{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Measuring Synaptic Short Term Plasticity\n",
"\n",
"In this notebook we use the [Allen Institute synaptic physiology dataset](https://portal.brain-map.org/explore/connectivity/synaptic-physiology) to measure short term plasticity and its relationship to cell subclass.\n",
"\n",
"For an introduction to the Jupyter Notebook interface interface, try [Codeacademy: How To Use Jupyter Notebooks]( https://www.codecademy.com/articles/how-to-use-jupyter-notebooks) or [Jupyter Notebook Quick Start Guide](https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/what_is_jupyter.html).\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import scipy.stats\n",
"from aisynphys.database import SynphysDatabase\n",
"from aisynphys.cell_class import CellClass, classify_pair_dataframe"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. Load the data\n",
"\n",
"First, load up the \"small\" database from release 1.0 and query all cell `pairs` for mouse projects."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loaded 1805 synapses\n"
]
}
],
"source": [
"# Download and cache the sqlite file for the requested database\n",
"# (for available versions, see SynphysDatabase.list_versions)\n",
"db = SynphysDatabase.load_current('small')\n",
"\n",
"# Load all synapses associated with mouse multipatch experiments\n",
"pairs = db.pair_query(\n",
" experiment_type='standard multipatch',\n",
" species='mouse',\n",
" synapse=True,\n",
" preload=['cell', 'synapse'],\n",
").dataframe()\n",
"\n",
"print(\"loaded %d synapses\" % len(pairs))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Classify cells\n",
"\n",
"We will pick four simple class defintions for this analysis:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"cell_classes = {\n",
" 'pyr': CellClass(cell_class='ex', name='pyr'),\n",
" 'pvalb': CellClass(cre_type='pvalb', name='pvalb'),\n",
" 'sst': CellClass(cre_type='sst', name='sst'),\n",
" 'vip': CellClass(cre_type='vip', name='vip'),\n",
"} "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# add new columns to the dataframe that apply the classification defined above\n",
"classify_pair_dataframe(cell_classes, pairs, col_names=('pre_class', 'post_class'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. Calculate average STP metric for each pre/post pairing"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | post_class | \n",
" pyr | \n",
" pvalb | \n",
" sst | \n",
" vip | \n",
"
\n",
" \n",
" | pre_class | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" | pyr | \n",
" -0.336121 | \n",
" -0.236252 | \n",
" 0.153188 | \n",
" -0.054481 | \n",
"
\n",
" \n",
" | pvalb | \n",
" -0.498625 | \n",
" -0.475758 | \n",
" -0.347782 | \n",
" -0.263452 | \n",
"
\n",
" \n",
" | sst | \n",
" -0.125576 | \n",
" -0.133408 | \n",
" -0.168907 | \n",
" 0.288444 | \n",
"
\n",
" \n",
" | vip | \n",
" -0.163041 | \n",
" -0.103669 | \n",
" 0.045582 | \n",
" -0.012254 | \n",
"
\n",
" \n",
"
\n",
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"%matplotlib inline\n",
"\n",
"fig,ax = plt.subplots(figsize=(8, 6))\n",
"\n",
"hm = sns.heatmap(stp, cmap='coolwarm', vmin=-0.4, vmax=0.4, square=True, ax=ax,\n",
" cbar_kws={\"ticks\":[-0.3, 0, 0.3], 'label': '<-- depressing facilitating -->'})\n",
"\n",
"fig.suptitle(\"50 Hz Train-induced STP\", fontsize=16)\n",
"hm.set_xlabel(\"postsynaptic\", fontsize=14)\n",
"hm.set_ylabel(\"presynaptic\", fontsize=14);\n",
"hm.figure.axes[-1].yaxis.label.set_size(14)\n",
"hm.tick_params(labelsize=12)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Alternatively, we can calculate the average resting state variance for each pairing"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | post_class | \n",
" pyr | \n",
" pvalb | \n",
" sst | \n",
" vip | \n",
"
\n",
" \n",
" | pre_class | \n",
" | \n",
" | \n",
" | \n",
" | \n",
"
\n",
" \n",
" \n",
" \n",
" | pyr | \n",
" -0.339417 | \n",
" 0.490162 | \n",
" 1.822031 | \n",
" 1.548100 | \n",
"
\n",
" \n",
" | pvalb | \n",
" -0.833936 | \n",
" -0.919017 | \n",
" -0.719812 | \n",
" -0.142069 | \n",
"
\n",
" \n",
" | sst | \n",
" -0.230243 | \n",
" -0.159587 | \n",
" 0.125349 | \n",
" 0.076180 | \n",
"
\n",
" \n",
" | vip | \n",
" 0.539063 | \n",
" 0.579907 | \n",
" 0.502864 | \n",
" 0.271833 | \n",
"
\n",
" \n",
"
\n",
"