{
"cells": [
{
"cell_type": "markdown",
"id": "e4b3bbb6",
"metadata": {},
"source": [
"# 2023-08-30__STA_examples"
]
},
{
"cell_type": "markdown",
"id": "b8dea55c",
"metadata": {},
"source": [
"(Result of workflow speed (& ergonomy) tests: full Julia (no Python hybrid))"
]
},
{
"cell_type": "markdown",
"id": "e2c22c5f",
"metadata": {},
"source": [
"So, for all 10 Ns;\\\n",
"For 10 diff seeds;\n",
"for both exc, inh, and unconn;\\\n",
"we conntest (maximum) 100 input spike trains.\\\n",
"(Each test is comprised of calculating 101 STAs: one real and the rest with shuffled ISIs)."
]
},
{
"cell_type": "markdown",
"id": "47729ae0",
"metadata": {},
"source": [
"From the prev nb (https://tfiers.github.io/phd/nb/2023-08-16__STA_conntest_pyjulia.html),\n",
"we found we'd take a shorter window, so that 'area over start' measure (to determine if exc or inh) is correct."
]
},
{
"cell_type": "markdown",
"id": "7b1d1381",
"metadata": {},
"source": [
"But ok, it's good to show that in thesis.\\\n",
"So, we repeat an example STA plot here.\n",
"\n",
"for full N ofc."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "87bc30a3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"using Revise … ✔ (0.3 s)\n",
"using Units, Nto1AdEx, ConnectionTests, ConnTestEval … "
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mPrecompiling Nto1AdEx [368485ca-63bc-4029-854f-349d2205662c]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"✔ (1.5 s)\n",
"using StatsBase … ✔ (0.2 s)\n"
]
}
],
"source": [
"include(\"lib/Nto1.jl\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6472ac5d",
"metadata": {},
"outputs": [],
"source": [
"N = 6500;"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "eadc71d1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"600"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"duration = 10minutes"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "6e1f2ff2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 2.994944 seconds (2.14 M allocations: 1.027 GiB, 2.34% gc time, 42.14% compilation time)\n"
]
}
],
"source": [
"@time sim = Nto1AdEx.sim(N, duration);"
]
},
{
"cell_type": "markdown",
"id": "c478ceef",
"metadata": {},
"source": [
"(1st run: 2.5 secs, 27% compilation time)."
]
},
{
"cell_type": "markdown",
"id": "4082c38b",
"metadata": {},
"source": [
"We want our input spiketrains sorted: the highest spikers first.\\\n",
"And split exc/inh, too."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "7b31295e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"using DataFrames … ✔ (0.7 s)\n"
]
}
],
"source": [
"include(\"lib/df.jl\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "9d2ca124",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"5200×2 DataFrame
5190 rows omitted
| 1 | 58522 | 97.5 |
| 2 | 35312 | 58.9 |
| 3 | 24428 | 40.7 |
| 4 | 20653 | 34.4 |
| 5 | 18898 | 31.5 |
| ⋮ | ⋮ | ⋮ |
| 5196 | 194 | 0.323 |
| 5197 | 185 | 0.308 |
| 5198 | 175 | 0.292 |
| 5199 | 168 | 0.28 |
| 5200 | 123 | 0.205 |
1300×2 DataFrame
1290 rows omitted
| 1 | 21808 | 36.3 |
| 2 | 21054 | 35.1 |
| 3 | 16402 | 27.3 |
| 4 | 14905 | 24.8 |
| 5 | 14429 | 24 |
| ⋮ | ⋮ | ⋮ |
| 1296 | 234 | 0.39 |
| 1297 | 220 | 0.367 |
| 1298 | 218 | 0.363 |
| 1299 | 197 | 0.328 |
| 1300 | 140 | 0.233 |
| 1 | 0.0 | -65.0 |
| 2 | 0.01 | -58.1 |
| 3 | 0.5 | -54.0 |
| 4 | 0.9 | -51.6 |
| 5 | 0.99 | -49.8 |
| 6 | 0.999 | -46.8 |
| 7 | 1.0 | 40.0 |