{
"cells": [
{
"cell_type": "markdown",
"id": "6b4fc05a",
"metadata": {},
"source": [
"### run_example_simulation.ipynb\n",
"\n",
"This notebook runs the AL model with two short odor presentation that runs on a personal machine in the span of a minute, highlighting the main functions for circuit execution and visualization."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "afdf4b5e",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"import numpy as np\n",
"import pandas as pd\n",
"import time\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "25dfc103",
"metadata": {},
"outputs": [],
"source": [
"project_dir = '../'\n",
"sys.path.append(project_dir)\n",
"\n",
"from utils.data_utils import get_AL_activity_dfs\n",
"from utils.simulation_class import Sim\n",
"from utils.LIF_funcs_cython import run_LIF_general_sim_wrap as run_LIF, spikes_from_APMask\n",
"from utils.data_utils import set_connect_from_scale_dic\n",
"from utils.model_params import params as default_params\n",
"from utils.make_output_figures import plot_sim_spikes2, plot_AL_activity_dur_pre_odors\n",
"from utils.plot_utils import set_font_sizes\n",
"set_font_sizes()"
]
},
{
"cell_type": "markdown",
"id": "889320c8",
"metadata": {},
"source": [
"Load connectomic information and imputed glomerulus x odor responses:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "455d8b14",
"metadata": {},
"outputs": [],
"source": [
"# default hemibrain data\n",
"hemibrain_dir = os.path.join(project_dir, 'connectomics/hemibrain_v1_2')\n",
"df_neur_ids = pd.read_csv(os.path.join(hemibrain_dir, 'df_neur_ids.csv'), index_col=0)\n",
"al_block = pd.read_csv(os.path.join(hemibrain_dir, 'AL_block.csv'), index_col=0)\n",
"imputed_glom_odor_table = pd.read_csv(os.path.join(project_dir, 'odor_imputation/df_odor_door_all_odors_imput_ALS.csv'), index_col=0)"
]
},
{
"cell_type": "markdown",
"id": "4e726b0a",
"metadata": {},
"source": [
"Set odor presentation information:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ca6242da",
"metadata": {},
"outputs": [],
"source": [
"# set ORN decay dynamics to mimic adaptation\n",
"decay_tc = 0.11 # seconds to half-height\n",
"decay_fadapt = 0.75 # a fraction of max height\n",
"\n",
"# odors to present\n",
"odor_panel = ['3-octanol', '4-methylcyclohexanol']\n",
"\n",
"# set up time array\n",
"n_odors = len(odor_panel)\n",
"stim_duration = 0.01\n",
"stim_pause = 0.005\n",
"trial_duration = stim_duration + stim_pause\n",
"end_time = (n_odors*trial_duration) + 0.001"
]
},
{
"cell_type": "markdown",
"id": "5933ab3c",
"metadata": {},
"source": [
"Set multipliers onto cell type columns of the connectivity matrix (these particular ones were found by hand tuning to give reasonable AL features):"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "dcaf8672",
"metadata": {},
"outputs": [],
"source": [
"col_orn = 1\n",
"col_iln = 0.2\n",
"col_eln = 0.4\n",
"col_pn = 4\n",
"\n",
"custom_scale_dic = {\n",
" 'ALL': 0.1,\n",
" 'otoo': col_orn, 'otoi': col_iln, 'otoe': col_eln, 'otop': col_pn,\n",
" 'itoo': col_orn, 'itoi': col_iln, 'itoe': col_eln, 'itop': col_pn,\n",
" 'etoo': col_orn, 'etoi': col_iln, 'etoe': col_eln, 'etop': col_pn,\n",
" 'ptoo': col_orn, 'ptoi': col_iln, 'ptoe': col_eln, 'ptop': col_pn,\n",
" }"
]
},
{
"cell_type": "markdown",
"id": "0d6ba86c",
"metadata": {},
"source": [
"Initialize an instance of a Sim object that houses all simulation info:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "c6e4c147",
"metadata": {},
"outputs": [],
"source": [
"sim = Sim(params = default_params,\n",
" df_neur_ids=df_neur_ids, \n",
" al_block=al_block,\n",
" model_name = 'example run',\n",
" home_dir = project_dir,\n",
" end_time = end_time,\n",
" )"
]
},
{
"cell_type": "markdown",
"id": "36cd7bac",
"metadata": {},
"source": [
"Add the odors into the Sim object:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "25a5807c",
"metadata": {},
"outputs": [],
"source": [
"# set up timings\n",
"t_odor_bounds = np.arange(0, end_time+0.01, trial_duration)\n",
"start_times = t_odor_bounds[:-1] + stim_pause\n",
"end_times = t_odor_bounds[1:]\n",
"\n",
"# add odors according to their times\n",
"for i in range(n_odors):\n",
" sim.add_odor(odor_name = odor_panel[i],\n",
" odor_start = start_times[i], \n",
" odor_end = end_times[i],\n",
" tc = decay_tc,\n",
" fadapt = decay_fadapt,\n",
" imputed_glom_responses=imputed_glom_odor_table)"
]
},
{
"cell_type": "markdown",
"id": "ccd7f313",
"metadata": {},
"source": [
"Set the timing of odor-evoked spikes on ORNs:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2ca39622",
"metadata": {},
"outputs": [],
"source": [
"# set Iin\n",
"Iin = sim.make_Iin(is_input_PSC=True)"
]
},
{
"cell_type": "markdown",
"id": "b7bf9e1d",
"metadata": {},
"source": [
"Set which LNs to flip to excitatory:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "53af8255",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[63 36 54 62 78 85 55 57 44 92 29 40 33 61 39 59 1 56 71 9 79 27 66 72\n",
" 96 48 35 74 4 64 10]\n"
]
}
],
"source": [
"# set excitatory LN positions\n",
"np.random.seed(1234)\n",
"LN_bodyIds = df_neur_ids[df_neur_ids.altype == 'LN'].bodyId.values\n",
"num_LNs = len(LN_bodyIds)\n",
"num_eLNs = int(np.round(num_LNs / 6.4))\n",
"topk = int(np.round(num_LNs / 2))\n",
"nlns = len(LN_bodyIds)\n",
"elnpos = np.random.choice(np.arange(topk), num_eLNs, replace=False) \n",
"print(elnpos)\n",
"\n",
"# ### Set synapse strengths \n",
"sim.set_eLNs(elnpos)"
]
},
{
"cell_type": "markdown",
"id": "a7273af2",
"metadata": {},
"source": [
"Set the connectivity matrix multipliers from above:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "1c97654f",
"metadata": {},
"outputs": [],
"source": [
"# set scalars on class to class strengths\n",
"connect = set_connect_from_scale_dic(custom_scale_dic, sim)\n",
"sim.set_connect(connect.values) "
]
},
{
"cell_type": "markdown",
"id": "aa8b76a5",
"metadata": {},
"source": [
"Run the leaky-integrate and fire simulator, and retrieve arrays of `num_neurons x num_timesteps` for voltages (`V`), currents (`I`), and indicators of when a neuron is undergoing a templated action potential (`APMask`):\n",
"\n",
"(should run in about a minute)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "b5d20ba5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"running LIF...\n",
"done running LIF\n",
"elapsed 26.17527198791504\n"
]
}
],
"source": [
"# ### Run simulation\n",
"t0 = time.time()\n",
"print('running LIF...')\n",
"V, I, APMask = run_LIF(sim, Iin)\n",
"print('done running LIF')\n",
"tf = time.time()\n",
"print('elapsed', tf-t0)"
]
},
{
"cell_type": "markdown",
"id": "43bd7820",
"metadata": {},
"source": [
"Get spike timings for all neurons via the action potential mask:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "274f9b8f",
"metadata": {},
"outputs": [],
"source": [
"# count spikes\n",
"Spikes = spikes_from_APMask(APMask)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "a5a955c2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" ...,\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.],\n",
" [0., 0., 0., ..., 0., 0., 0.]])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Spikes"
]
},
{
"cell_type": "markdown",
"id": "3c5f7c37",
"metadata": {},
"source": [
"For each odor, for each neuron, record firing rates in the time before the odor presentation, and during:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "6ca8d63b",
"metadata": {},
"outputs": [],
"source": [
"df_AL_activity, df_AL_activity_long = get_AL_activity_dfs(sim, Spikes)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "5883fda7",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" neur_name | \n",
" neur_type | \n",
" fr_pre_odor0 | \n",
" fr_dur_odor0 | \n",
" fr_pre_odor1 | \n",
" fr_dur_odor1 | \n",
" glom | \n",
"
\n",
" \n",
" \n",
" \n",
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" ORN_DA1_0 | \n",
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\n",
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\n",
" \n",
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\n",
" \n",
" | 4 | \n",
" ORN_DA1_4 | \n",
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\n",
" \n",
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"