86ea234a", "metadata": {}, "outputs": [ { @@ -145,13 +145,15 @@ "source": [ "net.compute_xyz()\n", "net.rotate(180)\n", + "net.arrange_in_layers(layers=[10, 1], within_layer_offset=150, between_layer_offset=200)\n", + "\n", "fig, ax = plt.subplots(1, 1, figsize=(3, 6))\n", - "_ = net.vis(ax=ax, detail=\"full\", layers=[10, 1], layer_kwargs={\"within_layer_offset\": 150, \"between_layer_offset\": 200})" + "_ = net.vis(ax=ax, detail=\"full\")" ] }, { "cell_type": "markdown", - "id": "c7b39541", + "id": "d7d9fc7d", "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)`._" @@ -159,7 +161,7 @@ }, { "cell_type": "markdown", - "id": "1e1e5d74", + "id": "7201bb0d", "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", @@ -169,8 +171,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "e4b94afc", + "execution_count": 6, + "id": "429054e3", "metadata": {}, "outputs": [], "source": [ @@ -181,7 +183,7 @@ }, { "cell_type": "markdown", - "id": "1d629fbe", + "id": "99ea65dd", "metadata": {}, "source": [ "Let's visualize this again:" @@ -189,13 +191,13 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "39d172dc", + "execution_count": 9, + "id": "28f133ed", "metadata": {}, "outputs": [ { "data": { - "image/png": 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MmYJNmzZBJpOJ2JIEwF0ArwOYD2CR8peMjAzBVjqnTGHDxtdfZ5mBzMwEqZZjRPDJ+UrOrVu38M4772D9+vUiixYAEJhgAcAUAE4llC0/8+YBrq5sG9CWLaI0wamE8B6XEfDs2TPMnTsX33zzDeRyOSwtLREQEICJEyfCVsRQC3I50KGDJe7cMcHUqXlYsCAfgHBDRQXbtgGffw44OjL3CFdXwarmGAHlej8rHHfVCDDW0M25ubkUHBxMDg4OBNYFogEDBlBkZKTObNi/XxU7PjFRnDYKCoiaNWPtfPGFOG1wDBeDy2TNKT9HjhzBG2+8gSlTpiA1NRUtWrTAmTNnsGfPHtTSYWiFvn2BVq2AzEyWM1EMTEyYUyrAtgPdvi1OO5zKAxcuAyMsLAy9evVC7969cf/+fbi5ueGbb77B1atX8ZYediVLJMCil/PymzYBGpKHV5jOnVnYG54Fm6MNfI6rEAMHDsTVq1dLDN3s4+NTprA12pKcnIyFCxdi06ZNKCgogJmZGfz9/TFnzhw46HlPDBHQoQNw6RLw5ZfAunXitBMZyVYXc3NZFuwPPhCnHY5hwd0hNKDtg2ndurVa4llNODk5lRq62d3dXastRAUFBfj2228xZ84cvHjxAgDw3nvvYfXq1ahXr572Nyky//wDvPMOYG4OPHwIVK8uTjtz5gBLlgB16gChoUAlDw7LARcujWj7YKKjoxEVFaUxsmlMTAyysrK0atPU1FQtNv2rIuft7Y2QkBAsWrQIoaGhAIBGjRphzZo16NGjhyD3LSREbDh39ixbAdy6VZx2MjJY9Ii4OGDZMpYVm1O54cKlAaHcIYgIqamppYZuLuteQicnJyxatAhjx46Fqalpue0Tm7NngbffBkxN2f7C2rXFaeenn4CPP2ZJNR48ALy8xGmHYxhwB1QdIJPJNH4KCgrK7Bhas2ZNhIeHY8KECQYtWgCLWPrOO2yLjphRHYYNY8ljMzKA2bPFa4djvPAeVyGePn2Kp0+fauxJxcbGar2x2czMrMj8V+Hvzs7OSE5ORqtWrYwqUODly0D79syF4d49QKxpuEuXAF9fdnz1KtC6tTjtcPQPHypqQOjJeRcXl1JDN7u4uBhEfC8x6NMHOHyY9Yx27hSvnY8/ZsPGDh2A8+eZawan8sGFSwPaPphBgwbh2rVrJYZu9vb2hqWlpQ6tNzxu3GBOqRIJcOcOy5koBjExbKI+Kwv4+WdgyBBx2uHoFy5cGjD2vYqGSP/+zNdqwABgzx7x2lmyhLlIVKsGhIUBRjSq5mgJn5zn6IyFC1mPa+9eFo5GLKZMAWrWBKKjgZUrxWuHY1xw4eKUi6ZNgYED2fG8eeK1Y2UFrFrFjpcvB54+Fa8tjvHAhYtTbhYsAKRS4NAhtvInFh9+yPzHcnK4QyqHwYWLU24aNmQri4C4vS6JhEWPkEiA3buBc+fEa4tjHHDh4lSI+fOZT9fRo8CFC+K107y5KnnspEksigSn6sKFi1MhXnsN+OQTdjx3rrhtLV4M2Nszd4wdO8Rti2PYcOHiVJg5c1iSi5MngdOnxWvH3Z318AAgMBBISxOvLY5hw4WLU2Fq1lQN4+bOFTcI4IQJzCk1MZH5eHGqJly4OIIwaxaLnXX+PHD8uHjtmJsDwcHseM0allyDU/XgwsURBB8fYNw4dix2r6tXL5YJOz8fmDpVvHY4hgsXLo5gzJwJWFsDV64Af/4pXjsSCet1mZgwHzIxe3gcw4QLF0cwPDzYHBTA/LrEdFl4/XVVW5MnsxhhnKoDFy6OoEybxiKXhoSwTdhiMn8+4OIC3L0LfP21uG1xDAsuXBxBcXVlPSCACUsZA8KWCScnVSTWuXOBl7lGOFUALlwcwZkyBXB0ZD0hMUPeAMwNo2lTIDmZ7Z3kVA24cBWiCoQm0wmOjkBAADtesEDc+SdTU1UW7C1bmFhyKj88kGAh+vfvjytXrpSaEJYHIyyd9HSWBejFC7Y9Z8QIcdtTBDZ85x3g2DEe5tmY4BFQNaDtg2nVqhVu3LhRan22tralxpz39PQ0+Kw9YrNiBQtDU6cOi15qZiZeWxERbKUxL4+5SLz3nnhtcYTFoIVr2bJlCAwMhL+/P9a+7Nvn5OQgICAAu3fvRm5uLvz8/LB582Z4eHgor3v69CnGjRuHU6dOwdbWFiNGjEBQUFCZREHbBxMfH19qQtg0LTfISSQSeHh4aOy1OTo6IiEhAW+99RYcHBy0vhdjIjOTiVZiIrBtm2pbkFgEBrIksnXrslj4PAu2cWCwwnX16lUMHDgQ9vb26NKli1K4xo0bh8OHD2PHjh1wcHDAhAkTIJVK8e+//wJgOQybN28OT09PrFy5EnFxcfj4448xevRoLF26VOv2hYw5n5GRUWpC2NjYWK3zK1arVg3Xrl1TE+vKxNq1bJWxenW2PUdMMUlPZ/sY4+NZb2/aNPHa4giHQcacz8jIwLBhw/DNN9/AyclJeT41NRXfffcdgoOD0bVrV7Rq1Qrbt2/HhQsXcOnSJQDA33//jdDQUOzcuRPNmzdHz549sXjxYmzatAl5eXlim14EuVyOzMxMjZ+MjAxkZmaWKSlsdHQ06tWrh1WrVunlnsRm7FjA2xuIigK++UbctuzsgKAgdrx4MZCQIG57HP0heo9rxIgRcHZ2xpo1a9C5c2c0b94ca9euxcmTJ9GtWzckJyfD0dFRWb5mzZqYNGkSJk+ejHnz5uHQoUMICQlR/h4ZGYk6dergxo0baNGiRbFt5ubmIjc3V/k9LS0N1atXL1XRIyMj8eTJE409qbi4OOTn52t131ZWViVO8Pv4+CA0NBSzZs1S3l+9evWwevVq9OnTB5JKNLu8eTMwfjzg5QU8esTiyIuFXA60awdcuwZ89hnw7bfitcURhvL0uESdPd69ezdu3LiBq8UEJI+Pj4e5ubmaaAGAh4cH4uPjlWVeHUIpvivKFEdQUBAWLlxYZnsHDBhQakLYV+euNE3OOzg4lCo+NWvWhJ+fH3bs2IFZs2YhPDwc77//Pt555x2sWbMGjRs3LvM9GCKffcaGbk+eMJeFKVPEa0sqBdavZ0lkv/8e+OILoGVL8drj6AfRhCsqKgr+/v44fvy4zhOoBgYGYkqht0PR4yqNBg0aICMjo0R3CE9PT5gJuDwmlUrx6aef4n//+x+WLl2KNWvW4Pjx42jWrBnGjRuHhQsXwtnZWbD29IGFBfNsHzWKTZ6PGcO2BYmFry8wdChLIuvvD5w9y90jKh0kEvv37ycAZGJiovwAIIlEQiYmJvTPP/8QAEpOTla7rkaNGhQcHExERHPnzqVmzZqp/R4REUEA6MaNG1rbkpqaSgAoNTW1orclOg8fPqQPPviAABAAcnZ2po0bN1J+fr6+TasQeXlEr71GBBAFBYnfXlQUkbU1a2/3bvHb45Sf8ryfoglXWloa3b59W+3TunVrGj58ON2+fZtSUlLIzMyMfvvtN+U1YWFhBIAuXrxIRERHjhwhqVRKCQkJyjJff/012dvbU05Ojta2GJNwKfjnn3+oSZMmSgFr3LgxHT9+XN9mVYgff2RC4uxMpIv/FAsXsvZq1CDKzBS/PU75MCjhKo63336b/P39ld/Hjh1LNWrUoJMnT9K1a9fI19eXfH19lb8XFBRQkyZNqEePHhQSEkJHjx4lNzc3CgwMLFO7xihcRET5+fm0adMmcnFxUQrY+++/T+Hh4fo2rVwUFBA1bMjEZOFC8dvLzGSipav2OOXD6IQrOzubvvjiC3JyciJra2vq168fxcXFqV3z+PFj6tmzJ1lZWZGrqysFBASUedhkrMKlICkpifz9/ZXDbTMzM5o6dSrFxsZSRkaGqJ/09KLn5HJ5ue9l924mJPb2RC9eCPiQNPDrr6w9Kyuip0/Fb49TdgxeuPSFsQuXgtDQUPLz81P2vsT/vEnAGQIc1M5nZGSU+x5kMqKmTZmYzJol4MPRgFxO9OabrL2hQ8Vvj1N2yvN+8ugQRsTrr7+OP//8ExMnToSJiYnIrZkA+A7AWwCE8xyVSgGFp8q6dcCzZ4JVXSyFs2D//LO4SWs5uoMLlxFx7tw5tG3bFhs2bIBMJoObmxs+++wzZGRkiPBJxenT1WBqSgAGYN26HOVv1tbWFbqPDz5gvlWZmcy/S2xatgQ+/ZQd+/vzLNiVAhF7gAaDsQ8VHz9+TAMHDlQO1RwcHCg4OJhyc3NFb3vlSjbMsrQkunVLuHoPH1bNPb0yrSkK8fFEdnasze3bxW+Poz18jksDxipcGRkZNHfuXLK0tCQAJJVK6fPPP6fExESd2SCTEfXsyV74hg2JKjC9pYZcTtS+Pav3yy+FqbM0FCLs6UmUlqabNjmlw4VLA8YmXDKZjH766Sfy8fFR9rI6d+5MISEherEnMZHIy4u99J98Ily9x4+zOs3NmcOo2OTmEtWty9qcOVP89jjawYVLA9o+mLy8PB1ZpJnLly9T+/btlYJVu3Zt2rdvX4VcEITg5EkiiYS99Dt3ClOnXE701luszrFjhamzNA4dUonlw4e6aZNTMuURLh4BtRDvvfcezp49W2roZg8PD8Gjm8bGxiIwMBA//vgjAMDGxgazZ8/G5MmTdb7XUxPz5wOLFrF9hjduAPXqVbzOs2eBt99m0VEfPABq1ap4nSVBBPj5sSSy/foBv/8ubnuc0jHYQIL6RtsH07JlS9y8ebPU+qRSKTw9PTVGh1Ccs7e3LzVCRHZ2NoKDgxEUFITMzEwALBTQ0qVL4e3tXbYbFZmCAqBrV+DcObZSd+GCMIEB33kH+OcftvL33XcVr6807t4FmjVjqdNOnGD3xNEfXLg0oO2DUUQ3LSl0c1xcnNaBAm1sbDT22lxcXHDu3Dls27YN0dHRAABfX1+sW7cObdq0EeS+xSA6GmjenCXB8PdXZdipCJcusYgOJibAvXvC9ORK48svgQ0bWGqzGzdYtiCOfuDCpQEhQzfLZDIkJiYWG7pZ8YmOjtY6Nj3AwjcvX74cQ4YMMYoAgn/+qUpGcfAg8P77Fa+zd2/gyBFg2DBg586K11caSUlMIJOSWKDDcePEb5NTPFy4NCCUcGVlZRXbCyt8LjY2VusoqQCLAXb9+nXY2NiU2y59MGUKsGYN4OwMhISwmPIV4fp1oHVr5uF+5w7QqJEgZpbIpk3AhAmAiwuLh18osjhHh3Dh0oC2D+b27duIjIzUOFRMSUnRuk13d/cSJ/jd3NyQnZ2NatWqCRqYUFfk5gIdOzLB6dQJOHWq4sMtRW7EAQPEz4ANsDm75s3ZnJdQw15O2eHCpQFtH0zbtm2LDTNdGGtr6xLjyCuipJqbmwt9GwbHo0dAixYsu86cOSxBRUW4fZtNmhOxXlyzZoKYWSL//MMWB0xMWPuvvy5+mxx1uHBpQNsHM3r0aISEhGiMI+/t7a1VLPmqxO7dwJAhbIh3/DjQrVvF6hs8GPj1V6BvX+DAAUFMLJUPPmBzdX5+wF9/8TDPuoYLlwaEnJznFGXUKObG4OnJekoVSREZFgY0bsw2Ql+9yua9xObhQzanlp/PFh569xa/TY4Kg8yryKn8rF/PXvz4eGDEiIpFX2jYkK0sAsC8ecLYVxp167KktQD7WwnTW1Y6uHBxKoy1NRveWVoCx44Bq1ZVrL5589ic019/6S5+1uzZrKcYHg5s3KibNjnlhwsXRxCaNGE9L4CJwMtk5OWibl1g5Eh2PHduhU3TCnt7YOlSdrxwIZCYqJt2OeWDCxdHMEaNAgYOZG4GgwcDZfAeKcLcuWz/4smTwOnTQllYMiNHAq1aAWlpuhNMTvngwsURDIkE2LYNqFOHZa0eNYq5NpSHmjXZ9QATEV0sIUmlKl+ub75hCw0cw4QLF0dQHByYi4SZGbBvH7B1a/nrmj2bbeI+f565WuiCTp1Yb5GIOaVW/jV344QLF0dw2rQBli1jx5MnA//9V756fHxUewh11esCgOXLASsrFnJn3z7dtMkpG1y4OKIwaRLQqxfbGjRoEEuMUR5mzmSrlleuAIcPC2qiRmrUAKZPZ8dTpwLZ2bppl6M9XLg4oiCVAj/8AHh7A/fvs83M5cHDQ3XtvHm663VNnw5Uq8bm6oKDddMmR3u4cHFEw9WV5TKUSoEdO8ofrmbaNBZ19eZNtglbF1hbq1KnLV0KxMTopl2OdnDh4ojK22+rXAvGjmXhmcuKqysbegKs16VlHMcKM3gwi4CRlcWGrBzDgQsXR3TmzmUClpnJxCA3t+x1TJnCVizv3tVNyBtAlQUbYL3FijjVcoSFCxdHdExMgF27WM/p5k029CsrTk5AQAA7XrCAObnqgtatgU8+Ycc8C7bhwIWLoxN8fNg8F8BivZcnZI2/P4u4+uABE0JdsXQpm2O7ckW37XI0w4WLozN692ZDPoBl9Hn6tGzX29sDM2aw40WLWBgaXeDpyQIlAqz9jAzdtMvRDBeuQuTk5KAKhCfTK0FBzEE1ORkYOrTsQ77x4wF3dyAiQtWD0wWTJgGvvQbExbF74OgXHkiwEH369MHJkydLjICq+FsVQjOLRUQEC/mclgbMmgUsWVK269euZR751auzMDRC5HbUhoMHWbRUCwuWRq12bd20W9nhEVA1oO2DadGiBUK03Fnr5uamMea84ryLiwsP86yBX39lK4wSCfD330D37tpfm5PDej+xsSx21vjx4tlZGCIWn/7ECeDDD4HfftNNu5UdLlwa0PbBZGdna0w7Vvg4T8sQmRYWFmrCVvjY3NwckZGRGDRoEKpVqybUrRoVY8awKAweHmw/Y1lCPm/ezATLy4sl7bCyEs/Owty5w5J4yOUss1HnzrpptzLDhUsDQsacJyI8f/5co8BFR0cjKioKycnJWtXn4eGBCxcuoE6dOhWyyxjJygLatmW+WT16sIinUi1nXXNzgfr12QR/cLAq9LIuGD+eCecbb7As2CYmumu7MsJjzotMWloawsLC8N9//xX7CQkJwZ07d7QWLQBISEhAo0aNMGvWLKSnp4toveGhCPlsZcWGi4otNtpgYaHyyA8K0u1K36JFzK/s1i3g22911y5HBe9xFSIkJAQREREah4oZWr4dJiYm8PT0LDEhrI+PDyIjI+Hv74/TL0N8enl5ISgoCB999BGk2nY9KgHffguMHs16LufOAb6+2l2Xn8+Sa0REMPHS5bacDRuAL79kTrXh4YCjo+7armzwoaIGhEwI6+DgUGpCWHd3d5hoOX4gIhw8eBABAQGIiIgAALRp0wbr1q2Dr7ZvsJFDxFwjdu9mIWVCQliPRht+/JFlFnJ2BiIjma+XLsjPZ3Nd9+6xYSqPIFF+uHBpQNsHM3bsWNy6datYMVKcs7GxEcXG3NxcrFu3DosXL1b27IYNG4Zly5ZVicn7tDSgZUs20d6vHwvgp82CrEzGEnWEhbEkF7pKaQawjEbvvguYmrJJ+wYNdNd2ZaJcc9BUBUhNTSUAlJqaqm9TSiUuLo4+/fRTkkgkBICsra1p0aJFlJWVpW/TROfqVSIzMyKAaNMm7a/bvZtd4+BAlJQkmnnF0qcPa7tXL922W5koz/vJhctAuXbtGnXs2JEAEACqUaMG7d69m+Ryub5NE5XgYCYEFhZEN29qd41MRtS0Kbtu9mxRzSvC/fsqsT1yRLdtVxa4cGnAGIWLiEgul9Pu3bupevXqSgHr1KkTXb9+Xd+miYZcrurF1K9PlJ6u3XW//86usbUlevZMXBtfZepU1naDBkR5ebptuzJQnveTz3EZAVlZWVi1ahWWLVuG7OxsSCQSfPLJJ5g9ezbc3d1F885X/Mt4tXpra2tRdwQ8fw40b86ijn78MQsBXRpELATNjRssbE5ZXCsqSmoqUK8e8OwZsGaNKughRzv4HJcGjLXH9SpPnz6loUOHKntf4n+GE3CMgMZq5zMyMkS/1zNniKRS1pP54QftrvnzT1beyoooLk5c+17lm29U82yJibpt29jhQ0UNVBbhIiLKycmhL774gqRSqciiJSXgIbG+TAEBmwhw0ZlwEREtXMjEwMaGKCys9PJyOVG7duyaL78U377CFBQQNW/O2h47VrdtGztcuDRQGYRLLpfTgQMH6LXXXlOKS61atWj69OmUkZEhyuf27Uzq2zf/pXgROTjIadmyHMrJ0c0CQUEBUZcurO1mzYiys0u/5u+/WXlzc6KoKNFNVOPMGda2VEr033+6bduY4cKlAWMXrtu3b1O3bt2UguXp6Uk7duwgmUymk/ZPnVL1JgCievWIDh1iPRyxiYkhcnVl7U6YUHp5uZzozTf11/MZOJC13aWLbp5PZYALlwaMVbieP3+uNiy0sLCgwMBASktL07ktBQVE335L5OGhErDu3Ylu3RK/7SNHVG3+/nvp5RU9HzMzoshI0c1T4/FjIktL1v6+fbpt21gxOOFaunQptW7dmmxtbcnNzY369u1LYa9MVmRnZ9MXX3xBzs7OZGNjQ/3796f4+Hi1Mk+ePKFevXqRlZUVubm50dSpUyk/P19rO4xNuPLy8mjdunXk5OSk7GX179+fHj16pG/TKDWVaOZMNhRTDIvGjhV/QlrhcuDoyMShNLp3Z+U//VRcu4pj7lzWdu3a2g1vqzoGJ1x+fn60fft2unPnDoWEhFCvXr2oRo0aapO7Y8eOperVq9OJEyfo2rVr1L59e+rQoYPy94KCAmrSpAl1796dbt68SUeOHCFXV1cKDAzU2g5tH0xycjLl6dkR5+jRo/T6668rBeuNN96gkydP6tWm4oiIIPrf/1Q9IXt7olWriHJzxWkvN5eobVvWlq9v6f5SFy+ysiYmROHh4tikiYwMIh8f1v7Spbpt2xgxOOF6lcTERAJAZ86cISKilJQUMjMzo7179yrL3Lt3jwDQxYsXiYjoyJEjJJVK1XphW7ZsIXt7e8rV8i3R9sH06tWLJBIJubu7U4sWLahPnz70+eef06JFi+i7776jv/76i27dukUvXrwQ3IP9/v371KdPH6Vgubi40NatW6mgoEDQdoTmzBmiFi1UAla3LtGBA+LM70REMIEEiLT5/1avXqzs8OHC21IaO3eqVkRjYnTfvjFh8MIVHh5OAOj27dtERHTixAkCQMnJyWrlatSoQcHBwURENHfuXGrWrJna7xEREQSAbty4UWw7OTk5lJqaqvxERUVp9WBatWqltbuApaUlvfbaa/Tmm2/S4MGDKSAggIKDg+nXX3+l8+fPU0REBOXk5JT6TJKTk2nKlClkZmZGAMjU1JQmTZpESbredFcBCgqIvv+eyNNTJWBdu4qzsrZnD6tfImEriCVx7ZqqbGio8LaUhFzOeoYA0YgRum3b2CiPcJmW5qAqFHK5HJMmTULHjh3RpEkTAEB8fDzMzc3h+EowIw8PD8THxyvLeLwS01fxXVHmVYKCgrBw4cIy23jlyhW8ePGixNDNMTExePHiBXJycvDo0SM8evSoxDpdXV2LRJjw8vKCpaUlzp49i/379ysDD/bq1QurV69Gw4YNy2y7PjExYUlT//c/YNkyYPVq4ORJlhBj1Chg8WKWmUcIBgwAPv8c+PprYPhwFvLZ07P4sq1aseQWBw6wJLK//iqMDdogkQDr1rEIrz/8AHzxBTvmCISIQqrG2LFjqWbNmhRVyLlm165dZG5uXqRsmzZtaPr06URENHr0aOrRo4fa75mZmQSAjmjY1VreHpe2ZGdnU0REBJ07d452795Nq1evpilTptCgQYPI19eXqlWrpuxBafNp0KABHT58WBDbDIHISJVbgGL+a8UKIi06oFqRlUXUpIlqZbMkr5D//lPZERIiTPtlYcQI1nb79tw9QhMGO1QcP348VatWjSIiItTOizVUfBUhVhXz8/MpKiqKLl26RPv27aMNGzbQzJkz6aOPPqKuXbtSgwYNyM7Orswe6s2bN9f7goBYnD1L1KqVSjhee41o/35hXuC7d9nWHm0mwBUi2rdvxdstKzExbJ4LYPNenKIYnHDJ5XIaP348eXt704MHD4r8rpic/+2335TnwsLCip2cT0hIUJb5+uuvyd7eXqs5JCLtH8y5c+do+/bt9NVXX9G4cePo/fffp1atWpGXl1eZttjY2dlRw4YNqVu3bvTxxx9TYGAgbdy4kX7//Xe6fPkyRUdHU3Z2Nj3TdRgDPSCTEW3frj7/1aWLML2f775TrRyeP6+5XGioat/j1asVb7esLF3K2vbxYSuOHHUMTrjGjRtHDg4OdPr0aYqLi1N+CgfFGzt2LNWoUYNOnjxJ165dI19fX/L19VX+rnCH6NGjB4WEhNDRo0fJzc1NFHeIdu3alShIJiYmVK1aNWrXrh3179+fJk6cSEFBQfTjjz/SiRMnKCwsTC/OocZAejqLlWVhoZowHz2a6BWXvTIhlxMNHcrqq16d6MULzWU/+oiV69mz/O2Vl+xs5tMFMB8vjjoGJ1yaBGD79u3KMgoHVCcnJ7K2tqZ+/fpR3Ctb+x8/fkw9e/YkKysrcnV1pYCAAFEcUCdPnkw9evSgkSNH0uzZs2nz5s108OBBunbtGsXGxhq8a4Ix8Pgx0aBBqt6XnR3R8uXln/9KS2MuGADRBx9oHoaGh7OeGUB04UL57S8v+/axti0tde/Nb+gYnHAZCsbmOV8VOH+eqHVrlYDVqcNe7vLMf127popCumGD5nKffcbKdOtWfrvLi1yu2jA+YIDu2zdkuHBpgAuXYSKTsVhbXl4qAXv7bSIt11zUWLtWFRVC0/WPH6sE7vTpCpleLv77TzXX9tIHm0Plez+rTvI+jsEhlbIIpw8esOSulpbAmTPM/2rUKECDm16xfPkl8P77QF4eMGgQUFxu3Zo1Wb0Aa0/XsX/feAMYM4Yd+/uzDEWcciKikBoMvMdlHDx5QjR4sKr3ZWtLFBSk/Ubl58+JqlVj1370UfFloqNVCwSled6LQWIi2ygOsKipHN7j4hg5NWoAv/wC/Psv0KYNkJEBBAYCr78O/PZb6T0kFxfg559ZT+6nn4qPVe/jA4wdy47nzNF9r8vNDZg/nx3PmsXi1XPKgYhCajDwHpfxIZMR/fSTKsoCQPTWW0TaJDhavJiVt7Ymunev6O9xcSrn1T/+EN720sjLI2rYkLUfEKD79g0N3uPiVBqkUrYX8f59lp3a0hI4e5Zl8vnsMyAuTvO1gYFA165AVhab78rJUf/d0xOYMIEdz5un+16XmRkQHMyO169nc3ycssGFi2PQ2NgACxcyARs6lInM998D9esDQUFFRQlgm7537mTDslu3gICAomWmTwdsbYGbN4H9+8W/j1fp2RPo1QvIzy/ePk7JcOHiGAU1agC7dgEXLgDt2rH5r1mzgIYNgb17i/aavLyAH39kx5s3A/v2qf/u6qrKfzh/PiCXi34LRQgOBkxNgT//BI4d0337xgwXLo5R4evLxGvnTjbR/uQJMHAg8NZbwPXr6mXffZf1rAA2vHz8WP33KVMABwfgzh1gzx6dmK9GgwbAxInsePJk1vviaAcXLo7RIZUCw4ax4eOCBYCVFXD+PFuJ/OQTIDZWVfarr1gPLTUVGDJEXRycnFTDtAULgIICXd4FY9481vu7dw/YskX37RstIi4WGAx8VbFyExXFwjMrVh9tbIi++orF7SJiewMdHNhvM2aoX5uaSuTsXLaM2UKzdasqEUgVCBhSBL6qyKmSVKvG/LYuXQLatwcyM5mPVsOGLOppzZrAd9+xssuXq88n2durhpMLF+pnuDZqFPOqT0lR+XhxSoYLF6fS0K4dm//atYuJ2dOnwODBwJtvssn9ceNYuY8+UnenmDCBhZaOiAB27NC93SYmLMwzAGzdCty+rXsbjA0uXJxKhUTC3Cbu32c9KGtr5onfti2Qlsa88J89Y+Kl2CtoYwPMnMmOFy8GcnN1b3fnzsCHH7LVzUmTdO9bZmxw4eJUSqyt2cT3/ftMpADWE4uMZA6gJ06wxB4Kxo4FvL2BqCjg22/1Y/PKlYCFBUs0cvCgfmwwFrhwFeL58+fIzs7WtxkcAalWjflzXb7MXClyclTzWPPmAefOsWMrK2D2bHa8ZAmgj38GtWurVjkDAvTT8zMWJESVv1OalpYGBwcHpKamwt7eXmO5Xr164a+//oKTk5MypVjhjyK9mI+PD9zc3CCVct03JojYZP20aUB0NDtnbs4cQN95hwlF/fpsbiw4mPlW6ZqMDGZDXBzrEc6YoXsbdI2272dhuHAVom3btrh69apWdZqZmcHLy0tNzIoTORsbG6FugyMQ2dnA0qWsZ6X41z98OBOKv/4CRo9WTdbr4z/fTz+xOGW2tkB4uOa8kZUFLlwa0PbBEBFSUlJKTQibmJgIbR+bg4NDETHz9PSETCbDw4cPMXbsWGWCXI5uOXaM7RdUbPextgamTmXCERmpvx6PXM6GtVeuMIfa77/XvQ26hAuXBsrzYEoiPz8f8fHxRQQuOjoaT58+RVRUFOLj45GrxSSFs7MzTp06hTfeeKPCdnHKzoYNLHqqRKLqfTk5AcnJgLMzEzAB/smUmUuXmHgBwNWrLCpGZYULlwaEEK68vDy1ntervTDFufJM7kulUowZMwaLFi2Cm5tbuezjlA8ioF8/torn6ck2PSvmvwA2bNy2TT+2ffQR25PZoQPb0iSR6McOseHCpQFtH8yZM2cQHh5erCA9e/ZM6/acnZ1Lndx3dXXF06dPMX36dOzduxcAG1YuWLAA48ePh5mZWYXvm6MdSUlA8+bMFWLwYKBxY7bHUdFh/t//2GR99eq6tSsmhk3UZ2WxyK5Dhui2fV3BhUsD2j6Y9u3b4/Llyxp/t7CwKDIZX9x3S0vLMtl39uxZ+Pv7IyQkBADQoEEDrFmzBj179ixTPZzy8++/wNtvM6fU7duB7t2Bpk3ZNhyAuUvMmMFWJK2tdWfXkiVs+1K1aswnTZdt6wouXBrQ9sFMmzYN9+7dK3al0NvbGy4uLpCI1F+XyWT4/vvvMXv2bGXvrmfPnggODkbDhg1FaZOjztKlzJfL2hq4do1FbPjwQ7YlR+FlX60am7QfMoRFqRCb7Gzm7f/kCdvHuGCB+G3qmnJN5Qi2xduAMaboECkpKTR16lQyMzMjAGRqakqTJk2ipKQkfZtW6ZHJiLp3Z5EamjYlyswkatGCfX//faKaNVURKNq1I7p4UTd27d2ryoL95Ilu2tQlPCGsBoxJuBQ8ePCA3nvvPQJAAMjFxYW2bNlCBQUF+jatUhMXR+TuzoRi3DiiP/9kx1ZWLDzO0qUsbZpCwIYOJXr6VFyb5HKWKARg6dsqG1y4NGCMwqXg2LFj1KhRI6WANW3alE6cOKFvsyo1x46phGnvXta7Aoj8/dnvsbFEn35KJJGoRG3ePKKMDPFsunlT1d65c+K1ow+4cGnAmIWLiCgvL4/Wr19PTk5OSgHr168f3b59m1JSUigjI0OnH7lcru9HIjozZzKRcHBgAQYBlkg2KkpV5vp1ojffVImctzfRjz+yIacYjBnD2mnZUrw29AEXLg0Yu3ApeP78OU2YMIFMTEyUAibuZwEBRwlYTEBfAnwIAGWI2bUwEPLyiHx9mVC0bUvUqZNq+FgYuZzot9+IatVSCVibNkT//iu8TQkJRPb2rI3vvhO+fn3BhUsDlUW4iIjS0tLos88+I4lEogPhOqd8GVWfWOrZM58WLCA6fJi9TJWVx49ZOGXF3BJAZGbG5rpeJTubKChIff5r8GDhJ9NXr2Z1u7uzsNOVgfK8n9wdwkiQy+X44YcfMGvWLMTHxwMA3njjDQwdOhQTFNlNBea//6S4elWKGzfY5949KWSyou4g1auzRBWtW7NPq1Zsu0xl4PffmUsEwJxUQ0KATz9VhYJ+lfh45nf1/fdMviwt2f7HGTPYpumKkpfH/MsePGAhp5cvr3id+oa7Q2jA2Htc58+fp1atWil7QnXr1qVDhw7pfK4pM5MNgdatI/roI5ZGXjFh/OrntdeIBg0iWrmS6PRporQ0nZoqKOPHq5JZAEQmJkTh4SVfc+OGaiUQIPLyItqxQ5i5KcVKp5lZ6XYYA3yoqAFjFa4nT57Q4MGDlYJlb29PK1eupJycHH2bpiQ1lQnTqlVMqF57rXghk0iY0H30ERO+f/9lQmgMZGcTNWvG7sPFhf0dPrz06+Ryon37iGrXVj2H1q2Jzp+vmD1yOdG777L6+vatWF2GABcuDRibcGVmZtL8+fPJysqKAJBEIqFRo0ZRfHy8vk3TihcviP7+m/k89e9PVL168WJmYkL0xhvMtWDLFqKrV4kMSJPVCAtjac8KC3FoqHbXZmcTLV9OZGenun7QIDaHVl5CQ9nzA9izNma4cGlA2wcTFRVFiYmJJNPTWrNcLqeff/6Zqlevruxlvfnmm3Tjxg292CMk8fFsMn/BAqI+fYg8PIoXMzMzolatiMaOJfr2W6KQEKL8fH1bz1C4RSg+AweqfisoIDp1iujnn9nf4vyE4+OJRo1SDa8tLIhmzyZKTy+fPf7+rJ7GjQ3nGZUHPjmvAW0n/3r27ImjR4/C3NwcXl5eJUZ38PHxgZWVlWA2Xrt2Df7+/rhw4QIAoEaNGli5ciUGDBgg2v5IfULEoh9cu8Y+V6+yv0lJRctaWgItWrCJf8UiQP36bA+hrhkxgsWwV/Dff8DDh4C/v3o4nGrVWMqx/v2L1hESwsJCnz7Nvnt6AkFBLOppWfY/JicD9eoBL14AGzcC48eX5470D99krQFtH0yXLl1wWvGvSQucnJxKDd3s7u5eYmz6uLg4zJo1CzteJvSztrZGYGAgAgICBBVGY4AIePxYXciuX2dpxV7F1patXipWMlu3Bl57TfyYVRkZrN0HD9j3tm2Zra++RQo7fvutePEiYjHApk4FHj1i51q1AtasYXkgtWXLFuCLL9gqbni4ca7mcuHSQFkeTF5eHuLi4jQGDFR8z8rK0qptU1NTZe/N29sb3t7ecHZ2hpmZGf7991+cPn0aOTk5AIDhw4cjKCgI1apVq/A9VxbkcvZCFu6Z3bzJYlS9iqOjSsQUPbPq1YUXs5AQJlilZb2WSFjPKzJSc+8wNxdYv57lc0xPZ+cGDGBuDrVrl25LQQHQsiVLIjtxIqvL2ODCpQGh/biICKmpqUXE7OnTp4iIiEB0dDTi4+ORogjmVApt2rTBunXr4KuI1cspkYICICxMvWcWEsJ8nF7FzU3dx6x1a8DLq+I2bNrEMmAzLgFYA2ADgK0AsgH4KD979vigXz8PmJqaaqwvIYGlS/v2WybWFhbAlClAYCBgZ8fKyGQsnVpcHLuHN99kgnjyJNCtGzv+7z8WCNGY4MKlgYoKFxEhKSmpxAQaMTExZYqSKpVKIZfL8eabb+L06dM81VkFycsD7txR75nducNE7lV8fNR7Zq1aAa6uZWtPJpPD3PwU5PJuL8+kAbAHcBDAB0XKSyRSODt7oHbtkudNIyPtMWWKBKdOses8PVmcMDs7Ni+maR6tf39g/36WZu3YMeMK88yFSwPaPpjDhw/j/v37xYZu1ibxBcDSlpU276WIkpqVlQVbIdypOcWSnQ3cuqXqlV27BoSGFp2PAoBatYp6/zs4lFz/H38A779PABQqkQjgJwCPAcQU+sQDkGlls42NDby8vGFpORCPH09ERobHy1+iAewF0B9ATQCqBB89e7Lh4sqVTMAPHQLee0+r5gwCLlwa0PbBdOjQARcvXtT4u4uLS6krjS4uLrz3ZMBkZLA5ssI9s/Dw4svWr6/eM2vRQj3PYmiotsMyGZioqcTsww9jYW39FJGRkYiOjkZiYmIx86bmACYAmAdAoaJHATQGUDQAvkLI6tZlvU0LC21s0z9cuDSg7YOZP38+Hjx4UGyPycvLq8yx5DnGQUoKcOOGes/s8eOi5aRSFka5TRvWQ9u0CSh+doAAJEElVKYATAAcABABIAZSaSzk8kQtLXQDsAjA6Jf15AI4DOAdAHbFXrFyJVuxNAa4cGmgMmyy5uiWZ8+YK0bhBYDY2OJKEoAEAB5gQ8Z4AE0BPH/5uxTAbQCNAMwCEKR2tbm5ucYcBz4+PrhxwweTJ3sDsAIQDiAdQEsAbcGGj0EAPnrZjgo7O9aT9PCAwVOe91PzMgeHU4VxcwPefZd9FMTGAn//zVb7kpMBNgQ0AeBZ6EpPAHEArgO4AzZh3whAJljvaBsUq41bt/pgzJiSE7Aosmwz6oEJ5WmwHl0cgEcA8gBEvfydkZ7OEn98+23Z790Y4D0uDkdLEhOBzp1Z9p9atQBv74lgGx1aA3gDQAOwnpF2S3onTgBdu5ZcRiZjbcXEvLqokAtgN4DhYOJ5A8BqAMsBMD9AiQS4fBnIzCzqQmFI8KGiBrhwcSpKUhLQpQtbpfTxYf5UNWpoEhXtcHAo6v1fq1ZRV4bff2dJaQFN7cjAenEJAKwBzEDz5rMQEmIKc3N1/7aStiLpi0odj2vjxo1Us2ZNsrCwoLZt29Lly5e1vtbYokNwDIuUFLbxG2Cbw+/fV/22bx/bNK0pLpmmj7l58eddXIj8/Njm6wMHiKKjVeFxqlUrqc7rBHRSbs53cmpLQH6x4YUkElafoVBpo0Ps3r2bzM3N6fvvv6e7d+/S6NGjydHRkRK0jBvMhYtTXtLTiTp0YC+9qyvRnTtFyxQnKpqErHp1Vj4vj0W++OYbos8/Z8JoZlb8NZ6eRO+9RzR/PvsUDo+j/pETsJvs7Gq8FLC5L8/nFGuHoWS6q7TRIdq1a4c2bdpg48aNAFgY4+rVq2PixImYOXNmqdfzoSKnPGRlAb17sygOjo7AqVMsfHNxFN6O4+oKDBumcpVYtowNK0ubY8rNZUPRwj5md+++OkGvGRMTtnDQrVs23n13FYB1YIsENQHsB9AEgDsUa3JHjgBvvaVd3RXB2tq6xAWISjlUzM3NJRMTE9q/f7/a+Y8//pjef//9Yq/Jycmh1NRU5ScqKor3uDhlIieHqEcP1juxsyMqw8wEff01u87KStVTi4kpnx2KcNnBweqZtIv71KnDEnQMHao4F03A+pfHp5TDSF1/SssKVZ4el8G7eD9//hwymQwerzikeHh4KJNGvEpQUBAcHByUn+rVi3oZcziayM8HBg5krg/W1qxn0ratdtfm5gJffcWOFy1iPbTnz4Hhw1mvrKxYWwO+viz0zZMngJkZ6/0BQL9+zM46ddj3iAhg927g558VV/sA+BzATQA7yt64AVMp/bgCAwMxZcoU5fe0tDQuXhytKChgw7xDh9iWmUOHgE6dtL/+u++AqCjA25sF9nv/fbaP8NQpYMkSFgGirCxdyrz0JRImVLt2ATVrMpEyN2dlkpJUQ8z9+4Fr1+RgTqnmAFoA6Ae2h3I6gLcBMB+vwYPLbk9Zsba2Fr7S8nVgdUd5hoqvwifnOdpQUMCSYChCSB85Urbrs7OJfHzY9Rs3qs7/9BM7J5USnTlTtjq3bVMNBVesUCXr+P774svLZDIKDPyRAG8CPAjoRcB2Ah4XGVqeOlU2W8SiUg4Vzc3N0apVK5w4cUJ5Ti6X48SJEzx+FUcw5HJg7Fhg5042yb1nD4u6UBa++Yb5dFWrBowaxc7JZOz7W2+xNoYOZUNHbThwgNkEMC/43FwWprlePeCjj4qWv3TpEjp06ICgoI8BxAKwATAKwAgoIkooqF69bJFWDQ4RhVQwdu/eTRYWFrRjxw4KDQ2lMWPGkKOjo9ZZb3iPi1MScjnRhAmqXtHu3WWvIyuLuS0ARFu3snOafK9atWJtlsSZMyyZBsASbLx4QeTgwL7//LN62ejoaBo+fLhyMtzW1paGDQsiILvYSXzux6VDNmzYQDVq1CBzc3Nq27YtXbp0SetruXBxNCGXE02bpnqpd+zQ/tq7d+9STEwMFRQUUHAwu75mTaLcXJVjqqYVwJEjNdf7338qkerbl2XwmT2bfW/SRJVUNisrixYvXkzW1tZK0Ro5ciTFxsYSUfHCqfAjMyQqtXBVBC5cHE3Mm6d6qRU9JW3x9vYmACSV2pFUmkgAUYsWG2n8+Alkb7+UgB8I+IeAewSkFxGvCxeKpjSLiGBZrwGiTp1YTy4xUZXTcf9+lsZuz549VLNmTaVgdejQga5cuVLERm3SpukbLlwa4MLFKY6gIJWIrF1btmtlMhnVqFGDpFIpAVNf1vPw5YR4ZwImEPA1Af8SkPry994ENCDgzMsh23MCthCwj4BL5O4eQ15ecmXPKimJtRUQoBpiXr9+g9566y2lYFWrVo1+/vlnkpc29jRguHBpQNsH8/jxY2XXn1O5WbNGJVrLlpX9+owMoitXiNavLyALCxkBRNbWeRqHhkAuAR+9FBwHAiJfnv/55TkbAq68FLSn1LSpH73//vs0bNg0MjVl9dap84VSsKysrGj+/PmlOncaA5V2y09FKWtCWKlUCk9Pz1LDNPPtQ8bJ1q3AuHHseP58YMECzWXz81lAvjt3WAqw27fZcUQEk53i8PJiW3/UyQCLhhr78q8cwDCwkDTzAXQG0AXAMwCdALxM3IiNAMYDOA+ALQMOHjwYy5cvR40aNcpw14YLDyRYQQoKCpTZd2JjYxEbG4urV69qLG9ra1ti9EofHx94enrCzMxMh3fBKYkdO1SiNX06Ey6AidDTpyqBUvwNCys+7RnAgg0mJzOn1VGj2KdxY0AqzcVrr8UiPl4RulkhVoU/sQDugsXPmgv2KmYA6A2VaNUEC9cMSKXzQSTBtGnTsHz5cmEfihHCe1yvIJPJkJCQUGoqsrTi0isXg0QigYeHh1pCWCcnJ+Tn5+PRo0eYMmUK3jRqhxrjYfdu5hUvl7N4VJ07M4FSfDT9J7WxAZo0AZo2Vf1t1EiOzz+/gUOHWsPBIQ6+vqMRFxeNmJgYPNfWUQsSMG/2GgAKwPIypgDwxtSpPggP98XBgy7o1o3w55+5MDc3r5SJWHggQQ2IER0iIyOjiKA9efIEERERiIqKUiaElZeytd/Ozg6HDx/m4iUCWVksE8/t22wbzB9/lFze1BRo2LCoSNWsyRJlFCYlBXBySgHgCGAQgD1qv1tYWMDBwRspKT7Iy1Mkh/WGnZ0P0tMV36sDKL43/uOPwCefMAfWCxfYfsXKCh8qioRcLsezZ89K7YUls0DkWmFiYgKZTIb09HS89dZbGDhwIFasWIGaNWuWfjFHjYKC4uehHj3SPA9Vq5a6ODVpAjRooNr7Vxpr1gCAI5ycYjBxYiNUr/6N2lSBiwuLJf9q9ukOHYDXXlNP7FoYiYR52v/1FxOt3r0rt2iVF97jKsShQ4cQFhZWRJDi4uJQUFxK5GKwsrIqNhls4bkvLy8vmJub49mzZ5g7dy6++eYbyOVyWFpaYtq0aZgxYwZsCifw4wB4GaglWl2cbt9mMeA1zUM5OLAhIBELk7x2LdCsmSqtfXlISmLCl54O7N2rCqusLYGBLEbXqyhCVgUHs7haRCxtWosW5bfVGOBDRQ0IkRC28FxVSSuNDg4OJQZNK47//vsPkyZNwunTpwEAPj4+WL58OYYOHVrmuioLSUnq4qSYh0pNLb68tTXrNSl6UE2bsuSvQ4eyIWPfvkxkhFgnmT2bRWx44w2WXLYs007//gt07w7k5DCbC+eArV6dCeuuXao483v3VtxeQ4cLlwa0fTCLFi3Cw4cPi10p9PT0hKmpeCNrIsLvv/+OqVOn4vHLbKTt27fHunXr0FbbYFBGSFYW6zG9KlLF5zBkG6AbNFCJk0KoatVSF5DLl5lAZGSwFGMHDgiT2fn5c9ZWZiabN/vgA+2vvXOHbWxOSQH69AF++w24eFE9A09ICEuaIZGw8o0aVdxmQ6dSRkAVAmPynM/OzqalS5eSjY2N0tnw448/ppjyhtA0EPLzie7dI9q7l22z6d+fqF69kvfz1axJ1KcP0cyZRLt2sT18OTmlt3XjBpGjI6ujSxe2bUYopk+nl1t7St8oXZjHj4m8vdm1HTqwyKbF0asXKzN8uDD2GgPcc14DxiRcCmJiYmjEiBFK8bKxsaElS5ZQdna2vk0rEbmcKCqKxbJasYLoo4/YS66IdFDcx8WFqHNnookTWdjjCxeIyvuf6vZtVcyqjh1ZsguhSEggsrZmdf/xh/bXPXtG1KABu65RIxbpoTj+/ZeVMTEhCg8XxmZjgAuXBoxRuBRcvnyZfH19lQJWq1Yt+u233wxib1pSEtHZs0SbNhGNHcs2BSt6OsV9rK2J2rQh+uQTFkP977+J4uLK1nMpifv3WfowgKh1a5ZWTEgUewbbtNHe5owMonbtVJEZoqI0l+3SRRXGpirBt/xowNiz/BARfvnlF0yfPh0xMTEAgLfffhtLly5FvXr1xAmNW4jsbOD+fSlCQ6W4e1eCsDAz3LkjwUtTimBiAtSvX3Qeqnbtsk1kl4WICBasLyaGrRqePAk4OwtXf1wci+2ek8NcFd59t/Rr8vNZ6OajR5kt588Dr79efNmTJ4Fu3Zg7Rng4ywpUVeBzXBow5h5XYTIyMmjevHlkaWmp7IGJ+1lDQBgBBRp7UTVqEPXuTTRjBtHOnSxXoDbzUELy9ClRrVqqoVhiovBt+Puz+n19tettyWSqMNBWVkQXL2ouK5ezegEW0LCqwYeKGqgswkVElJCQQEOGDNGRcO0rJFLPiaW4Wk/r1uXQv/8KPxQrD7GxRHXrMhvr1mXfhSY6WjVHd/y4dtcohpUmJkSHD5dc9vBhlcCJYb+hU573k3vOGwl5eXnYsGEDFi1apNwn2blzZwwbNgxDhgwRpc1Ll6TIzMxB48ZyeHhYQiJpA6ANrK3NYQjuZYmJbHj18CFzUTh5krkVCE1QEIv3/uabrL3SWLUKWL2aHX//PdCrl+ayRMCcOex4wgRx7K+UiCikBoMx97jkcjn98ccfVK9ePWVPqGXLlnTu3Dl9m6ZXXrwgeuMN1lPx8WGRQ8XgyRMic3Pts+L88INqGL1yZenl9+1jZW1t2epjVYQPFTVgrMIVGhpKfn5+SsHy8PCg7777jmSKoONVlJQUtmoIsFXE+/fFa+vzz1X+YKXx559saAgQTZ1aevmCAqLGjVn5uXMrbquxwoVLA8YmXC9evKAvv/ySTExMCACZmZnR9OnTjcZ+MUlPZw6cAEttf+eOeG1FRhKZmrK2zp4tueyFC2yOCmC+a9r8v2XXLlbe0ZEoOVkIi40TLlwa0PbB3Lt3j8LCwigtLU1HlqmTn59PmzZtImdnZ2Uvq2/fvhRelbwRSyArS+Xr5OhIdPOmuO199hlr6513Si539y6RkxMr27MnUV5e6XXn5akWFZYsEcZeY4VPzlcQf39//P333wBYnKzSNlR7eHgItn/xxIkTmDRpEu7cuQMAaNy4MdauXYvu3bsLUr+xk5sL9OvHUtnb2QHHjgHNm4vX3qNHLFoqACxcqLlcVBTg58ciobZrp/1G7h9/ZIsKrq7Al18KYnKVggtXISwsLGBnZ4f09HSkp6cjLCwMYWFhGstLpdJiI0a8KnL29vYaozw8fPgQU6dOxcGDBwEAzs7OWLx4McaMGSPqpm5jIj8fGDiQiZW1NXDkCCD2vvPFi1k8rJ49NcfDSkpiohUdzQIQHj7MoqWWRm4usGgROw4MBGxthbO7qsA954shPT1dY8BAxfe4uDjIZDKt2rexsVGKmaenJ+zt7SGVSnHt2jWEhISgoKAAJiYm+OKLL7BgwQI4C+nybeQUFLDQNHv3sugOhw9r55JQER48YB7ucjmLMlGcSGZlsegTFy8CPj4sSqm23u6bNqlcHx49AqyshLXf2OARUAXCzs4ODRs2RMOGDTWWkclkSExMVIpZVFQUHj58iEePHiE6Ohrx8fFISkpCbm4uMjMz8eDBAzx48KDYunr06IE1a9agUVWIYVIG5HLg009Vw6/9+8UXLYD1huRyFnqmONFS9AAvXgQcHVlPUFvRysoCvvqKHc+Zw0WrvHDh0oKsrKwSQzYrMgLl5+drVZ+JiQlMTU2Rl5eH3r1749ChQ1U2YKAmiICxY4GffmJ7H/fsYcM2sbl3D/j5Z3Zc3NwWETB6NOv5WVoCf/7JMvtoy5YtQHw8i2M/apQwNldFuHAV4ueff0ZoaGgRUUpJSdHqeolEAnd392IDERae93JycgIA5Ofnw1zbIOdVCCLA3x/45hu2KXvXrrIF7KsICxey9j/4AGjZsujvM2cCP/ygEtOOHbWvOz1dFbJ5/nzt49tzisKFqxCbN2/Gv//+W+xv1tbWpU7Ce3l5lSmHIhetohAxcdiwgX3//ntg0CDdtH3nDhMjoPgkscHBwIoV7Pibb4D33itb/evXswiq9eoBH31UIVOrPFy4CvHBBx+gWbNmxfaYSloZ5AjHwoUqcdi6FRgxQrdtE7FY782aqf+2axcQEMCOly1jqcPKQnIysHKlqh2+YFxBRPMqMyCMzXO+qhIUpNrnt3atbtsOCWHtSiQsimph/vpL5UE/aVL5Ah/Ons2ub9JEO6/6qkR53s/KlxaXY5SsXct8mgAWjcHfX7ftK4aGgwaxwIcKLl8GPvxQ5ZaxejXKHBnj2TN2fwBbsayEyah1Dn+EHL3z9dfA5MnseP58NselS65fZ1mApFLWvoKwMJaQNSsL6NED2L69fKKzfDnLCtSype4WGSo7XLg4euWHH5jbAwBMn64uHLpC0dsaOpR5wAMsBLSfH/DiBdCmDbBvX/lWAWNjmcMpwPy3+DSpMHDh4uiNX39lDqYA26+3bJnuX+wrV5gvlokJMG8eO5eczGLKP33KYucfPlz+bTlLl7I49R06aBennqMdXLg4emH/fmDYMOahPno0mwPSR29E0cP76CPmppCdzRJc3LnDtuQcOwa4uZWv7idPgG3b2PGSJby3JSRcuDg658gRNgkukzHB2LpVPy/1hQssA4+JCTB3LpuAHzyYZeNxcGC/1apV/voXL2bbg7p1Azp3FspqDsD9uDg65sQJoH9/9kIPGMAcTPW1yqbobX3yCUudNmoUcOgQ28z9xx/AG2+Uv+7wcFVYnMWLK2wq5xV4j4ujM86dY8Ow3Fygb1/m1KkvR8yzZ4F//mGbt2fPZhueFSL6668sMUZFWLiQ9Sh799YcFodTfrhwcXTClSsq14J332XiUIbdUYKj6G199hnrZS1dyr5//TUT1Ypw545qo7Yi7hZHWPhQkSM6N28y14L0dKBLF+D339lwTF+cOgWcPs3cG5o0ASZOZOe/+kqYiA3z5zP//w8/LH6jNqfi8B4XR1Tu3gXeeQdISWGRFA4d0m8MKiKV28O77zLHVyIW2G/WrIrXf/06E2aJpOSQz5yKwYWLIxoPHrAVtRcvgNatK+YPJRT//MNWDc3M2LEiKOC6dcKsbCpEcejQssXp4pQNLlwcUYiIALp2BRISWKSFY8eYi4E+KdzbMjVl823durHEFUKsbF64wFw9TEyKD4vDEQ4uXBzBiYpighATAzRqBBw/DhhCGP1jx4BLl1jPKjsbaNWKOcIKNd82dy77+8knQN26wtTJKR4uXBxBiYtjPa3Hj9nL+88/5fc8FxIi1RwWEbPtyBGW6kwITp5kH3NzlYBxxIOvKhbi7t27AABvb284OjrywIFl5Nkzlvnm4UPmcX7yJNs2Ywjs389WNwHA3Z31vtzdhambSCVWY8ZonziDU364cBViypQpyoSwVlZWJSaD9fb2hre3Nw+//JKkJLZ6GBrK0nWdPAlUr65vqxgFBarN3ObmTLTq1BGu/qNH2fyWpaUwK5Oc0uHCVQgbGxs4OzsjKSkJ2dnZePjwIR4+fFjiNW5ubiWKm4+PD1xcXCp17y01lbkW/Pcf4OHBRKt2bX1bxSBijq+pqez7nj3CZsAmYl73gCpXIkcHiBWONTIykj799FOqVasWWVpaUp06dWjevHmUm5urVu6///6jTp06kYWFBVWrVo2WL19epK49e/ZQgwYNyMLCgpo0aUKHDx8uky1lDQ2blZVFDx8+pDNnztDPP/9Mq1atosmTJ9PAgQOpY8eOVKtWLTI3NycAWn0sLCyodu3a1KlTJ+rXrx+NGDGChg4dSr6+vnTw4MEy3YuhkZ5O1KEDC0vs4lI07LG+mTNHFQ76ww+Fr3/fPla3rS1RYqLw9VcFyhO6WTTh+uuvv2jkyJF07NgxevToER08eJDc3d0pICBAWSY1NZU8PDxo2LBhdOfOHfrll1/IysqKvv76a2WZf//9l0xMTGjFihUUGhpKc+bMITMzM7pdhjdEjJjzcrmcnj17RiEhIXT48GHaunUrTZkyhT744ANq2bIl+fj4kJWVVamiZmVlRQcOHCB5eQKZ65msLKIuXdiL6+hIdOOGvi1SZ+NGlWhZWhK9eCFs/QUFRI0bs/rnzBG27qqEQQlXcaxYsYJq166t/L5582ZycnJS64XNmDGDGjRooPw+cOBA6t27t1o97dq1o88//1zrdisqXKmpqRQaGkrHjx+nHTt20JIlS2j8+PH0wQcfUJs2bcjb25ukUqnWPTBzc3OytLQkiUSiPNe9e/cyibG+yckh8vNjL62dHdHly/q2SJ09e1jiC4VwzZ8vfBu7dqlEOzlZ+PqrCuV5P3U6x5WamgrnQg49Fy9exFtvvaU2we3n54fly5cjOTkZTk5OuHjxIqZMmaJWj5+fHw4cOKCxndzcXOTm5iq/p6WlaWXfTz/9hLt37xZJCJuRkaHV9SYmJvDy8ip2vqvwObuXa/Dp6ekICgrC6tWr8c8//6BZs2YYN24cFi5cCBcXF63a1Af5+Sye1rFjgLU1cysoLlW9vjh5Ehg+nEkWwBxfJ00Sto2CAtVG7alTAUdHYevnlIKIQqpGeHg42dvb07Zt25Tn3nnnHRozZoxaubt37xIACg0NJSIiMzMz+vnnn9XKbNq0idzd3TW2NX/+/GJ7OqUpeseOHTX2khwdHalRo0b0zjvv0MiRI2n27Nm0efNmOnDgAF29epViY2OpoKCgrI+FiIgePXpE/fv3V7bl5ORE69ato7y8vHLVJyb5+UQDB7KehoUF0T//6Nsida5fZz1AxbwTQLR4sfDtfPcdq9vVlc3zccqPToaKM2bMKHUodO/ePbVroqOj6bXXXqPPPvtM7bxYwpWTk0OpqanKT1RUlFYPJjg4mPz9/Wn58uW0c+dOOnXqFD148IAyMjJKfS5CcPLkSXrjjTeUz/H111+no0eP6qRtbZDJiD76iL2wZmZER47o2yJ1wsOJ3N2Zfa+/zv46OxMJnU4zJ4eoRg1W/+rVwtZdFdHJUDEgIAAjR44ssUydQk4ysbGx6NKlCzp06IBtigDcL/H09ERCQoLaOcV3T0/PEssofi8OCwsLWJRjH8dkRY4sPdGlSxfcuHED3377LebMmYN79+7h3XffRZ8+fbB69WrUr19fb7YRsWw8P/3E9uL9+ivQs6fezClCfDwLnZOYyPZGKmYHpk0D7O2Fbeu771giDS8vYNw4YevmaImIQkrR0dFUr149Gjx4cLHDKMXkfOEhUWBgYJHJ+T59+qhd5+vrq9PJeX2QnJxMkydPJlNTUwJAZmZmFBAQQCkpKTq3RS4nmjiR9TCkUqLdu3VuQomkphI1b87sq1OHZcEWaxiXlUXk5cXq37RJ2LqrKga1qhgdHU1169albt26UXR0NMXFxSk/ClJSUsjDw4M++ugjunPnDu3evZusra2LuEOYmprSqlWr6N69ezR//nyDcIfQFffu3aNevXoph49ubm60bdu2cs+nlRW5nGj6dNXq3I4dOmlWa7KzVS4Z7u5EoaFEtWuz7ytXCt/eqlWs7po12ZCRU3EMSri2b9+ucQ6sMIUdUH18fGjZsmVF6tqzZw/Vr1+fzM3NqXHjxqI7oBoiR44coQYNGiifYfPmzenPP/+k6OhoysjIEO0TGJirFK1163IoIyPDYHzOCgqI/vc/lUvG9etE33yjEjGhpybT0lgvDmCT8xxhMCjhMiQqg3AREeXl5dHatWvJ0dFRa5+xin2mK0UL+FJ5XleLFSUhlxONG8dsMzcnOnGCKDdXNWkeHCx8m199xequW5etrnKEoTzvJw9rY0SYmZmhb9++eLOiKWi0whTA+y+PZwJYr4M2tWfxYmDLFhZb66efWCid779nk+aenmwhQUiSk4GVK9nxwoX6y07EYfDHbyRkZGQonVVzc3MhlUrRr18/DB48GD1FWt7LyAAOHszFsGFzAaiCTFlbW4vSnrZ8/bXK+XPDBhZ6OTeXZYsGWIQGoePaBwezjdqNGzPnW46eEbEHaDAY81BRJpPRDz/8QF5eXsqhWteuXenWrVv6Nk0v7NvHVjYBorlzVecV+xJ9fNiEvZAkJqqcWX//Xdi6OXyOSyPGKlwXL16ktm3bKgWrTp06tH//foOZHNc1p06x+SyAaMwYNs9FxFwUvL3Z+c2bhW936lRWd8uWqjY5wsGFSwPGJlzR0dE0fPhwpWDZ2trSsmXLKKcKr7/fvElkb88EpF8/tqKoQOG3VaOG8C4KsbEssgRgeDsFKgsGv8na0Ll27Rry8/Ph7e0NLy8vnUc3zc7OxurVqxEUFISsrCxIJBKMHDkSS5cuLXGnQGUnIoJ56aelAW+9xbJEm5iw37KygKAgdjxnjvCJZpcuBXJygA4dWLBEjmHAhasQgYGB+Oeff5Tf3d3dSwzf7OPjAycnpwpHNyUi/Pbbb5g2bRqePHkCAOjQoQPWrVuH1q1bV6huYycxkW3liY8H3ngDOHiQhUhWsGULS4FWuzZQyk60MvPkCVsIAFiW60ocxNbo4MJVCA8PD9SsWROxsbHIz89HYmIiEhMTcVORZaEYLC0tNYZsLvxd097Jmzdvwt/fH+fOnQMAVKtWDStXrsSgQYMqdbhnbUhPB3r1UiXf+Osv9fAxGRnA8uXseO5cluRVSBYvZiF8unYFunQRtm5OxZAQKaIWVV7S0tLg4OCA1NRU2Gux41Yul+P58+dqMbkKx+hSnHvx4oXWNri6usLLywuurq6wt7eHRCLB7du38ejRIwAsOcf06dMxffp0vbsbGAK5uSxW/IkTgKsr8O+/wKt7zJcvB2bOBF57DQgLE9a3KjwceP11QCZjbXfoIFzdHHXK+n4CvMdVLFKpFO7u7nB3d0eLFi00lsvJyVGK2sOHDxEWFoaIiAhERUUhPj4eycnJyMjIUArh8+fPi61nyJAhWL58OaobSlocPSOXAyNGMNGysWE9rVdFKy0NWLGCHc+fL7xD6MKFTLR69eKiZYhw4SqFgoICxMfHl9r7Sk9P16o+iUQCW1tbSKVSZGZmYtCgQdi5c6fId2E8EAH+/ixsjpkZy4dY3DTfhg0sJVqDBsCQIcLacPcuWwAA2HCRY3hw4SrEli1bcPv2bTVRSkhIgLajaXt7e42T+IrzHh4eMDU1BTFXFEilfNdVYZYuBTZuZMc//MByNb5KaiqwahU7FqO3NX8+E9APPwRathS2bo4wcOEqxM8//4zz588XOW9qagovL68S48j7+PjA1tZW67YkEkmVn3x/lW+/VeUoXLdOc09q7VogJQVo1Iht9xGSGzeAffvYCuLChcLWzREOLlyFGDZsGDp37lxEoNzc3GCicBziiMLBg8Dnn7PjwEDgyy+LL5eczPYNAsCCBSp/LqGYN4/9HTqU7UvkGCZcuAoxVuiQAhytOHcOGDyYTcp/+qlqs3RxBAezifmmTdlQTkguXgQOH2ZiqNjEzTFM+AQLR6/cvg289x7zTn/vPebwqWkE/eIFGyYCrLcl9PSgYpg6ciRQr56wdXOEhQsXR288fsy84lNTgY4dgd27S55oX7WKOZ02bw588IGwtpw8yT5mZsyZlWPYcOHi6IVnz5hoxcWxuaQ//mDJZUsqv2EDO164UNjeFpFKrMaMAWrWFK5ujjhw4eLonIwM5hX/4AFQowbLiO3kVPI1K1cCmZlAq1ZsSCkkR48CFy6wPZCzZglbN0ccuHBxdEpeHptUv3oVcHFhouXjU/I1CQkq365Fi4Td7EykmtsaPx7w9haubo54cOHi6Ay5HPjkE+Dvv9mw8PBhoGHD0q9bvhzIzgbatRM+Ce2BA8x3y9YWmDFD2Lo54sGFi6MTiICAALaVxtSUOXm2a1f6dbGxLHQNIHxvSyZTzW1NmgS4uQlXN0dcuHBxdMKKFSpXhu3btQ/Kt2wZc5Xo2LH47T8VYc8eti/R0ZGJKsd44MLFEZ3t21n4GQBYvRoYPly766KjVYH8hO5tFRSonEynTlWP88UxfLhwcUTljz+A0aPZ8fTpwJQp2l+7dCmbzH/7beED+f34I4u55eqqeXsRx3DhwsURjQsX2CZomYzF11q2TPtrnzxhm64B5rclZG8rN1e1gXrmTMDOTri6ObqBCxdHFO7eBfr0YfNTvXsD33xTNvFZsoSFTe7WjfW4hOS771jGay8vYNw4Yevm6AYuXBzBefqUecUnJwPt27NJ8LLEg4+IYPNigPChZbKzWeILAJg9u2RvfY7hwoWLIygvXjDRiolhMdv//LPs4vDVV2zy3M+PrSYKyZYtbJtRjRrAqFHC1s3RHVy4OIKRmcmGhWFhQLVqzCvexaVsdYSHs4lzQPjeVkaGKgfj/PnC52Dk6A4ej6sQly9fRl5enjKQoGXhBH6cEsnPBwYMAC5fZvsOjx0DypP7Y/FiNpnfu7d2DqplYf164PlzoG5d4OOPha2bo1u4cBVizpw5aglhnZ2dS8yVqIiOWtXjxsvlwGefsWw8VlZsK0+jRmWvJywM2LWLHQvd20pJYRu1FXULHaeeo1v4f75C+Pj4oG7duoiJiUF2djaSkpKQlJSE27dva7zGzMwMXl5eGpNkKM7Z2Njo8E50y4wZwE8/scihe/cCvr7lq2fRIiaC77/PokAISXAwE6/GjYFBg4Stm6N7eELYYiAipKSklJqSrCwZgBwcHODt7Q0XFxfY2dmhoKAAT58+xcyZMzFS6NzxOmTVKmDaNHa8Ywfz1yoPoaFAkyZsT+ONG0AJ6SzLzPPnQO3abI5r3z6gf3/h6uZUHJ4QViAkEgmcnJzg5OSEJk2aaCyXn5+P+Ph4REdH4/79+7h//z4ePXqEqKgoJCQkICkpCRkZGZDJZEhNTUVqamqROj5/mSHi448/Nroh548/qkRrxYryixbAhm9ETFSEFC2ARZfIyGD19usnbN0c/cB7XKWQm5uLuLi4YntchY+zs7O1qs/ExAR2dnaQSCRIS0uDTCYDALRu3Rpr165FR6HX/0XiyBE2pJPJ2DaeVavK791++zbwxhvs+NYtlghDKOLigDp1mCPs4cMsMzXHsOA9rgqyceNG3Lp1S02gnj9/rvX1Li4uGifzFedcXV2VPavc3Fxs2LABixYtwrVr19CpUycMGTIEy5cvR/XyLMnpiEuX2AqiTMY2TK9cWbEtOQsWsL8DBworWgDb75iTw+bdhI7lxdEfvMdViLfeegvnzp0rct7CwqLUyXdvb+9yu08kJCRgzpw5+O6770BEsLKywowZMzBt2jRYG5hr9717QKdOQFISC01z6FDZvOJf5eZNli1aIgHu3CnfaqQmnjxh2Xry84ETJ4CuXYWrmyMc5elxceEqxLfffovY2NgiAuXs7KyTrNM3b96Ev7+/UjyrV6+OFStWYNCgQQaR9ToqinmyR0UBbdsyMShD8u5i6duXid/QoSpXCKEYPZpt1O7aldnKMUzKNZVDVYDU1FQCQKmpqfo2pVTkcjn9+uuvVKNGDQJAAKhjx4509epVvdr14gVRo0ZEAFGDBkTPnlW8zqtXWX1SKVFYWMXrK0x4OJGJCav/33+FrZsjLOV5P41rGasKIJFIMHDgQISFhWHx4sWwtrbGv//+i7Zt2+LTTz9FfHy8zm3KymKZdUJDWTKJY8dYHKuKogjkN3w40KBBxesrzMKFbA6uVy+gQwdh6+YYACIKqcFgTD2uV4mOjqbhw4cre1+2tra0bNkyysnJ0Un7eXlEffqwnoujI9Ht28LUe/Eiq9PEhPWOhOTuXSKJhNV/7ZqwdXOEpzzvJ5/jMhIuXboEf39/XLlyBQBQp04dzJ8/Hx07doSnp6cobRIB48aZY+dOM1haEg4dykGHDnJYW1tXeM7Nz49l+/n0UxYfS0gGDAB++435hO3bJ2zdHOHhc1waMOYeV2FkMhn9+OOP5OnpqeyBifsJIiZf+QS8pzyfkZFRofs4d471hkxNiSIiBHo4L7lxg9UtkQjXO+SIC5/jquRIpVI0a9YM9evX10FrpgBavjweA+APwWpWzG19+inbiiMkinRjQ4awLUScygkfKhoJz58/x9y5c7Ft2zbI5XJYWFhg5MiR+PDDD9FBpNnnvDzg779N0KePTO18RYaKp0+zxBdmZsDDhyygn1BcvMgm4k1M2EKCTvSdU2G453wlJD8/H5s3b8aCBQuQkpICABgwYABWrFiBWrVqidq2jY2wkRSIVL2t0aOFFS1A1dsaMYKLVmWHC5cB89dff2HKlCkICwsDADRr1gzr1q3D20Jnj9ARJ08CZ8+yyKOBgcLWfeoUczI1MwPmzRO2bo7hoZM5rtzcXDRv3hwSiQQhISFqv926dQtvvvkmLC0tlZ7ir7J37140bNgQlpaWaNq0KY4cOaILs/XG/fv30bt3b/Tq1QthYWFwc3PDtm3bcP36daMVLSKVoHz+OQvtLGTdit7WmDFAzZrC1c0xUERbKijEl19+ST179iQAdPPmTeX51NRU8vDwoGHDhtGdO3fol19+ISsrK/r666+VZf79918yMTGhFStWUGhoKM2ZM4fMzMzodhmWjLRdtTh16hT9+eefdOPGDUpISCCZTFbme60IycnJNHnyZDI1NSUAZGZmRgEBAZSSkqJTO8Tg2DG22mdpSRQbK2zdf/2lqjsmRti6OeJTnlVF0YXryJEj1LBhQ7p7924R4dq8eTM5OTlRbm6u8tyMGTOoQYMGyu8DBw6k3r17q9XZrl07+vzzzzW2mZOTQ6mpqcpPVFSUVg+ma9euau4AZmZmVLNmTerQoQMNGDCA/P39acWKFbRr1y46ffo0hYeHU2ZmZhmfSFEKCgpo69at5Orqqmy7T58+dP/+/QrXbQjI5UTt2jFxmTxZ+LpbtWJ1BwQIWzdHN5RHuESd40pISMDo0aNx4MCBYqMcXLx4EW+99RbMzc2V5/z8/LB8+XIkJyfDyckJFy9exJRX8rb7+fnhwIEDGtsNCgrCwnIELW/QoAGSk5MRExODxMRE5Ofn48mTJ3jy5EmJ1zk6OhYbMaLwx93dvdhAgadOncKkSZNw69YtAMDrr7+ONWvWwM/Pr8z2Gyp//cWSaFhZsTDPQnLwIHD9OltIELpujuEimnAREUaOHImxY8eidevWePz4cZEy8fHxqP2KI4+Hh4fyNycnJ8THxyvPFS5T0p69wMBANbFLS0vTKr7V5s2blcd5eXnKAIKaQjfHxMQgKysLKSkpSElJwd27dzXWbWJiAg8PD7i4uMDe3h5EhIcPHyIxMREAE79FixZh7NixMKtInBgDo/Dc1oQJwCv/KSuETKaa25o0CXBzE65ujmFTZuGaOXMmli9fXmKZe/fu4e+//0Z6ejoChV4+0gILCwtYVDBpnrm5OWrWrImaJcz0EhFSU1Px+PFj3L59Wy10c3x8PJKTk5Geno78/HzIZDLExsYiNja2SD3jx4/HwoUL4VLWJIRGwB9/qHpEijDPQrFnD4vh5eAABAQIWzfHsCmzcAUEBJSa3KFOnTo4efIkLl68WERAWrdujWHDhuGHH36Ap6cnEhIS1H5XfFfsv9NURqz9eYUhIiQlJZXY41IMK7XF1tYWdnZ2StH79NNPsXHjRhHvQn/I5are1pdfCtsjKihQ+YRNncpyOXKqDmUWLjc3N7hp8S9w/fr1+Oqrr5TfY2Nj4efnh19//RXtXmb69PX1xezZs5Gfn68cHh0/fhwNGjSA08t/ib6+vjhx4gQmTZqkrOv48ePwLW8OrBJYsWIFbt68qSZOOTk5Wl1rbm6uNrdV3DyXl5eXwUU0FZMDB4D//gPs7ITvEf30E8t67eIC+PsLWzfH8BFtjqvGK27Rti9DZb722muo9tKJZ+jQoVi4cCE+++wzzJgxA3fu3MG6deuwZs0a5XX+/v54++23sXr1avTu3Ru7d+/GtWvXsG3bNsFtPnz4MM6ePVvkvKura6mhm11dXQ0iSqmhIJerekSTJjGBEYq8PFXC2JkzmTByqhjiLHAWJTIysog7BBHRf//9R506dSILCwvy8fGhZcuWFbl2z549VL9+fTI3N6fGjRvT4cOHy9S2tsutO3fupODgYNq9ezedO3eOIiIidBb3qrLx66/MRcHBgSgpSdi6N29mdXt5EQngjcLRMzwelwYqwyZrY0ImY9l67t1jPSMht+BkZwN16wKxscDGjcD48cLVzdEP5Xk/eVgbjuD8+isTLScn4eeftm5lolWjBjBqlLB1c4wHLlwcQSkoUM0/TZ3KXBWEIiMDCApix/Pmsc3anKoJFy6OoPz8M/DgAZuMnzhR2LrXrweePWNDxY8/FrZujnHBhYsjGPn5wKJF7Hj6dGFX+1JSWMZsgGW+rkSbCzjlgAsXRzB++gl49Ig5mgo9aR4czMSrUSNg8GBh6+YYH1y4OIKQlwcsXsyOZ85kW3yE4vlzQOHat2gRC83Mqdpw4eIIwg8/AI8fA56ewNixwta9YgWbmG/RAujXT9i6OcYJFy5OhcnNBRS7u2bOBITc1RQXx/y1ANZGMZGBOFUQ/s+AU2G+/x54+hTw9mahk4UkKIg5nfr6Aj17Cls3x3jhwsWpEDk5wJIl7HjWLBYsUCiePgW+/podf/UVwLeCchRw4eJUiG++AWJiWPILoT3Zv/qKTfp36QJ07Sps3RzjhgsXp9xkZwNLl7LjOXOE9WR/+JANQQHVaiWHo4ALF6fcbN0KxMezdGCffCJs3QsXss3aPXsCHTsKWzfH+OHCxSkXmZnAsmXseO5coFC+kwoTGgrs2sWOeW+LUxxcuDjlYvNmIDERqFNH+H2D8+ezJBv9+gGtWglbN6dyIGp6MmPjxIkTyMjIUEY19fDwgAl30y5CejqgyJcyb56w+wZv3gR++42tICr2PXI4r8KFqxBLly7FyZMnld9NTEzg6elZYhx5b2/vKheccONG4MULoF49YNgwYetWBB0cMgRo0kTYujmVBy5chWjcuDEyMjIQExOD+Ph4yGQyZeKMkrC1tS01IaynpydMTY3/caelqaI0zJ8PCHlLly4Bf/7J9iIq4tVzOMVh/G+SgKxfv155LJPJkJCQUGpC2NTUVGRkZOD+/fu4f/9+ifW7u7srE8IWFBQgNjYWU6dOLZKp25BZtw5ITgYaNhQ+SoMiueuIEUD9+sLWzalc8JjzFSQzMxOPHj3CrVu3iiSETUpKQkZGBvLy8jReb2JiguXLl2PixIkwF3JpTgRSUoBatYDUVGD3bmDQIOHqPn2aOZqambFAhLVqCVc3x7Apz/vJe1wlIJfLkZiYWGpC2OTkZK3rdHBwgL29PYgIz549Q25uLqZOnYqvv/4awcHB6N27t8GmOVuzholW48bAgAHC1UvEHFgBYPRoLlqc0uE9rkIsXboUN27cUApSXFwcCgoKtGrD2tpaq3muwr0qmUyGH374AYGBgcps2H5+fggODkajRo0qdtMCk5TEBCU9na36ffihcHUfPcocTS0tWSBCb2/h6uYYPuXpcXHhKkTnzp1x5swZtXMSiUS5slhSQlgHB4dy95TS0tKwZMkSrFmzBvn5+TAxMcH48eMxf/58ODs7l6tOoZkzh22mbtYMuHFDuPAyRECbNsD168CUKcDq1cLUyzEeuHBpQNsH8+uvv+LZs2dqAqXL1cCHDx9i6tSpOHjwIADA2dkZixcvxpgxY/S6Ivn8OVC7Ngvmd+AA0LevcHUfOMAcTW1sgIgIwN1duLo5xkG55qCFzkpriJQnU64+OX78ODVu3JgAEABq0qQJHT9+XG/2zJjBMke3bEkklwtXr0xG1KQJq3vWLOHq5RgX5Xk/+ZYfA6R79+4ICQnBxo0b4ezsjDt37uCdd97BBx98gIcPH+rUlsREYMMGdrxwobAxsfbsAe7cYbkXp04Vrl5O5YcLl4FiamqK8ePHIzw8HBMnToSJiQkOHjyIxo0bY8aMGUhLS9OJHStWAFlZbB6qd2/h6i0oUDmZTp3Ksl5zONrC57iMhNDQUEyePBl///03AMDDwwPTp09Hjx49ULt2bVHajI+XoEkTK+TkSLB/fw7eeUcGgK2gVtRlY8cOFgrHxQWIjBQ2ByPHuOCT8xqoDMIFAESEw4cPY/LkyToaMq4BMAnABQCqoFgZGRmwqUD+sbw8oEEDlhVo5Uo+TKzqlOf95ENFI0IikcDBwQF2OumeSAC88fJ4nqA1f/edKpXZF18IWjWnisA9542Ep0+fYvr06fj1118BMA/8CRMm4L333kMTkcIoEAGXLmWjffuDapPy1hXIP5adrUplNnu2sKnMOFUHLlwGTmZmJpYvX46VK1ciJycHEokEY8aMweLFi+Hm5iZ6+927C1vf1q1AbCxQvTrb3sPhlAcuXAYKEeHnn3/GjBkzlGF1OnfujLVr16JZs2Z6tq58ZGSwPIkAi7slZHINTtWCC5cBcvXqVfj7++PixYsAgFq1amHVqlXo37+/wW7A1oYNG4Bnz4DXXmOhazic8sKFqxB//PEHUlJS1Lb86GYinBEbG4tZs2bhhx9+AADY2Nhg1qxZmDJlCiwtLXVmhxikpDCfMIA5sgoZ7plT9eDCVYg1a9bg1KlTaufs7Ow0brBWnK/ofsacnBysWbMGS5YsQWZmJgDg448/RlBQELwrSaiENWuYeDVqJHwAQk7VgwtXIdq1awepVKoMa5Oeno709HSEhYUhLCxM43VSqRQeHh4lhrTx8fGBvb292lCPiPD7779j2rRpiIyMBAC0b98e69atQ9u2bUW/X13x/DkTLoAlwOD5RzgVhTuglkB6enqpoZvj4uIgk8m0qs/S0hKurq6ws7MDESE6OhoZGRkAAG9vb6xYsQJDhgyBVKiYMQbCjBlsmNiiBXDtmnAhcTiVA+45rwExPefz8/MRFhamFrr56dOnSEhIUAvdrOkxS6VSzJo1CzNmzICtra2gthkCcXFsMj47myXCEHK/I6dywEM3C0xWVlapPa7Y2Fjk5+drVZ+FhYUyWYZMJsOzZ88wceJELKrECQSDgphotW8P9Oqlb2s4lQUuXIWYPXs2rl27phSnlJQUra6TSCRwd3cvNXSzo6OjUbszlJWnT4Gvv2bHX30lbEgcTtWGC1chLly4gNOnT6uds7GxKTUhrJeXF8z4+n4RvvqKbaju3Bno2lXf1nAqE3yOqxCHDh1CcnKymkC9uhLI0Y6HD1nuRZkMOH8e6Nix9Gs4VRM+x1VB3n//fX2bUGlYtIiJVs+eXLQ4wsMXpjmCExoK7NzJjhcv1q8tnMoJFy6O4CxYwELi9OsHtGqlb2s4lREuXBxBCQkB9u5lK4gLF+rbGk5lRVThOnz4MNq1awcrKys4OTnhgw8+UPv96dOn6N27N6ytreHu7o5p06YVyRx9+vRptGzZEhYWFqhbty527NghpsmcCjJ3Lvs7eDDQtKl+beFUXkSbnN+3bx9Gjx6NpUuXomvXrigoKMCdO3eUv8tkMvTu3Ruenp64cOEC4uLi8PHHH8PMzAxLly4FAERGRqJ3794YO3Ysdu3ahRMnTmDUqFHw8vKCn5+fWKZzysmlS8w7Xiplw0UORzSETOyoID8/n3x8fOjbb7/VWObIkSMklUopPj5eeW7Lli1kb29Pubm5REQ0ffp0aty4sdp1gwYNIj8/vzLZY2wJYY2V7t1ZctdPPtG3JRxjwmASwt64cQMxMTGQSqVo0aIFvLy80LNnT7Ue18WLF9G0aVN4eHgoz/n5+SEtLQ13795Vlun+SuxgPz8/ZYA9TeTm5iItLU3twxGX06eBf/5hcbbmCZtbg8MpgijCFRERAQBYsGAB5syZgz///BNOTk7o3LkzkpKSAADx8fFqogVA+T0+Pr7EMmlpacjOztbYflBQEBwcHJSf6tWrC3ZvnKIQqea2Ro0CatXSqzmcKkCZhGvmzJmQSCQlfsLCwiCXywGwvX8ffvghWrVqhe3bt0MikWDv3r2i3EhhAgMDkZqaqvxERUWJ3mZV5u+/mXe8pSUwZ46+reFUBco0OR8QEICRI0eWWKZOnTqIi4sDADRq1Eh53sLCAnXq1MHTp08BAJ6enrhy5YratQkJCcrfFH8V5wqXsbe3h5WVlUYbLCwsYMEzMegEIpVYffEFUEkCtnIMnDIJl5ubm1YpsVq1agULCwvcv38fnTp1AsDiVj1+/Bg1a9YEAPj6+mLJkiVITEyEu7s7AOD48eOwt7dXCp6vry+OHDmiVvfx48fh6+tbFrM5InLoEAsOaGPDAgZyODpBrJUCf39/8vHxoWPHjlFYWBh99tln5O7uTklJSUREVFBQQE2aNKEePXpQSEgIHT16lNzc3CgwMFBZR0REBFlbW9O0adPo3r17tGnTJjIxMaGjR4+WyRa+qigOMhlR06ZsJXHWLH1bwzFWyvN+iiZceXl5FBAQQO7u7mRnZ0fdu3enO3fuqJV5/Pgx9ezZk6ysrMjV1ZUCAgIoPz9frcypU6eoefPmZG5uTnXq1KHt27eX2RYuXOLwyy9MtBwciF7+/4jDKTPleT95WBtOuSgoABo3Bh48YJEgFKuKHE5ZKc/7yfcqcsrFzp1MtFxcAH9/fVvDqWrweFyFOHjwIJKSktQCCVa1cMvakJen2kA9YwbAO7EcXcOFqxBr164tErrZysqq1NDN3t7eMDc314/ReuD774HHjwFPT2D8eH1bw6mKcOEqRKdOnWBpaalMlpGUlITs7Gw8fPgQDx8+LPFaNze3UhPCOjs7G33vLTtbFRxw9mzA2lq/9nCqJnxyvgSys7MRGxtbYnqymJgY5OXlaVWfmZmZMj2ZXC7Hs2fP8OWXXxpVerK1a4HJk4Hq1YHwcID7+XIqCk8IqwFdJIT977//EBYWhsjIyDIlhJVIJAgMDERgYKDBJ4TNyGDJXRMTgW++YfsSOZyKwoVLA+V5MESEtLS0UhPCxsfHK/dmloatrS1cXFxgZ2cHIkJUVJQycoWXlxeWLVuG4cOHQ2qgOeqXLQMCA5l43bvHIkFwOBWFC5cGtH0wM2fOxJUrV5TilJmZqVX9JiYm8PLyKnUS387OTu06IsLBgwcREBCgjKjRtm1brFu3Du3bty//DYtASgpQpw6QnAz89BMwfLi+LeJUFrhwaUDbB9O1a1ecOnVK7Zyjo2OJE+7e3t5wd3eHiYlJue3Lzc3F2rVr8dVXXyEjIwMAMHz4cCxbtgw+Pj7lrldI5s9njqavvw7cvg1U4HY5HDW4cGlA2wdz+PBhpKamKkXK29sbNjY2OrMzLi4Os2fPxvbt2wEA1tbWmDlzJqZOnVpiNAyxef6c9bbS01kijP/9T2+mcCoh5ZqDFmzDkQFjbHsVr169Sh06dCAABIBq1qxJe/bsIblcrhd7pk9nexKbN2cbqzkcITGY0M2citG6dWucP38ev/zyC6pVq4YnT55g4MCBePvtt3Hz5k2d2hIfD2zYwI4XL2aJMDgcfcP/GRooEokEgwcPxv379zF//nxYWVnh3LlzaNWqFUaPHl0kwKJYBAUxp9P27YHevXXSJIdTKnyOy0h4+vQpZsyYgd27dwMA7OzsMGHCBLz//vtoKlICw+hoCd54wwp5eRL88Uc2unRhbh/W1tZGvwOAYzjwyXkNVAbhUnD+/Hl8+eWXOhoybgXwOYBTALoqz2ZkZOh00YJTueFhbSo5RISkpCQdpVuTAHB/ecyDbXEMC77J2ki4e/cuJk+ejOPHjwNgiURmzpyJ7t27o5aI+cBCQ7PQqNExtXPWfGc1R89w4TJwXrx4gfnz52Pr1q2QyWQwNzdHQEAAAgMDi3jii0GbNqI3weGUGS5cBkp+fj62bt2K+fPnIzk5GQDQv39/rFy5EnXq1NGzdRyOfuHCZYD8/fffmDx5MkJDQwEATZs2xbp169ClSxc9W8bhGAZcuArxyy+/IDExUW1fopeXl86im4aHhyMgIAB//PEHAMDFxQVfffUVRo0aBVNT/p+Kw1HA34ZCfP311zhz5kyR8+7u7iVusq5odNPU1FR89dVXWLduHfLz82FqaooJEyZg3rx5cHJyquhtcTiVDi5chfDz84O7u7tazK38/HwkJiYiMTERISEhGq+1tLRU25xdnMh5eXnB0tJSeY1MJsP27dsxe/ZsJCYmAgB69uyJ4OBgNGzYUOzb5XCMFu6AWgJyuRzPnz8vNXTzixcvtK7Tzs5OuRr4/PlzZdjnBg0aIDg4GL169SrbzXE4Rg73nNeAmJ7z6enpiIiIwK1bt3D//n1EREQUG7pZE6amplixYgXGjx9fpTIFcTgKyvN+8qGiBgoKChAfH19q6Ob09HSt6pNKpXB3d1cmyygoKEBsbCymTp2KSZMmiXszHE4lgwtXISZOnIhLly4hJiYGCQkJWseSt7e31zhpr/h4eHhUKEoqh8NRwYWrEKGhobh27Zryu6mpqTKWfEmrioaenYfDqWzwOa5CnDx5EpmZmUqRcnd3N9iMOxxOZYHPcVWQrl27ll6Iw+HoHd6d4HA4RgcXLg6HY3Rw4eJwOEYHFy4Oh2N0cOHicDhGBxcuDodjdHDh4nA4RgcXLg6HY3Rw4eJwOEYHFy4Oh2N0cOHicDhGBxcuDodjdHDh4nA4RgcXLg6HY3Rw4eJwOEYHFy4Oh2N0cOHicDhGR5WIgKqITp2WlqZnSzgczqso3suyRJGvEsKlSCFWvXp1PVvC4XA0kZ6eDgcHB63KVolkGXK5HLGxsbCzs4NEIim1fFpaGqpXr46oqCjBE8gaM/y5FA9/LsWj7XMhIqSnp8Pb21vr5DRVoscllUpRrVq1Ml9nb2/P/yEWA38uxcOfS/Fo81y07Wkp4JPzHA7H6ODCxeFwjA4uXMVgYWGB+fPnw8LCQt+mGBT8uRQPfy7FI+ZzqRKT8xwOp3LBe1wcDsfo4MLF4XCMDi5cHA7H6ODCxeFwjA4uXBwOx+io0sK1ZMkSdOjQAdbW1nB0dCy2zNOnT9G7d29YW1vD3d0d06ZNQ0FBgVqZ06dPo2XLlrCwsEDdunWxY8cO8Y3XMZs2bUKtWrVgaWmJdu3a4cqVK/o2SVTOnj2L9957D97e3pBIJDhw4IDa70SEefPmwcvLC1ZWVujevTvCw8PVyiQlJWHYsGGwt7eHo6MjPvvsM2RkZOjwLoQnKCgIbdq0gZ2dHdzd3fHBBx/g/v37amVycnIwfvx4uLi4wNbWFh9++CESEhLUymjzXpVElRauvLw8DBgwAOPGjSv2d5lMht69eyMvLw8XLlzADz/8gB07dmDevHnKMpGRkejduze6dOmCkJAQTJo0CaNGjcKxY8d0dRui8+uvv2LKlCmYP38+bty4gWbNmsHPzw+JiYn6Nk00MjMz0axZM2zatKnY31esWIH169dj69atuHz5MmxsbODn54ecnBxlmWHDhuHu3bs4fvw4/vzzT5w9exZjxozR1S2IwpkzZzB+/HhcunQJx48fR35+Pnr06IHMzExlmcmTJ+OPP/7A3r17cebMGcTGxqJ///7K37V5r0qFOLR9+3ZycHAocv7IkSMklUopPj5eeW7Lli1kb29Pubm5REQ0ffp0aty4sdp1gwYNIj8/P1Ft1iVt27al8ePHK7/LZDLy9vamoKAgPVqlOwDQ/v37ld/lcjl5enrSypUrledSUlLIwsKCfvnlFyIiCg0NJQB09epVZZm//vqLJBIJxcTE6Mx2sUlMTCQAdObMGSJiz8HMzIz27t2rLHPv3j0CQBcvXiQi7d6r0qjSPa7SuHjxIpo2bQoPDw/lOT8/P6SlpeHu3bvKMt27d1e7zs/PDxcvXtSprWKRl5eH69evq92jVCpF9+7dK809lpXIyEjEx8erPRMHBwe0a9dO+UwuXrwIR0dHtG7dWlmme/fukEqluHz5ss5tFovU1FQAgLOzMwDg+vXryM/PV3s2DRs2RI0aNdSeTWnvVWlw4SqB+Ph4tYcLQPk9Pj6+xDJpaWnIzs7WjaEi8vz5c8hksmLvUfEMqhqK+y7pmcTHx8Pd3V3td1NTUzg7O1ea5yaXyzFp0iR07NgRTZo0AcDu29zcvMic8avPprT3qjQqnXDNnDkTEomkxE9YWJi+zeRwjJ7x48fjzp072L17t87brnTxuAICAjBy5MgSy9SpU0erujw9PYusnilWRzw9PZV/X10xSUhIgL29PaysrLS02nBxdXWFiYlJsfeoeAZVDcV9JyQkwMvLS3k+ISEBzZs3V5Z5dfGioKAASUlJleK5TZgwQbngUDjWnaenJ/Ly8pCSkqLW6yr870Wb96o0Kl2Py83NDQ0bNizxY25urlVdvr6+uH37tto/wOPHj8Pe3h6NGjVSljlx4oTadcePH4evr69wN6VHzM3N0apVK7V7lMvlOHHiRKW5x7JSu3ZteHp6qj2TtLQ0XL58WflMfH19kZKSguvXryvLnDx5EnK5HO3atdO5zUJBRJgwYQL279+PkydPonbt2mq/t2rVCmZmZmrP5v79+3j69KnasyntvdLGkCrLkydP6ObNm7Rw4UKytbWlmzdv0s2bNyk9PZ2IiAoKCqhJkybUo0cPCgkJoaNHj5KbmxsFBgYq64iIiCBra2uaNm0a3bt3jzZt2kQmJiZ09OhRfd2W4OzevZssLCxox44dFBoaSmPGjCFHR0e1VaHKRnp6uvLfAwAKDg6mmzdv0pMnT4iIaNmyZeTo6EgHDx6kW7duUd++fal27dqUnZ2trOPdd9+lFi1a0OXLl+n8+fNUr149GjJkiL5uSRDGjRtHDg4OdPr0aYqLi1N+srKylGXGjh1LNWrUoJMnT9K1a9fI19eXfH19lb9r816VRpUWrhEjRhCAIp9Tp04pyzx+/Jh69uxJVlZW5OrqSgEBAZSfn69Wz6lTp6h58+Zkbm5OderUoe3bt+v2RnTAhg0bqEaNGmRubk5t27alS5cu6dskUTl16lSx/zZGjBhBRMwlYu7cueTh4UEWFhbUrVs3un//vlodL168oCFDhpCtrS3Z29vTJ598ovyforFS3DMBoPZvPjs7m7744gtycnIia2tr6tevH8XFxanVo817VRI8HheHwzE6Kt0cF4fDqfxw4eJwOEYHFy4Oh2N0cOHicDhGBxcuDodjdHDh4nA4RgcXLg6HY3Rw4eJwOEYHFy4Oh2N0cOHicDhGBxcuDodjdPwf9EK6ckvOPccAAAAASUVORK5CYII=\n", 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\n", 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" ] @@ -206,12 +208,12 @@ ], "source": [ "fig, ax = plt.subplots(1, 1, figsize=(3, 6))\n", - "_ = net.vis(ax=ax, detail=\"full\", layers=[10, 1], layer_kwargs={\"within_layer_offset\": 150, \"between_layer_offset\": 200})" + "_ = net.vis(ax=ax, detail=\"full\")" ] }, { "cell_type": "markdown", - "id": "7886a6a9", + "id": "843ffa0b", "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:" @@ -219,8 +221,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "f78efb05", + "execution_count": 10, + "id": "2508510c", "metadata": {}, "outputs": [], "source": [ @@ -231,13 +233,13 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "10cc3baa", + "execution_count": 11, + "id": "60790ead", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -248,12 +250,12 @@ ], "source": [ "fig, ax = plt.subplots(1, 1, figsize=(3, 6))\n", - "_ = net.vis(ax=ax, detail=\"full\", layers=[10, 1], layer_kwargs={\"within_layer_offset\": 150, \"between_layer_offset\": 200})" + "_ = net.vis(ax=ax, detail=\"full\")" ] }, { "cell_type": "markdown", - "id": "96d8182e", + "id": "08422604", "metadata": {}, "source": [ "### Inspecting and changing synaptic parameters" @@ -261,7 +263,7 @@ }, { "cell_type": "markdown", - "id": "66a544f8", + "id": "8515ca40", "metadata": {}, "source": [ "You can inspect synaptic parameters via the `.edges` attribute:" @@ -269,8 +271,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "50f8a206", + "execution_count": 12, + "id": "b8f05aa0", "metadata": {}, "outputs": [ { @@ -295,8 +297,8 @@ " \n", " \n", " global_edge_index\n", - " global_pre_comp_index\n", - " global_post_comp_index\n", + " pre_global_comp_index\n", + " post_global_comp_index\n", " type\n", " type_ind\n", " pre_locs\n", @@ -313,11 +315,11 @@ " 0\n", " 0\n", " 0\n", - " 286\n", + " 287\n", " IonotropicSynapse\n", " 0\n", " 0.125\n", - " 0.625\n", + " 0.875\n", " 0.0001\n", " 0.0\n", " 0.025\n", @@ -328,11 +330,11 @@ " 1\n", " 1\n", " 28\n", - " 298\n", + " 289\n", " IonotropicSynapse\n", " 0\n", " 0.125\n", - " 0.625\n", + " 0.375\n", " 0.0001\n", " 0.0\n", " 0.025\n", @@ -343,11 +345,11 @@ " 2\n", " 2\n", " 56\n", - " 286\n", + " 289\n", " IonotropicSynapse\n", " 0\n", " 0.125\n", - " 0.625\n", + " 0.375\n", " 0.0001\n", " 0.0\n", " 0.025\n", @@ -358,11 +360,11 @@ " 3\n", " 3\n", " 84\n", - " 295\n", + " 301\n", " IonotropicSynapse\n", " 0\n", " 0.125\n", - " 0.875\n", + " 0.375\n", " 0.0001\n", " 0.0\n", " 0.025\n", @@ -373,11 +375,11 @@ " 4\n", " 4\n", " 112\n", - " 302\n", + " 281\n", " IonotropicSynapse\n", " 0\n", " 0.125\n", - " 0.625\n", + " 0.375\n", " 0.0001\n", " 0.0\n", " 0.025\n", @@ -388,11 +390,11 @@ " 5\n", " 5\n", " 140\n", - " 288\n", + " 295\n", " IonotropicSynapse\n", " 0\n", " 0.125\n", - " 0.125\n", + " 0.875\n", " 0.0001\n", " 0.0\n", " 0.025\n", @@ -403,11 +405,11 @@ " 6\n", " 6\n", " 168\n", - " 287\n", + " 289\n", " IonotropicSynapse\n", " 0\n", " 0.125\n", - " 0.875\n", + " 0.375\n", " 0.0001\n", " 0.0\n", " 0.025\n", @@ -418,11 +420,11 @@ " 7\n", " 7\n", " 196\n", - " 305\n", + " 290\n", " IonotropicSynapse\n", " 0\n", " 0.125\n", - " 0.375\n", + " 0.625\n", " 0.0001\n", " 0.0\n", " 0.025\n", @@ -433,7 +435,7 @@ " 8\n", " 8\n", " 224\n", - " 299\n", + " 303\n", " IonotropicSynapse\n", " 0\n", " 0.125\n", @@ -448,7 +450,7 @@ " 9\n", " 9\n", " 252\n", - " 284\n", + " 280\n", " IonotropicSynapse\n", " 0\n", " 0.125\n", @@ -479,28 +481,28 @@ "" ], "text/plain": [ - " global_edge_index global_pre_comp_index global_post_comp_index \\\n", - "0 0 0 286 \n", - "1 1 28 298 \n", - "2 2 56 286 \n", - "3 3 84 295 \n", - "4 4 112 302 \n", - "5 5 140 288 \n", - "6 6 168 287 \n", - "7 7 196 305 \n", - "8 8 224 299 \n", - "9 9 252 284 \n", + " global_edge_index pre_global_comp_index post_global_comp_index \\\n", + "0 0 0 287 \n", + "1 1 28 289 \n", + "2 2 56 289 \n", + "3 3 84 301 \n", + "4 4 112 281 \n", + "5 5 140 295 \n", + "6 6 168 289 \n", + "7 7 196 290 \n", + "8 8 224 303 \n", + "9 9 252 280 \n", "10 10 23 280 \n", "\n", " type type_ind pre_locs post_locs IonotropicSynapse_gS \\\n", - "0 IonotropicSynapse 0 0.125 0.625 0.0001 \n", - "1 IonotropicSynapse 0 0.125 0.625 0.0001 \n", - "2 IonotropicSynapse 0 0.125 0.625 0.0001 \n", - "3 IonotropicSynapse 0 0.125 0.875 0.0001 \n", - "4 IonotropicSynapse 0 0.125 0.625 0.0001 \n", - "5 IonotropicSynapse 0 0.125 0.125 0.0001 \n", - "6 IonotropicSynapse 0 0.125 0.875 0.0001 \n", - "7 IonotropicSynapse 0 0.125 0.375 0.0001 \n", + "0 IonotropicSynapse 0 0.125 0.875 0.0001 \n", + "1 IonotropicSynapse 0 0.125 0.375 0.0001 \n", + "2 IonotropicSynapse 0 0.125 0.375 0.0001 \n", + "3 IonotropicSynapse 0 0.125 0.375 0.0001 \n", + "4 IonotropicSynapse 0 0.125 0.375 0.0001 \n", + "5 IonotropicSynapse 0 0.125 0.875 0.0001 \n", + "6 IonotropicSynapse 0 0.125 0.375 0.0001 \n", + "7 IonotropicSynapse 0 0.125 0.625 0.0001 \n", "8 IonotropicSynapse 0 0.125 0.875 0.0001 \n", "9 IonotropicSynapse 0 0.125 0.125 0.0001 \n", "10 IonotropicSynapse 0 0.875 0.125 0.0001 \n", @@ -532,7 +534,7 @@ "10 0 " ] }, - "execution_count": 9, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -543,7 +545,7 @@ }, { "cell_type": "markdown", - "id": "9590bd7b", + "id": "4c57d8f2", "metadata": {}, "source": [ "To modify a parameter of all synapses you can again use `.set()`:" @@ -551,8 +553,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "a4578607", + "execution_count": 13, + "id": "7d295f14", "metadata": {}, "outputs": [], "source": [ @@ -561,7 +563,7 @@ }, { "cell_type": "markdown", - "id": "1f63ec83", + "id": "d74dafa0", "metadata": {}, "source": [ "To modify individual syanptic parameters, use the `.select()` method. Below, we change the values of the first two synapses:" @@ -569,8 +571,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "b36c9d54", + "execution_count": 14, + "id": "f7ba4d28", "metadata": {}, "outputs": [], "source": [ @@ -579,7 +581,7 @@ }, { "cell_type": "markdown", - "id": "22f89733", + "id": "a9d4f9fc", "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)." @@ -587,7 +589,7 @@ }, { "cell_type": "markdown", - "id": "85713b1f", + "id": "fa3826a7", "metadata": {}, "source": [ "### Stimulating, recording, and simulating the network" @@ -595,7 +597,7 @@ }, { "cell_type": "markdown", - "id": "42fcf594", + "id": "4cdff397", "metadata": {}, "source": [ "We will now set up a simulation of the network. This works exactly as it does for single neurons:" @@ -603,8 +605,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "1899674f", + "execution_count": 15, + "id": "70d3f34a", "metadata": {}, "outputs": [], "source": [ @@ -620,8 +622,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "id": "c8613e12", + "execution_count": 16, + "id": "4eeb188d", "metadata": {}, "outputs": [], "source": [ @@ -630,7 +632,7 @@ }, { "cell_type": "markdown", - "id": "35d1a94b", + "id": "544cdeef", "metadata": {}, "source": [ "As a simple example, we insert sodium, potassium, and leak into every compartment of every cell of the network." @@ -638,8 +640,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "id": "08b9e276", + "execution_count": 17, + "id": "22a17a74", "metadata": {}, "outputs": [], "source": [ @@ -650,7 +652,7 @@ }, { "cell_type": "markdown", - "id": "75991e3f", + "id": "e969f523", "metadata": {}, "source": [ "We stimulate every neuron in the input layer and record the voltage from the output neuron:" @@ -658,8 +660,8 @@ }, { "cell_type": "code", - "execution_count": 15, - "id": "399c0a74", + "execution_count": 18, + "id": "e66b5f02", "metadata": {}, "outputs": [ { @@ -692,7 +694,7 @@ }, { "cell_type": "markdown", - "id": "0199e07f", + "id": "e8ae006c", "metadata": {}, "source": [ "Finally, we can again run the network simulation and plot the result:" @@ -700,8 +702,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "id": "821e6863", + "execution_count": 19, + "id": "71146fc4", "metadata": {}, "outputs": [], "source": [ @@ -710,13 +712,13 @@ }, { "cell_type": "code", - "execution_count": 17, - "id": "021edd8c", + "execution_count": 20, + "id": "b6905f50", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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EDkCz5f/CCD1OpvRfOXAO1rTFV155BcOHD0dMTAwKCwtx4MABrZukOL0mRc0ye+jCSVEZIl1/K0e+81J8lo5ekfM7BOIj3DxYbvERdhAGPYcEPmpVcMs7QT64mlkuwvxvQPgdVjc6FgZHwjaoUaAsGINypeiWLVuwatUqrF27Ft999x2mTJmCBQsWoKGhQeumKUqgSdFovT9hpOuzXOT10HnLRYG0RX/vX8YIXXB+o16nWDqhUNDViNA9K2B9+eC8Z69KlovvugAEGTZqR+i+SJi2AQB6nNraLr4ObxBlubz00ktYtmwZ7rvvPowfPx6vvfYaLBYLNm7cqHXTFEU8KWrxpls5XKRXoapw8GWjyCuMPkHUK2SJ+CYtE2XsiCgOwfkBKNJpAD77w3MtneIeujATSP0sF/97qua1hbgEHUuMUcdn22jto4vXmqiBpoJut9tx6NAhFBUV8b/T6XQoKipCcXGx5N/YbDZYrVa/n8sRt2g4RrNcgOgmRmlhKI/XLeOkqNBDV8JycfW2dJSI0Knfm6DQBB6N0HWc57OlHYdSEbpTIOgGlWu5OESWi2/5v3ZZLhzXf3z0QWe5NDU1weVyISsry+/3WVlZqKurk/ybdevWISkpif/Jy8tTo6myI/6wTQYdb790RbHBrVB4E8zypy2a9b5Jyw6ZCn8RQhRrN8Uh6OgA8FaI3Mv/xR1HvOI57wLPXpCHrc7Sf9/CIgB8yqTqEbrYy1ex4mQwxO1SA80tl3BZs2YN2tra+J/q6mqtmxQRToneW456LlJpi3IsALK7PG0SRtByFf4S1lkxCyZF5RRBhzhCV2jBD7VcTAqPBMTXAzSotigoOQD4MrXUrqMifpboSEHr1aJiW1UNDH0fohzp6enQ6/Wor6/3+319fT2ys7Ml/8ZsNsNsNku+djnhJr39NYvJAGuPMyrLxSFIW1RkYZFBB7PBkytuc7rR3uPgFxpFfG5BlCmcFFViJapJlGKn1KQojcqU9tBpxUPOa/Gom4dO36t3UlQrD12UAtxfVosOujx0k8mE6dOnY8eOHfzv3G43duzYgVmzZmnYMuVROkL3WCMyRugiQZQzF104CWySOTuHv4ZoAi/erMykaCDLpUOhyo58DrrOX1QdLgKbwlkevmvTe6rNoh7asehlXtMRLVp46JpG6ACwatUq3Hvvvbjqqqswc+ZM/P73v0dnZyfuu+8+rZumKOJJUQCylP60Syz9tzvd6HG4EGPUB/vToAjz0AEgMcaAxnabPJUcBbsseSYS1bRc5BVa4SIfAII0QmUtF35iUjC53mlzwWyI/DPv89pu/wjdotEmF75ImKZP9i8PfVAJ+pIlS9DY2IinnnoKdXV1uPLKK7F169ZeE6UDDakPW44SusK0xXjBApr2HmdUgi7MQgF8aX9y5KILF0MBEKRFyp+26Gu/MvnhDtFIQPEyA1RUvd8jgzfHvsfhRqfNye/OpMi1RZ2JmouaKISQXqNdur9pl8aC7hR9NmqguaADwMqVK7Fy5Uqtm6Eq0oIe/VBRKLx6by2RDpsT7T0OvtZ4ROcVR+ix8vnctl454vJZRRSHaLJSOQ9dOkKn1SPljtbEIwLPNY3ocdgUj1D5PHQdjdBpZKye1SFMsuI9dH6kq53lQoivBMSgSVsczPCbRHNSEbo8lgsAwSIdeba246NoGX1uh2DRkufc8kfovfLQFaq4KJ4opB46oEzkKp6EBdRLHxTnocdrEBnTKFjYDks/yEMXLvgysHroAx+pGXA5SujaRfaFXCsihSUFAGUmRU2iCF2RlagGr/gobLnQ92I26PnPQgnbxSnyjwH1qh46+0GWizBt05cPr912eBThgi8V9ZwJulYE89CjEXRftCu2L6KLdnt76PKtFhWWtvWc29NZdNnl21w50KRou8yTomLLBRB0HgoInXj5PQDV9vcUZ7n4tqFTT0j9BF1UJEzLtEU3YRH6oEJcbRGQZ6gorFoIQLYUwN6Wi3yTor1HFT6bQi4RdIg7JIU2npC2QJRZleq5Xu8CUPEqVHgEhJZL7whdrX1F/SwX3kPXPm1RGKEzD30QIGW5yJKHHmDyUjYPvdekqJzb2+n4/8pdDbHXpKhCS/KFu/hQlNzyTjzyAISWi9IRuv/ogAYkTjfhJ7qVRlzHxdOO6NN/o8XlYoI+qAg+KSqfoMuVMWITRbhyTbYCveusAIK0SJmiWptTpUlRp1TWiYKWi7u35aLWpKizV/lcX1qsWtGxUyIw4pf+9xcPXT09Z4KuFdKTovKlLfYuEyuT5aJghG5SMKoVR7JUZO1Ot6wrKsX3H1A2F13Ks49TaYGPw+1vLxkEIyu1/GtfgbDegZGWC4uE+x1wg6V87mBG0kOnm+xGuMkFISTw5GWU2SiB0hblyHIR+9uA/PXKxQt+hIuu5BRa8bZswmspITAOCUFTL8vFvzgXIOhMVLI7gs0haJm26JR4vtWACbpG+LJcfL/jPfQIH0Snm4DORYknL2WP0GW0RKSiWl+HIY/lQoWPpl16dreRP5ILluWihIfulLSrVIrQg/j3akXo0nNR2tRlF+LbdIMJ+qDANyTzfQSWKMt+Cotc0RoeclkXdpEgCidbo81oEE+KAvIvLrJLeduKbqQh9LR99ePlxh40Qld4UjRoDrw6YiqVVUTTJ7Vc+k/nyNScEAWYoGuGVK3kaPcVFVctBATCGGUKm29hkf9qTpebRD0BJm25yCu24pWigDLL/6WW4ivpoTslLAe1omTJHPgoR5nhIi7MBQjSfx3abRTt0qCOC8AEXTPcomXTABBLPfQIvT+ayWHwVi0E5PPQ6cQhjdBjjL4dlqK1XcTFuQDhwh+ZPHRBnXjfNeSPnKVsCDU8dJPf9dRJ25Pyr9Wsxw4IKz72jtAJiX7T9cjbxSL0QUWwCD1ay0UY6cpVuZCP0L1ZDBzHybb8X6rdcm7OAfRevOS5hvwLcBySaYTKbXIhaTmYlK3wSJGqJqj25hIOCa861qj3bRSt0cSoFqVzASbomiE9Kerz0CPxpcVRNOBvXUTjddtElgsA2TaLtgexKeSquMjfG6OylovUfICSm1xIpi2qnuUikTKp0oSklOXit1G0RrnoUu1SAyboGuETdOGDqOdfi2SlnXgTCsAX6TrdBD2OyFfvSZ1b7ujfX9DlTVsUbnLtu4Zygu73GShUqheQ9rHV2ldUekJS3SwXqTYA2q8W9aUtqntdJugawa/w0/kPlek/I4l6fZkovig6zqSP6pwUmyNw9B+t5RJ8UlTelaL+Ebr829BJjWSULM5F66lIpg7alZ0U5DeJlghKVFspKuHje9qhbT0XFqEPMqQmlHTeDSmAyETS5ugtjBzHyeJHS1su8uSii0vOAgpkuYiKlgmvIaeHHmw+QJmVotTH7m0lAZEvUgvn2rQkMaDFpKh0vjftWJSeRwgE89AHGVJDZSA6G0OcK06J1o92uX3bfAnPLVfFRcnJXJknRaUidCXSCaXmMfgsF7tT9ohZ6nsUY9TxQqKU9UEI0bTsACXQNm9yLnyLBF7QVVz2DzBB1wxHgMjCV5Qq/AdCShiF54w02vVbsGQUeujyLlqSnBSVobQAIF0vRslJUSn7iBD5I2YpUfVMCipbz4R+ZoD/exXaPWrgkrCcAJ+gt8m00jhc6OiFReiDBPFuL5Rotnaj0aFJLx2hRxrtCotXmSRWc0ZvuQSuttjtcPEPRzRIZbkoUXHRZ035rmM2+HL21ai/Dig/MeoQlIeVzIFXOW1RLJxJsfKMHiNF6jutBopcrbKyEvfffz9GjBiB2NhYjBw5EmvXroXdbvc7huO4Xj/79u1Tokn9DqnCRoAgQo8gMvXliuv9fp8YxTkBn0jpdZxfB0Ttodau6B4aOuHqv/TfwOcSRxtlud0+e8Ck9NJ/CUHnOE4wMSr3Dkm9RzeAwMtWyEP2W5UsuLZFbcsloHUp/0bj4RBotKw0hr4PCZ9Tp07B7Xbj9ddfx6hRo3D8+HEsW7YMnZ2deOGFF/yO3b59OyZMmMD/Oy0tTYkm9TscAWbBfTZGJBF6b1sBAFIsHuFt6bSFfU7AN9kq9uZT40wAgOYOe6+/CQe6ms8iqKdt0OuQYjGhpdOO5g470uPNEZ9faA8IOzsaxbV1Rdd+qWuJH+R4swGtXQ75N9QIEBgoPTlpF6xK1knUkVErXTDQikzfZ6ut5SJ+FpVGEUFfuHAhFi5cyP+7oKAAZWVlePXVV3sJelpaGrKzs5VoRr/GGWCoHI2NIV7NSUlP8IhhU4TCKzXRB4AX2aaOyDoKCk0tEwo6AKTFUUG3AUiI+Py2ANFkmrdDauq0gxAiS91qX+fn/16UWv5vl8hyAYT16pWyXKRHBnLPffRFQOtSxnr9kaBVhK7a1dra2pCamtrr94sXL0ZmZiauvfZafPzxx32ex2azwWq1+v1cjkitsgOi2wmoRyJXHIheeKUWFQFARoJHEBtlEnS6wQclVSC40UA7JI7zj2TT4j3ntzvdsk3iBYrQlSrQFagTT/WOyi5Fee8CEeg7kWLx3FNrj4OfsFQSXy58gAhdIw9dquqmGqgi6OXl5Xj55ZfxwAMP8L+Lj4/Hiy++iL///e/497//jWuvvRY333xzn6K+bt06JCUl8T95eXlKN18RHAHSrRKiSAWkohQnEsb0+OisERpVxpnF5/V0FO09Tr4ziYQeCctFeP7mKDsMYYYL51c7x8DvsNMSpW1EkVqABShXz4XaVbGieZMUb2fYIqOdJCRQBJrs7UgIUUdMfUFMgHkjzSN0fR9HyktYgv7EE09ITmQKf06dOuX3NzU1NVi4cCF+8IMfYNmyZfzv09PTsWrVKhQWFmLGjBlYv349fvzjH2PDhg1B27BmzRq0tbXxP9XV1eG8hX5DwAg9ilTAzj6EN9IInZ43XnTepFgjH4E0RxEJ0uqSYlFKi7IjokhlnvDXiPPemwjnF8QE9NBlLmVAoRZPjOjepXojZaUi9EAesVGv40sdtCh0bSH0s40xSlsuWqctqh2hh+Whr169GkuXLg16TEFBAf//Fy9exLx58zB79mz88Y9/7PP8hYWF2LZtW9BjzGYzzObIJ8j6C4HSzaLZHJkKI00do1BhjFTQ+QhdFPlzHIe0ODPqrD1oardhSHJsROf3WS5iD90boUcptnTTbbHoAZ57U9PaLWOELu2hp0Y5MR0IGqGLBY2P0BUS1WCWQkqcCe02J1oVGh0I6XFIf7ZJsep6+WKksp3UICxBz8jIQEZGRkjH1tTUYN68eZg+fTo2bdoEXQg1DUpKSpCTkxNOky5bpHZ7AaJLMaS7xFh6WS4eYbzU5YDT5e41gdQXnd7ziiN/AMhI8Ap6FLZId6BJUb4jik4YaIdEfWwhqTILXyBvmR8ltcsrcoEEjb6vSwqJarBOMsVixPkWz/dNaXoCZGAJJ0XdbqL63p6BJo2VRpEsl5qaGlx33XXIz8/HCy+8gMbGRv41mtHy1ltvwWQyYerUqQCA999/Hxs3bsSf//xnJZrU7wiUP0s9yNbu8B/EQNZIisUEHQe4iUe4MhNjwjovzZ0WR/6Az5+PVNDtTjffuVmMgbz/6KJaOhFJbQ8hclouDpebj1zjxPMB3kyjaCeQxXQHEHQ6OalUhB4oMwkAkhW2e4T4FoxJe+iEeEouJEp89oq2ayClLW7btg3l5eUoLy/H0KFD/V4T1uR+9tlnUVVVBYPBgHHjxmHLli24/fbblWhSv6MnkPcpyO0ON5WOCrpFJLx6HYfUOBOaOuxo6ohE0ANH6D5/PrKHt1uQXdLLcony3BQ+QpdoPx0FyGG5CBfTiO9VhkwpnmLo90g8/+CL0JWJkrsd3u+aKfCoR6nRgZBAz1GMUQ+zQQeb0422Lof6gi5RKE8NFLna0qVLQQiR/KHce++9KC0tRWdnJ9ra2rB///5BI+YA0OOU9j7pw+B0k7BtF7qYI5jw1rf3hN3WQJE/AGQneTqH2rbusM8LeKInwDNSEX/5sxI856639kS1OUc7P1ncO5rkO1AZoknacZgMul5DbX4tQLvcgt635aJE+mDwCN2bMqmK5SKdVQT41hnI8dmGCw1UpJ5FJWG1XDQikAcZY9TzkWS4k4G81y0RNeV6JywvtoYvvIGyZwDwE6EXLkUm6HRYTofpQrKSPCJoc7qjsg54y8XcO0qjkXNDBB2dGHr/pTq+dMF8QDSdkxCXm/CdiPiaSqcPBpr3AJTPsBHS45SO0AFfJxqtZRcJXQHSSZWGCboGuAU7Ekl9EVPjI4ssaFYBfZiFDIlC0Gk7aOaE33lTPOetiVTQvW1OlRB0s0GPDO9DebE1csHtDDIpmpPsHWFEcX5KIHEFfCMku8st2+pN4apT8Xsz6nX8IjW5M2uAwIvBACBZRcvFN3qUyGCSqTRFJHQFGRUqCRN0DRAuRZfqwSP5Ijpdbn6Imyaz8NIIJyNe4rzejqKmtTuiyJO2WaoTAgQjiwgtHcBXF0fq4cpN8p0/2siZLgaT6jiEIy+5fHT6vkx6XdAItUFmmwcQjNqCROhqWB3BRl90DkbuiehQCNbhKQkTdA0QrqqUjNAjyL+mKwI5Ttq+EApvuNBJyTSJAllUcLvsroiqLtJRRYpEmwEg1+vRRzKyoFBhofdVCJ0D6HG4o7Ym6HWk7hMAfrTRKJPA0kVKdDGamGzv5HeDVX5Bo9G31KgtK1E+G6svaKcm1YnKtTAtEuiaEKkOT0mYoGtAN18ulpMsgB/JUn16bKrFJHlOGqFHYl3QCF2q4mGMUc//PhIf/VKn54FMiesjQo9C0KmAUkEVEmPU8yOaaGwdwHefpEZIwuvXW+UROtoBBcrgoIJeJ9P1hAQbWWUl0slsm2zzBYGgE97xEoKeLtPCtEigdphUFpCSMEHXgEBL3SmRLHbhBT2AmNAIvc7aw+fAh9pWWiMmXcJyAXydxYVLXSGfl1Jn9Qh1ZoJ0KqXPcolclBp5y0g6co42U4fS0tnHZ5AS3QSyGNpRpUt0VAD49NS6KO5dIOiEp9TcB+247M7oRz3BcAsmhaUi9PQE7SJ0OloNFKgoBRN0DWjzpiMmBfCN0yLIWaYRbE6A5fcZ8WaYDDq43CSsSPR8i0ekk2KNfFkCMcPTLACAs02dIZ+XQsWNip0Y2hFVt4TfWVAarYEjdADISYq+0wCElou0oA+VYbQhpCHIyAMAshW0Pi51Bc5OijHq+ci9XgG7h9LSZQchHptRyrLjF42p7KG73cQ32R+gc1cKJugaQOu00BKfYjIimMyiwjssVVoYdToOI9LiAAAVjR0hn7eq2XPefK9oSzEqIz7s81LoJO3QAIJekOFp87nGzoiG7+09Dn5YTr1dMUOSo/fpAV8kHGgkkBvFPIYUNELPDCToScpF6H1FoMI1BEpB5wZSLSbJJfZylY4Il9ZuB2jqf6C5IaVggq4B1j68z5wIHkSfoAcR3szwhbfSG3WHdN6G8ATd4XLzEXpeivT589Ms4DiPVxpJtkJlk+e+pMebAo4whnk7uqrm8EcYQqpagt+raFM8xTR4xTJQhJ4p8LLlpMfh4kcjgayyzMTwg5Jw4a20AO+fdqwtnbawbMZooWmiiTEG1Wu5MEHXABrdBIrQswXeZ6hR6en6dgDAiPT4gMeM9Ea75WEI7/GLng1ErshJDHiMr6MIL4oub+iA3eVGgtkQMEI3G/T8a2cbwxfcs02e91oQ5L6MSLdEfH6Kw+Xmrax8bwchJtoUTzGV3g4oUGfIZ7m098At42pR2gnHmw389oZifBOjSkbowTu0tHgzDDoObqJu6mJzkKwwpWGCrgH0Sx5oqEwfBrsrtBWSPQ4XznhFeuKQwMI70iu84Qj60QutAIDJQ5MCHpOfFge9jkOHzRlWNHiCdha5iUFr1lAxPheBR1/qvcaorMCCPpyP0LsiFtpzTZ1wuQksJn3AzzXaFE8xdM5iZIb0e8tMMEOv4+BwEVkj5Wrv5PfQlNiAnxufuqigoFd7Oxa6lkCMXsfxz1K0GUwRtSs5vJpJcsAEXQNoGllWkvQHbjLo+FTA2hBsl1N17XC5CdLiTHxUJsXIML3uBmsPqpq7wHHApCGBBd1k0CHfazOE01kcqmoBEPzcADAi3SO4ZyPw6L87fwkAMDUvOeAxeakW6HUcuh2uiO2JkupWAMDEIUkBS7V6Ujw9nmq0PnqDtQetXQ5wnO/+iDHodfyo4HwUk8piyus9n8PwACMRwBeUKJEySaEW38jMwO2IxL6MFmpTBrs/SsEEXQNohB5MfHP4VLq+v4hflzcBAKYOSwka6Y7MiAfHeXKIQ1ncsuNUAwBgytBkyWwGIWOzPZs4l9a29XlewFN1c+9pT7uvHZ0e9FhqFVWEaYl02104esHTnmn5KQGPM+p1vK0TySgAAA6fbwUAXBmk4wCAoV57JFqBPVTl6ajGZSf2qlIphPr5cgr6sRrPPQ02GuQzhxSMjM80eGzGQCMUQL6U1HCg36FgiQRKwQRdA2jdkGCC7stQ6PuLuOe0p978f4wNvvlIrEnPf/mP1bT2ed7PjtcBAIquyOzz2EleS+bIhdAE/ciFNtS0dsNs0OHqEWlBjx3n9e9P1oa3IfjusgbYnG4MTYlFQYAolkKjqcoIJkY9nZPnM5gxvPdG6EJo1k4kow0h+8+1eK8XuKMCPKMPQD5BJ4TwncnkockBj1OiIxHS2mXnbcZJQexAanOFEhjJBR2tTcgNPvJUAiboKmN3ulHl/ZIXBIksaITeV250Q3sP/4BdN6bv3aSmeB/Ckurgwnu+uQtfnvGI1Pcm54Z8Xuq598WWb88DAG6clBM0wgQ8E7Ic53kow8kp/vjIRf4afdWVL4hgwphy1Ns5xRr1mNPnaMM3gRwphBBsK60HAMweGfx6VFijyeMXcqahAzWt3TAZdEE7rzxv+mxbtwNtCpTRPXCuBYR4PrdAmTaAf4KBGtS19aCmtRs6ru/RmhIwQVeZymbP5FmC2RAwLxoQRDjNwR/ED76rgctNMHVYMh+NBWNKnjeS9kYRgXh1TwUIAeaMTg/o0QqZ6PXBq1u6+yxXerG1G/88VAMA+NGMvD7PHW828G2gE6l9UdXcic9PeEYYt04b0ufxNIvnxMXQRhhC/rqvCgAwf0KWZG0eIVTQo4nQ951tQU1rN+JMelzXx6hM7kj54xJPJzl7ZFrQjthiMvDzQNURrCDui0+P1QIArh0VvEOjE5PRFHcLh11lHpty4pAk1WuhA0zQVeeY15IYk50QNGqkqW/BPF2Xm2Dzt9UAgB9e1bcwAr5I+vD5SwE3PihvaMd7Bz3nffj60SGdNynWiNHeLJoDXjsgEM9vPQW7y42rC1Ixc0Rwi4Iy0Tt8PV4TmuC+tO003ASYNzYD47IDe72UCbmeY0ovWsPKdKlq7sRHJZ7O6Z5Zw/s8flSmbz4g0oyaP+6tAADcPHVInx0I9XGr+ggMQqHH4eK/b0tC+L7RRW5y2y5tXQ5s9XbWt0wN3llne718tSL0D77zfBcWTdRmb2Qm6CpzyJt1MT3IJB3gy42uag784H96rBbnmjqRFGvE96f0bYsAHuFKiDHA2uPkvT4hTpcbq/9+FC43QdEVWX16wkLo5ObeM40Bj/n8RB0+LLkIHQc8seiKkLfYm+IdvvbVWQDA3tON+Mh7jVU3jA3p/KMzE2DUc7D2OEOutUIIwbP/KoXDRTB3TEafnykADEv1pXhGkgGy93QjdpU1Qq/j8JM5BX1fzyvoTR02foVypLzx1Tk0ddiQmxSDovFZfV9bIR/99b0V6HG4MS47oU9bQ1jDSFjlVAkOVbXgQGULjHoON08N7XmUGyboKkMFadqw5KDH5aVaoOOATrtLclGE203wyq5yAMB91wyX3FRBCoNeh7ler32XN4tFyAtfnMaR6lYkxBjwzE0TQjonhZ5356kGyej/dH07Vr93BACwbE5BWB7jNaM8E6cHzrXA7gy86u9iazce3VICwBMxB5swE2Iy6DAmy5OpE+oo4J0D57H9ZAOMeg5P3nhFyNehI5ljIU4gUy512rHm/WMAgHtnDQ/JCkuMMfIliE/Vtod1PSGn69vx8s4zAIBfLBwX0gpIJQT9xMU2/OnLswCAR28Y02dAkB5vQoLZAELkm0eQwuFy41cfngAA3DZtKJ/lozZM0FWkqrkT5Q0d0Os4zCoI7v2ZDXp+hr6iobft8tGRGpyqa0e82YCls4eH1Y753ujqH4cuwCFYEv23fVV4bY9nOP/bWybx1w+VWQVpSLYYUW+19YrSKxo7cO/GA+iwOVE4IhWr54cWOVPGZiUgPd6EbocL31ZKR+mtXXbc/9ZBNHfaMSE3EU8sGhfWNaYN80TY+84293ns1+VNePpjzwP8+MJxfNpmKFDb60iIE8iAZzL94c2HUdPajeFpFjx6Q2hWGODLEjpVF16WEKW1y46fvv0dehxuzB2TgcUhjgZpSYVzUUwAC2nqsGHF29/B4SKYPz6L/x4Hg+M4DKfrGCJMSe0LQgie+ugESmutSLEYw/5uywkTdBXZftITEc8cnhqw0qKQ8d4HURwxdtmdeO6zMgDAT+eN7DNHXMzCidlIjzehztqDt/dVwely43+2n8F/f3gcgMc3D/WhFRJj1OPWqUMBAP+z/Qwfpe893YgfvFaM2rYejM6Mx2s/nh72bugcx+EG7wP8vtenFNLUYcPdbxzAyVor0uPNeO3H0/v0l8XQUcDXFcEF/fD5S3jgr4fgcBHcOCkb/3nNiLCuQ+2jI31kGlFcboJHt5TgyzNNiDHq8Mpd0wLWpZHiihxPZxNu2ifgKW5276ZvUd7QgaxEM1764ZSAC6fEjPWOeE7VhTcvIUVzhw1LNx1AZXMXhiTHYt2tk0K26+hIJtI1BsFwuwl+8++TePfAeXAcsOH2KQFLEaiBYoI+fPhwcBzn97N+/Xq/Y44ePYo5c+YgJiYGeXl5eP7555VqTr9guzfVLBT/ERA8+KJI7rXdFaiz9iAvNTZsMQE80f/KeaMAAE9/Uoqrfrsdv9t+GgCwfG4BHi0KPfoT88B/FCDebEBJdSvu+vM+3P3Gftyz8QBaOu2YOCQRm5dfLbnLTSjcPt3TWfzr6EW/lZZHL7Tipv/9Gsdq2pAWZ8I7ywpDyvgRM6sgHTrOk7oYKAtlV1kD7vzTfnTYnLi6IBUv/fDKkAWOQssoHKlu9RshSdHjcOGhvx3Cv4/Vwqjn8PrdV4Wd30wzeErDtFxq27rxg9eKcaS6FSkWI/56f6HkJieBGJ0VD513IVs0pQfONXXi1le/wfEaK1LjTPjL/TPDqpPCC7pMIwVKW7cDK975Dm98dQ4A8MziCSE/20qhaIT+zDPPoLa2lv/52c9+xr9mtVoxf/585Ofn49ChQ9iwYQOefvpp/PGPf1SySZrR3GHDAa9VEMpCHUCYkdLKRzin6qx41WuL/HLRFWFHoZR7Zg3HXYXDAHiKhSVbjNhw+2T88sbQJyqlyEqMwfO3T4ZBx2Hf2RZ8eaYJOg64Z1Y+/vHg7KgKFk0bloKZw1Nhc7rxX+8exrbSevz3h8dwyx++4a2I9x6cxXvh4ZJkMeI/vPMAW7xZPhRCCP609yyWvXUQ3Q4X5o7JwBv3zojo/l+Rk4jUOBPabc6A9hHgKY9755/24YvSepgMOvzvndP49oXD5CHJAIATNW38XqB9cexCG2555RucqmtHerwZf72/MOz7GmPU82JaGsHoAPB03ov/9ytUNXchLzUW7z0wK+jKUCn4xVxN0S3mErLvbDNu/J8v8dnxOhj1HH6/5ErcHUKWk9IomiiZkJCA7Oxsydfefvtt2O12bNy4ESaTCRMmTEBJSQleeuklLF++POA5bTYbbDZfb2+1RvZFUZvPT9TD5SaYkJsYsBqfmCuHJcNk0KGmtRun6toxIj0Oq987AoeL4IbxWVg4UfrehoJOx+G3t0zC8rkFaOqwYUJuUsSdg5gbJ+VgTFY8Pj9RD6Oeww3js0OawOsLjuPwm1sm4tY/fIODVZdw8C8H+de+PyUXz940IWz7ScyPZg7DrrJG/K24CndfnY+hKRacbezA05+U8qtBb506BOtvmxy2bUTR6zj8n3GZ+MehC/j8eJ3k4qB9Z5vxs3cPo7HdhqRYI/50z1Uhp3iKyUuNxZDkWNS0duNAZQvmjQ0cUBBC8NY3lfi/n3pSS0dlxmPT0hkRjXgAz2rJisZOHKluDXpdMV12J575pJRPk5w2LBmv331VRHYGX5biohUuN5HcojFUmjpseH7rKbx38AIAT1ro75Zcyc+/aI2iEfr69euRlpaGqVOnYsOGDXA6fdFBcXEx5s6dC5PJ9wAuWLAAZWVluHTpUsBzrlu3DklJSfxPXl5o+ddaQxdCfG9y6Pmp8WYDH5G9vPMMVr1XghMXrUi2GPHbWyZGFUlT8tPiMD0/VTYxp4zKTMCKeaOwfO5IWcScMiYrAf94aBYWTczGuOwEfG9yDt5ZVoiX75gatZgDwA1XZGHqsGR02l34/stf4dY/fI2il/Zg7+lGmAw6/ObmiXjxh1MiFnMKTTP9x6EL/HZugMdieW7rKdz5p31obLdhdGY8/vnQ7IjFHPB0hHR+YLdEZhOlucOGB/92CE9/Ugq7y40bxmfhnw/OjljMAaCwwNPu4j7mJYTsPd2I+b/bi83fVoPjgJXzRmHLA7Mi9qZHZybAYtKj0+6KaBMWwJPOu/Grc5j3wm5ezH80Iw+fPjyn34g5oGCE/vDDD2PatGlITU3FN998gzVr1qC2thYvvfQSAKCurg4jRvj7v1lZWfxrKSnSN2nNmjVYtWoV/2+r1drvRb25w4ZvKjyFqL43KbwFB8vmFGBbaT0+PeZZSKHXcXjlzmlBlzsPdMZlJ+LVH09X5Nw67/29+439qGjsxCVv0a15YzPw5PfG87Xfo2Xu6HRMyE3EiYtWrHqvBI8UjcGRC634496zfB78rdOG4Dc3T5Rlo+EbJ+XgvYMX8MHhGqy50d+qI4Tgk6O1ePrjE2jptMOo57Bm0RW475rhUQcNdPRx+HwruuzOoO/lUqcdz/67lJ/0HpIciw23T8bsPlaD9oVex2HikCQcONeCkvOtYVlHhBDsKmvA+s9O4XS9r0T1rxdPwPT8yDtZpQjrm/LEE0/gueeeC3rMyZMnMW7cOD/RnTx5MkwmEx544AGsW7cOZnPkPqrZbI7q77Xg8xP1cBNPmdhQ7RbKzBGp+OWN4/D/dpQjI8GMZ2+aiGui/IIzgpObHItP/2sOvqlohrXbgSvzksP+3PqC4zj85uaJWPL6Puwq8ywWomQnxuCZmyZg/oTILTUxc0Zn8LbL63vO4r+8E99HL7Tiua2n8HW5J4Iel52ADbdPCTl/vy+Gp1kwLNWC8y1d+OxYHW7zTmwL8ax4Po8XvziNlk47OA5YOns4fj5/rGzL56fnp+DAuRZ8XdGEH4ZQbgLwlF5e/9kpfu1IssWIxxaMxY9mDIvKtlGSsO7W6tWrsXTp0qDHFBRIr14rLCyE0+lEZWUlxo4di+zsbNTX1/sdQ/8dyHe/XNlW6omuF02K7H0tnzsSy+eOlLNJjD4wG/Rheb6RMHVYCt5ZVogXvijDmfoO5KVacNOVuVgyI0+WqFyIXsfh5wvG4NEtR/C77adxsKoFLZ12vjaOSa/DT+eNxE+vGxW1nSSE4zj8YPpQvLjtNN4qrsQtU4fwWUGEEHxV3oTf/vskTtV5MnDGZiVg/W2TMFVmG6Poiky8ursCO081wO50B32P5Q0d2PD5KXx+wqNHJoMO980ejoeuCz9FWG3C+tZkZGQgIyP8WXYAKCkpgU6nQ2am5yGZNWsWnnzySTgcDhiNnpzabdu2YezYsQHtlssRm9OFfWc9PbzSAsG4/LhqeCo2L5+lyrVuvnIITtW24/W9Z/HlGY8FaNBxWDwlF4/eMCYqrzwYS2bm4bU9FTh6oQ2//uQElswYhuM1bXj7wHm+SFxSrBGPFo3GXVfnK7IP59S8FGQkmNHYbsNnx2tx05W9a8CUN7TjlV0V+KikBm4C6DhPquwjRWPCXmSnFRyRY3NDEcXFxdi/fz/mzZuHhIQEFBcX49FHH8WiRYvw1ltvAQDa2towduxYzJ8/H48//jiOHz+O//zP/8Tvfve7oFkuYqxWK5KSktDW1obExL6LMKlNcUUz7vjTPqTHm/Htk9fLMpHJYERD6UUrDla1ICnWiGtGpYeVWx4pW749j8f/eazX780GHe4sHIaH/8/oiNcnhMrLO87gxW2nkZ9mwccrrkWSxQhCCPadbcFfiiux9UQdqBoWXZGFXywcG3EKbLREqmuKTIqazWZs3rwZTz/9NGw2G0aMGIFHH33Uz1dPSkrCF198gRUrVmD69OlIT0/HU089FZaYXw7QmuJzRqczMWf0C8bnJmJ8rrrBz5IZw5AYY8QfdlegprUbeSmxKLoiC3cUDlOlQwGApdcMx7sHzqOquQsLfr8XE4ck4cTFNr/NLxZMyMLKeaNlm0NQG0UidDXp7xH6wt/vxam6drz0wym4dVrvCSEGg6EepRet+Mlb3/ptHBNn0mPxlbm4d/bwkEotq0G/itAHOxcudWFbaT0+PnIRp+raoeMQ0Qo/BoMhL+NzE7F99X9g7+lGNHXYkZ9mwfT8FNknobViYLwLjSGE4FRdO744UY8vSuv8dtXRccAjRWOiWvLOYDDkw2IyYKFGG1AoDRP0KLjY2o0PS2rwwXc1/Ia1gEfErxqeivnjs/D9KbnICrIZNIPBYMgFE/QwcbkJdp5qwFvfVOLriiZ+Vtxs8GwcccP4LFw/LpNF5AwGQ3WYoIdIj8OFdw+cx5vfVPrtzzhzRCpumzYEiyblIDGMGtUMBoMhN0zQ+8DhcmPLt9V4eecZ1Fs9VR4TYwy4o3AYflyYr9hiDAaDwQgXJuhB+KaiCf/9wXF+66ohybF48LqRuG3akAEzK85gMAYOTJUkaOt24JlPSvHP7zxlMtPjTVg5bxTuKBwGs0HeMrMMBoMhF0zQRZRUt2LlO9/hwqVucBzw48J8PLZwLPPHGQxGv4cJuoC/H6zGLz84BoeLIC81Fr9fMhXT8wdOoTAGgzGwYYIOz8KgP+yuwIbPywAACydk47nbJyMplkXlDAbj8oEJOoD/t6Oc3/X+oetG4hcLxrJCWgwG47Jj0Av6X4oreTF/8sYrsGyu9AYdDAaD0d9RdJPo/s63lS349SelAIBHikYzMWcwGJc1g1bQu+xOPLK5BC43weIpufiv60dr3SQGg8GIikEr6G98eQ41rd0YkhyLdbdOYp45g8G47BmUgu52E7xz4DwA4LEF8u0szmAwGFoyKAX9YNUl1Lb1IDHGgIUTs7VuDoPBYMjCoAxNZwxPwT8fmoXqlm7EGNlSfgaDMTAYlILOcRym56dier7WLWEwGAz5UMRy2b17NziOk/z59ttvAQCVlZWSr+/bt0+JJjEYDMaAR5EIffbs2aitrfX73a9+9Svs2LEDV111ld/vt2/fjgkTJvD/TktLU6JJDAaDMeBRRNBNJhOys32TjQ6HAx999BF+9rOf9UoPTEtL8zuWwWAwGJGhiof+8ccfo7m5Gffdd1+v1xYvXoyenh6MGTMGv/jFL7B48eKg57LZbLDZbPy/29raAABWq1XeRjMYDIZGUD0jdNPiUCEqsGjRIrJo0SK/3zU2NpIXX3yR7Nu3jxw4cIA8/vjjhOM48tFHHwU919q1awkA9sN+2A/7GfA/1dXVYWktR0joXcATTzyB5557LugxJ0+exLhx4/h/X7hwAfn5+Xjvvfdw2223Bf3be+65B+fOncOXX34Z8BhxhO52u9HS0oK0tLSwVntarVbk5eWhuroaiYmJIf/dQIfdl8CweyMNuy+BifTeEELQ3t6O3Nxc6HSh566EZbmsXr0aS5cuDXpMQYF/gatNmzYhLS2tTysFAAoLC7Ft27agx5jNZpjNZr/fJScn93nuQCQmJrIvoQTsvgSG3Rtp2H0JTCT3JikpKezrhCXoGRkZyMjICPl4Qgg2bdqEe+65B0Zj35tFlJSUICcnJ5wmMRgMBsOLopOiO3fuxLlz5/CTn/yk12tvvfUWTCYTpk6dCgB4//33sXHjRvz5z39WskkMBoMxYFFU0N944w3Mnj3bz1MX8uyzz6KqqgoGgwHjxo3Dli1bcPvttyvZJB6z2Yy1a9f2sm8GO+y+BIbdG2nYfQmM2vcmrElRBoPBYPRfBmW1RQaDwRiIMEFnMBiMAQITdAaDwRggMEFnMBiMAQITdAaDwRggDEpBf+WVVzB8+HDExMSgsLAQBw4c0LpJivL000/3qjsvTCXt6enBihUrkJaWhvj4eNx2222or6/3O8f58+fxve99DxaLBZmZmXjsscfgdDrVfitRs3fvXnz/+99Hbm4uOI7Dhx9+6Pc6IQRPPfUUcnJyEBsbi6KiIpw5c8bvmJaWFtx1111ITExEcnIy7r//fnR0dPgdc/ToUcyZMwcxMTHIy8vD888/r/Rbi4q+7svSpUt7fYcWLlzod8xAvC/r1q3DjBkzkJCQgMzMTNx8880oKyvzO0au52f37t2YNm0azGYzRo0ahTfffDP8BodV+WUAsHnzZmIymcjGjRvJiRMnyLJly0hycjKpr6/XummKsXbtWjJhwgRSW1vL/zQ2NvKvP/jggyQvL4/s2LGDHDx4kFx99dVk9uzZ/OtOp5NMnDiRFBUVkcOHD5NPP/2UpKenkzVr1mjxdqLi008/JU8++SR5//33CQDywQcf+L2+fv16kpSURD788ENy5MgRsnjxYjJixAjS3d3NH7Nw4UIyZcoUsm/fPvLll1+SUaNGkTvuuIN/va2tjWRlZZG77rqLHD9+nLz77rskNjaWvP7662q9zbDp677ce++9ZOHChX7foZaWFr9jBuJ9WbBgAdm0aRM5fvw4KSkpITfeeCMZNmwY6ejo4I+R4/k5e/YssVgsZNWqVaS0tJS8/PLLRK/Xk61bt4bV3kEn6DNnziQrVqzg/+1yuUhubi5Zt26dhq1SlrVr15IpU6ZIvtba2kqMRiP5+9//zv/u5MmTBAApLi4mhHgedp1OR+rq6vhjXn31VZKYmEhsNpuibVcSsXC53W6SnZ1NNmzYwP+utbWVmM1m8u677xJCCCktLSUAyLfffssf89lnnxGO40hNTQ0hhJA//OEPJCUlxe/ePP7442Ts2LEKvyN5CCToN910U8C/GQz3hRBCGhoaCACyZ88eQoh8z88vfvELMmHCBL9rLVmyhCxYsCCs9g0qy8Vut+PQoUMoKirif6fT6VBUVITi4mINW6Y8Z86cQW5uLgoKCnDXXXfh/PnzAIBDhw7B4XD43ZNx48Zh2LBh/D0pLi7GpEmTkJWVxR+zYMECWK1WnDhxQt03oiDnzp1DXV2d371ISkpCYWGh371ITk7223mrqKgIOp0O+/fv54+ZO3cuTCYTf8yCBQtQVlaGS5cuqfRu5Gf37t3IzMzE2LFj8dBDD6G5uZl/bbDcF7r/QmpqKgD5np/i4mK/c9BjwtWlQSXoTU1NcLlcfjcWALKyslBXV6dRq5SnsLAQb775JrZu3YpXX30V586dw5w5c9De3o66ujqYTKZeFSuF96Surk7yntHXBgr0vQT7ftTV1SEzM9PvdYPBgNTU1AF9vxYuXIi//OUv2LFjB5577jns2bMHixYtgsvlAjA47ovb7cYjjzyCa665BhMnTgQA2Z6fQMdYrVZ0d3eH3EZVdixiaMuiRYv4/588eTIKCwv5GvWxsbEatoxxufCjH/2I//9JkyZh8uTJGDlyJHbv3o3rr79ew5apx4oVK3D8+HF89dVXWjclIIMqQk9PT4der+81A11fXz+o9jVNTk7GmDFjUF5ejuzsbNjtdrS2tvodI7wn2dnZkveMvjZQoO8l2PcjOzsbDQ0Nfq87nU60tLQMqvtVUFCA9PR0lJeXAxj492XlypX417/+hV27dmHo0KH87+V6fgIdk5iYGFbQNagE3WQyYfr06dixYwf/O7fbjR07dmDWrFkatkxdOjo6UFFRgZycHEyfPh1Go9HvnpSVleH8+fP8PZk1axaOHTvm98Bu27YNiYmJGD9+vOrtV4oRI0YgOzvb715YrVbs37/f7160trbi0KFD/DE7d+6E2+1GYWEhf8zevXvhcDj4Y7Zt24axY8ciJSVFpXejLBcuXEBzczO/f8FAvS+EEKxcuRIffPABdu7ciREjRvi9LtfzM2vWLL9z0GPC1qVIZnovZzZv3kzMZjN58803SWlpKVm+fDlJTk72m4EeaKxevZrs3r2bnDt3jnz99dekqKiIpKenk4aGBkKIJ+1q2LBhZOfOneTgwYNk1qxZZNasWfzf07Sr+fPnk5KSErJ161aSkZFxWaYttre3k8OHD5PDhw8TAOSll14ihw8fJlVVVYQQT9picnIy+eijj8jRo0fJTTfdJJm2OHXqVLJ//37y1VdfkdGjR/ul57W2tpKsrCxy9913k+PHj5PNmzcTi8XSr9Pzgt2X9vZ28vOf/5wUFxeTc+fOke3bt5Np06aR0aNHk56eHv4cA/G+PPTQQyQpKYns3r3bL2Wzq6uLP0aO54emLT722GPk5MmT5JVXXmFpi6Hy8ssvk2HDhhGTyURmzpxJ9u3bp3WTFGXJkiUkJyeHmEwmMmTIELJkyRJSXl7Ov97d3U1++tOfkpSUFGKxWMgtt9xCamtr/c5RWVlJFi1aRGJjY0l6ejpZvXo1cTgcar+VqNm1a5fkZrz33nsvIcSTuvirX/2KZGVlEbPZTK6//npSVlbmd47m5mZyxx13kPj4eJKYmEjuu+8+0t7e7nfMkSNHyLXXXkvMZjMZMmQIWb9+vVpvMSKC3Zeuri4yf/58kpGRQYxGI8nPzyfLli3rFQQNxPsidU8AkE2bNvHHyPX87Nq1i1x55ZXEZDKRgoICv2uECquHzmAwGAOEQeWhMxgMxkCGCTqDwWAMEJigMxgMxgCBCTqDwWAMEJigMxgMxgCBCTqDwWAMEJigMxgMxgCBCTqDwWAMEJigMxgMxgCBCTqDwWAMEJigMxgMxgDh/wPG5j5/zs0f7QAAAABJRU5ErkJggg==\n", 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" ] @@ -732,7 +734,7 @@ }, { "cell_type": "markdown", - "id": "66e0c675", + "id": "57faa9ca", "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)." diff --git a/docs/tutorials/07_gradient_descent.ipynb b/docs/tutorials/07_gradient_descent.ipynb index baad3c6f..232cb980 100644 --- a/docs/tutorials/07_gradient_descent.ipynb +++ b/docs/tutorials/07_gradient_descent.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "7b2b1351", + "id": "73f14195", "metadata": {}, "source": [ "# Training biophysical models\n", @@ -77,7 +77,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "b414dd72", + "id": "1325e915", "metadata": {}, "outputs": [], "source": [ @@ -99,7 +99,7 @@ }, { "cell_type": "markdown", - "id": "b41aa1e5", + "id": "a54c3936", "metadata": {}, "source": [ "First, we define a network as you saw in the [previous tutorial](https://jaxley.readthedocs.io/en/latest/tutorials/01_morph_neurons.html):" @@ -108,7 +108,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "4ca62f3b", + "id": "c26b3245", "metadata": {}, "outputs": [], "source": [ @@ -133,7 +133,7 @@ }, { "cell_type": "markdown", - "id": "d7a10185", + "id": "e84fdcd7", "metadata": {}, "source": [ "This network consists of three neurons arranged in two layers:" @@ -141,13 +141,13 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "886cea53", + "execution_count": 4, + "id": "35d75f8a", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -159,13 +159,14 @@ "source": [ "net.compute_xyz()\n", "net.rotate(180)\n", + "net.arrange_in_layers(layers=[2, 1], within_layer_offset=100.0, between_layer_offset=100.0)\n", "fig, ax = plt.subplots(1, 1, figsize=(3, 2))\n", - "_ = net.vis(ax=ax, detail=\"full\", layers=[2, 1], layer_kwargs={\"within_layer_offset\": 100.0, \"between_layer_offset\": 100.0}) " + "_ = net.vis(ax=ax, detail=\"full\")" ] }, { "cell_type": "markdown", - "id": "8048a833", + "id": "0b84f9eb", "metadata": {}, "source": [ "We consider the last neuron as the output neuron and record the voltage from there:" @@ -173,8 +174,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "f4e23c03", + "execution_count": 5, + "id": "9890c8d6", "metadata": {}, "outputs": [ { @@ -196,7 +197,7 @@ }, { "cell_type": "markdown", - "id": "c21f1595", + "id": "045e0688", "metadata": {}, "source": [ "### Defining a dataset" @@ -204,7 +205,7 @@ }, { "cell_type": "markdown", - "id": "673697b7", + "id": "278c59ec", "metadata": {}, "source": [ "We will train this biophysical network on a classification task. The inputs will be values and the label is binary:" @@ -212,8 +213,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "8f032363", + "execution_count": 6, + "id": "65aeb105", "metadata": {}, "outputs": [], "source": [ @@ -223,8 +224,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "b1583465", + "execution_count": 7, + "id": "850adfee", "metadata": {}, "outputs": [ { @@ -247,7 +248,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "4f648cd4", + "id": "5ef01f20", "metadata": {}, "outputs": [], "source": [ @@ -256,7 +257,7 @@ }, { "cell_type": "markdown", - "id": "209a3098", + "id": "d6e92442", "metadata": {}, "source": [ "### Defining trainable parameters" @@ -265,7 +266,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "8892c796", + "id": "72bd9124", "metadata": {}, "outputs": [], "source": [ @@ -274,7 +275,7 @@ }, { "cell_type": "markdown", - "id": "28471b94", + "id": "2255ebcd", "metadata": {}, "source": [ "This follows the same API as `.set()` seen in the previous tutorial. If you want to use a single parameter for all `radius`es in the entire network, do:" @@ -283,7 +284,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "8ca68b36", + "id": "691dd053", "metadata": {}, "outputs": [ { @@ -300,7 +301,7 @@ }, { "cell_type": "markdown", - "id": "abfc4125", + "id": "f9c9dab6", "metadata": {}, "source": [ "We can also define parameters for individual compartments. To do this, use the `\"all\"` key. The following defines a separate parameter the sodium conductance for every compartment in the entire network:" @@ -309,7 +310,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "a846bce2", + "id": "34155c98", "metadata": {}, "outputs": [ { @@ -326,7 +327,7 @@ }, { "cell_type": "markdown", - "id": "1e0a9ed6", + "id": "c40f24b0", "metadata": {}, "source": [ "### Making synaptic parameters trainable" @@ -334,7 +335,7 @@ }, { "cell_type": "markdown", - "id": "fff33fb7", + "id": "40941f98", "metadata": {}, "source": [ "Synaptic parameters can be made trainable in the exact same way. To use a single parameter for all syanptic conductances in the entire network, do\n", @@ -345,7 +346,7 @@ }, { "cell_type": "markdown", - "id": "096e37e2", + "id": "13001ce6", "metadata": {}, "source": [ "Here, we use a different syanptic conductance for all syanpses. This can be done as follows:" @@ -354,7 +355,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "22074636", + "id": "2e16cc73", "metadata": {}, "outputs": [ { @@ -371,7 +372,7 @@ }, { "cell_type": "markdown", - "id": "601bab3c", + "id": "54fe76dc", "metadata": {}, "source": [ "### Running the simulation" @@ -379,7 +380,7 @@ }, { "cell_type": "markdown", - "id": "89c9e348", + "id": "1646a963", "metadata": {}, "source": [ "Once all parameters are defined, you have to use `.get_parameters()` to obtain all trainable parameters. This is also the time to check how many trainable parameters your network has:" @@ -388,7 +389,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "f6ca6114", + "id": "0e54920c", "metadata": {}, "outputs": [], "source": [ @@ -397,7 +398,7 @@ }, { "cell_type": "markdown", - "id": "fb887688", + "id": "be52c812", "metadata": {}, "source": [ "You can now run the simulation with the trainable parameters by passing them to the `jx.integrate` function." @@ -406,7 +407,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "1f8b4afe", + "id": "6399f3c3", "metadata": {}, "outputs": [], "source": [ @@ -415,7 +416,7 @@ }, { "cell_type": "markdown", - "id": "3aba8d4c", + "id": "30538f09", "metadata": {}, "source": [ "### Stimulating the network\n", @@ -426,7 +427,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "38037ad4", + "id": "cdad490f", "metadata": {}, "outputs": [], "source": [ @@ -444,7 +445,7 @@ }, { "cell_type": "markdown", - "id": "2e4e0970", + "id": "cdece8b6", "metadata": {}, "source": [ "We can also inspect some traces:" @@ -453,7 +454,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "76e63570", + "id": "82b053d2", "metadata": {}, "outputs": [], "source": [ @@ -463,7 +464,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "da8d329f", + "id": "0f0ed9c1", "metadata": {}, "outputs": [ { @@ -484,7 +485,7 @@ }, { "cell_type": "markdown", - "id": "cc7b2fa6", + "id": "0db56fe3", "metadata": {}, "source": [ "### Defining a loss function" @@ -492,7 +493,7 @@ }, { "cell_type": "markdown", - "id": "e774b36f", + "id": "34d61937", "metadata": {}, "source": [ "Let us define a loss function to be optimized:" @@ -501,7 +502,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "f7ff757f", + "id": "dd214660", "metadata": {}, "outputs": [], "source": [ @@ -515,7 +516,7 @@ }, { "cell_type": "markdown", - "id": "e85619c9", + "id": "115ec1d9", "metadata": {}, "source": [ "And we can use `JAX`'s inbuilt functions to take the gradient through the entire ODE:" @@ -524,7 +525,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "70ee2cda", + "id": "e5c89912", "metadata": {}, "outputs": [], "source": [ @@ -534,7 +535,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "6698502f", + "id": "4c1da7a7", "metadata": {}, "outputs": [], "source": [ @@ -543,7 +544,7 @@ }, { "cell_type": "markdown", - "id": "66888350", + "id": "4a63f90c", "metadata": {}, "source": [ "### Defining parameter transformations" @@ -551,7 +552,7 @@ }, { "cell_type": "markdown", - "id": "f1c5e0ef", + "id": "fd1e309a", "metadata": {}, "source": [ "Before training, however, we will enforce for all parameters to be within a prespecified range (such that, e.g., conductances can not become negative)" @@ -560,7 +561,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "964a4cc3", + "id": "949b55de", "metadata": {}, "outputs": [], "source": [ @@ -570,7 +571,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "6762e2af", + "id": "3d2610c3", "metadata": {}, "outputs": [], "source": [ @@ -594,7 +595,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "ed6d271f", + "id": "5a38ba8a", "metadata": {}, "outputs": [], "source": [ @@ -605,7 +606,7 @@ }, { "cell_type": "markdown", - "id": "69df4690", + "id": "0d54760e", "metadata": {}, "source": [ "With these modify the loss function acocrdingly:" @@ -614,7 +615,7 @@ { "cell_type": "code", "execution_count": 23, - "id": "1791e84f", + "id": "e38ef4d3", "metadata": {}, "outputs": [], "source": [ @@ -630,7 +631,7 @@ }, { "cell_type": "markdown", - "id": "fcddd13b", + "id": "31f99f65", "metadata": {}, "source": [ "### Using checkpointing" @@ -638,7 +639,7 @@ }, { "cell_type": "markdown", - "id": "3ca350ca", + "id": "22c00816", "metadata": {}, "source": [ "Checkpointing allows to vastly reduce the memory requirements of training biophysical models (see also [JAX's full tutorial on checkpointing](https://jax.readthedocs.io/en/latest/gradient-checkpointing.html))." @@ -647,7 +648,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "825e988a", + "id": "98ac2e6e", "metadata": {}, "outputs": [], "source": [ @@ -661,7 +662,7 @@ }, { "cell_type": "markdown", - "id": "907090cb", + "id": "8514b640", "metadata": {}, "source": [ "To enable checkpointing, we have to modify the `simulate` function appropriately and use\n", @@ -674,7 +675,7 @@ { "cell_type": "code", "execution_count": 25, - "id": "855ea0ce", + "id": "ac66eaaa", "metadata": {}, "outputs": [], "source": [ @@ -710,7 +711,7 @@ }, { "cell_type": "markdown", - "id": "7ba885ee", + "id": "b244b584", "metadata": {}, "source": [ "### Training\n", @@ -721,7 +722,7 @@ { "cell_type": "code", "execution_count": 26, - "id": "9957d8de", + "id": "6fe36a86", "metadata": {}, "outputs": [], "source": [ @@ -731,7 +732,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "c8c080ce", + "id": "38075c69", "metadata": {}, "outputs": [], "source": [ @@ -742,7 +743,7 @@ }, { "cell_type": "markdown", - "id": "418e2e24", + "id": "5202497b", "metadata": {}, "source": [ "### Writing a dataloader" @@ -750,7 +751,7 @@ }, { "cell_type": "markdown", - "id": "114f07c8", + "id": "e09ec737", "metadata": {}, "source": [ "Below, we just write our own (very simple) dataloader. Alternatively, you could use the dataloader from any deep learning library such as pytorch or tensorflow:" @@ -759,7 +760,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "73486cbc", + "id": "561bbb4c", "metadata": {}, "outputs": [], "source": [ @@ -802,7 +803,7 @@ }, { "cell_type": "markdown", - "id": "863daf96", + "id": "e7539391", "metadata": {}, "source": [ "### Training loop" @@ -811,7 +812,7 @@ { "cell_type": "code", "execution_count": 29, - "id": "a1c04203", + "id": "5081fbfa", "metadata": {}, "outputs": [ { @@ -854,7 +855,7 @@ { "cell_type": "code", "execution_count": 30, - "id": "983dbd4f", + "id": "c921f113", "metadata": {}, "outputs": [], "source": [ @@ -865,7 +866,7 @@ { "cell_type": "code", "execution_count": 31, - "id": "3091698e", + "id": "7cf81d3e", "metadata": {}, "outputs": [ { @@ -888,7 +889,7 @@ }, { "cell_type": "markdown", - "id": "6e8a104d", + "id": "703ccef2", "metadata": {}, "source": [ "Indeed, the loss goes down and the network successfully classifies the patterns." @@ -896,7 +897,7 @@ }, { "cell_type": "markdown", - "id": "cd9e7cc4", + "id": "55ef0076", "metadata": {}, "source": [ "### Summary" @@ -904,7 +905,7 @@ }, { "cell_type": "markdown", - "id": "b6fc5e6d", + "id": "73deafa7", "metadata": {}, "source": [ "Puh, this was a pretty dense tutorial with a lot of material. You should have learned how to:\n", @@ -918,7 +919,7 @@ }, { "cell_type": "markdown", - "id": "7cef661e", + "id": "c11d1b78", "metadata": {}, "source": [ "This was the last \"basic\" tutorial of the `Jaxley` toolbox. If you want to learn more, check out our [Advanced Tutorials](https://jaxley.readthedocs.io/en/latest/advanced_tutorials.html). If anything is still unclear please create a [discussion](https://github.com/jaxleyverse/jaxley/discussions). If you find any bugs, please open an [issue](https://github.com/jaxleyverse/jaxley/issues). Happy coding!"