{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Measuring Synaptic Kinetics\n",
"\n",
"In this notebook we use the [Allen Institute synaptic physiology dataset](https://portal.brain-map.org/explore/connectivity/synaptic-physiology) to measure the kinetic properties of synaptic connections and the 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",
"from aisynphys.database import SynphysDatabase\n",
"from aisynphys.cell_class import CellClass"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Download and cache the sqlite file for the requested database\n",
"# (for available versions, see SynphysDatabase.list_versions)\n",
"db = SynphysDatabase.load_version('synphys_r1.0_2019-08-29_small.sqlite')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are going to compare the strength of excitatory connectivity onto the three inhibitory cell subclassess -- Pvalb, Sst, and Vip.\n",
"\n",
"Begin by defining these subclasses:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"post_classes = {\n",
" 'pvalb': CellClass(cre_type='pvalb'),\n",
" 'sst': CellClass(cre_type='sst'),\n",
" 'vip': CellClass(cre_type='vip'),\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Query the database to get all excitatory synapses with a specific postsynaptic cre type. We also filter here for specific project names \"mouse V1 coarse matrix\" and \"mouse V1 pre production\" in order to exclude other experiment types."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"pvalb: 90 synapses\n",
"sst: 71 synapses\n",
"vip: 24 synapses\n"
]
}
],
"source": [
"# query once for each postsynaptic type, building up a Pandas dataframe\n",
"\n",
"pairs = None\n",
"for name, post_class in post_classes.items():\n",
" pair_query = db.pair_query(\n",
" project_name=[\"mouse V1 coarse matrix\", \"mouse V1 pre production\"],\n",
" post_class=post_class,\n",
" synapse=True,\n",
" synapse_type='ex',\n",
" )\n",
" pair_query = pair_query.add_columns(\n",
" db.Synapse.latency,\n",
" db.Synapse.psc_rise_time,\n",
" db.Synapse.psc_decay_tau,\n",
" db.Synapse.psp_amplitude,\n",
" )\n",
" df = pair_query.dataframe()\n",
" df['post_class'] = name\n",
" if pairs is None:\n",
" pairs = df\n",
" else:\n",
" pairs = pairs.append(df)\n",
" print(\"%s: %d synapses\" % (name, len(df)))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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