2021-09-27 • Vary E/I proportion
Contents
2021-09-27 • Vary E/I proportion¶
Previously we just plotted STA’s of inhibitory inputs. Now we measure the STA height, and compare it to the STA height of shuffled input spike trains.
Then we calculate separate true positive rates for inhibitory and excitatory connections, and we do this for a range of p_inhibitory.
Prelude¶
from voltage_to_wiring_sim.notebook_init import *
Preloading: numpy, numba, matplotlib.pyplot, seaborn.
Importing from submodules … ✔
Imported `np`, `mpl`, `plt`, `sns`, `pd`
Imported codebase (`voltage_to_wiring_sim`) as `v`
Imported `*` from `v.support.units`
Setup autoreload
v.print_reproducibility_info()
This cell was last run by lpxtf3 on DUIP74576 on Thu 04 Nov 2021, at 16:30 (UTC+0000).
Last git commit (Wed 03 Nov 2021, 20:11).
Uncommited changes to 6 files.
Run simulation & connection tests¶
from voltage_to_wiring_sim.experiments.N_to_1_IE import Params, simulate_and_test_connections, indices_where
v.pprint(Params())
Params
------
sim_duration = 600
timestep = 0.0001
spike_rate = 20
Δg_syn = 8E-10
τ_syn = 0.007
neuron_params = {'C': 1e-10, 'a': 30.0, 'b': -2e-09, 'c': -0.05, ...}
imaging_spike_SNR = 20
v_syn_E = 0
v_syn_I = -0.07
num_spike_trains = 30
p_inhibitory = 0.6
p_connected = 0.6
window_duration = 0.1
rng_seed = 0
from voltage_to_wiring_sim.conntest.classification import apply_threshold
from voltage_to_wiring_sim.conntest.classification_IE import (
calc_AUCs,
evaluate_classification,
sweep_threshold,
)
@v.cache_to_disk("2021-09-16__vary_E_vs_I")
def sim_and_test_and_eval_performance(p: Params):
d, _, test_summaries = simulate_and_test_connections(p)
# Eval at fixed p_value threshold
is_classified_as_connected = apply_threshold(test_summaries, p_value_threshold=0.05)
evalu = evaluate_classification(
is_classified_as_connected, d.is_connected, d.is_inhibitory
)
# Eval at all p_value thresholds
thr_sweep = sweep_threshold(test_summaries, d.is_connected, d.is_inhibitory)
AUC_inh, AUC_exc = calc_AUCs(thr_sweep)
return (evalu.TPR_inh, evalu.TPR_exc, evalu.FPR, AUC_inh, AUC_exc)
from dataclasses import replace
from itertools import product
def set_reordered_legend(ax, order: tuple[int], **kwargs):
h, l = ax.get_legend_handles_labels()
ax.legend([h[i] for i in order], [l[i] for i in order], **kwargs)
color_inh = "C1"
color_exc = "C0"
color_unconn = "C2";
def vary_p_inhibited(base_params: Params, ax1, ax2):
p_inh = np.linspace(0, 1, num=11, endpoint=True)
seeds = [0, 1, 2, 3, 4, 5]
results = []
for p_i, seed in product(p_inh, seeds):
params = replace(base_params, p_inhibitory=p_i, rng_seed=seed)
results.append(sim_and_test_and_eval_performance(params))
M = np.reshape(results, (len(p_inh), len(seeds), -1))
TPR_inh, TPR_exc, FPR, AUC_inh, AUC_exc = (M[:,:,i] for i in range(M.shape[-1]))
ax1.plot(p_inh, TPR_inh, "o", c=color_inh, ms=6, alpha=0.3)
ax1.plot(p_inh, np.mean(TPR_inh, axis=1), "-", lw=5, label="Inhibitory conn.", c=color_inh)
ax1.plot(p_inh, FPR, "o", c=color_unconn, ms=5, alpha=0.3)
ax1.plot(p_inh, np.mean(FPR, axis=1), "-", lw=4, label="Non-connections", c=color_unconn)
ax1.plot(p_inh, TPR_exc, "o", c=color_exc, ms=4, alpha=0.3)
ax1.plot(p_inh, np.mean(TPR_exc, axis=1), "-", lw=2.6, label="Excitatory conn.", c=color_exc)
ax1.set_ylabel("Fraction detected")
set_reordered_legend(ax1, [2, 0, 1])
ax2.plot(p_inh, AUC_inh, "o", c=color_inh, ms=6, alpha=0.3)
ax2.plot(p_inh, AUC_exc, "o", c=color_exc, ms=4, alpha=0.3)
ax2.plot(p_inh, np.mean(AUC_inh, axis=1), "-", lw=5, label="Inhibitory conn.", c=color_inh)
ax2.plot(p_inh, np.mean(AUC_exc, axis=1), "-", lw=2.6, label="Excitatory conn.", c=color_exc)
ax2.set_ylim(0.5, 1.02)
ax2.set_xlabel("Proportion of inputs inhibitory ($p_{inh}$)")
ax2.set_ylabel("Area under ROC curve")
set_reordered_legend(ax2, [1, 0])
(An uncached run of the above takes a bit over 3 minutes, for 1 seed).
fig, axes = plt.subplots(2, 2, **v.figsize(width=1000), sharex=True)
vary_p_inhibited(Params(v_syn_I = -70 * mV), axes[0,0], axes[1,0])
vary_p_inhibited(Params(v_syn_I = -50 * mV), axes[0,1], axes[1,1])
set_reordered_legend(axes[0,1], [2,0,1], loc="center left", bbox_to_anchor=(0.02, 0.78))
axes[0,0].set_title("$v_{inh} = -70$ mV", pad=24)
axes[0,1].set_title("$v_{inh} = -50$ mV", pad=24)
plt.subplots_adjust(wspace=0.4)
Uncached run for two seeds took 16’30”.
individual sims getting gradually slower.. from 20 s at start (v -70 p 0 seed 1) to 52 at end (v -50 p 1 seed 2). Maybe memory drag? python process is at 2GB now.
cache loading is deliciously fast though :)
Inspect some regimes¶
sim_and_test = v.cache_to_disk(simulate_and_test_connections);
fig, axes = plt.subplots(nrows=3, ncols=4, **v.figsize(width=1000))
for i, (v_syn_I, p_inh) in enumerate(product((-70 * mV, -50 * mV), (0.1, 0.9))):
d, td, ts = sim_and_test(Params(v_syn_I=v_syn_I, p_inhibitory=p_inh))
print(d.num_exc_conn, d.num_inh_conn)
ax = v.plot_signal(d.izh_output.V_m.slice(t_start=0, duration=1*second) / mV, time_units=ms, ax=axes[0, i])
ax.set_ylim(-65, -22);
ax.set_xlabel("Time (ms)")
ax.set_title("$v_{inh} = " + f"{v_syn_I / mV:.0f}$ mV" + "\n" +
"$p_{inh} = " + f"{p_inh:.1f}$", pad=24)
ax_e = v.plot_STA(td[indices_where(d.is_excitatory & d.is_connected)[0]].original_STA, ax=axes[1, i])
ax_i = v.plot_STA(td[indices_where(d.is_inhibitory & d.is_connected)[0]].original_STA, ax=axes[2, i])
ylims = (min(ax_i.get_ylim()[0], ax_e.get_ylim()[0]),
max(ax_i.get_ylim()[1], ax_e.get_ylim()[1]))
ax_e.set_ylim(ylims)
ax_i.set_ylim(ylims)
if i == 0:
ax.set_ylabel("Membrane pot. (mV)");
ax_e.set_ylabel("STA of VI signal for an" + '\n' + r"$\bf{excitatory}$ conn. (mV)")
ax_i.set_ylabel("STA of VI signal for an" + '\n' + r"$\bf{inhibitory}$ conn. (mV)")
else:
ax_e.set_ylabel(None)
ax_i.set_ylabel(None)
fig.tight_layout()
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d, td, ts = sim_and_test(Params(v_syn_I=-50 * mV, p_inhibitory=0.1));
np.median(d.izh_output.V_m) / mV
-50.468785742174646
fig, axes = plt.subplots(nrows=3, ncols=4, **v.figsize(width=1000))
for i, (v_syn_I, p_inh) in enumerate(product((-50 * mV,), (0.5, 0.6, 0.7))):
d, td, ts = sim_and_test(Params(v_syn_I=v_syn_I, p_inhibitory=p_inh))
print(d.num_exc_conn, d.num_inh_conn)
ax = v.plot_signal(d.izh_output.V_m.slice(t_start=0, duration=8*second) / mV, time_units=ms, ax=axes[0, i])
ax.set_ylim(-65, -22);
ax.set_xlabel("Time (ms)")
ax.set_title("$v_{inh}$ = " + f"{v_syn_I / mV:.0f} mV" + "\n" +
"$p_{inh}$ = " + f"{p_inh:.1f}")
ax_e = v.plot_STA(td[indices_where(d.is_excitatory & d.is_connected)[0]].original_STA, ax=axes[1, i])
ax_i = v.plot_STA(td[indices_where(d.is_inhibitory & d.is_connected)[0]].original_STA, ax=axes[2, i])
ylims = (min(ax_i.get_ylim()[0], ax_e.get_ylim()[0]),
max(ax_i.get_ylim()[1], ax_e.get_ylim()[1]))
ax_e.set_ylim(ylims)
ax_i.set_ylim(ylims)
if i == 0:
ax.set_ylabel("Membrane pot. (mV)");
ax_e.set_ylabel("STA of VI signal for an" + '\n' + r"$\bf{excitatory}$ conn. (mV)")
ax_i.set_ylabel("STA of VI signal for an" + '\n' + r"$\bf{inhibitory}$ conn. (mV)")
else:
ax_e.set_ylabel(None)
ax_i.set_ylabel(None)
fig.tight_layout()
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# v.plot_signal(d.VI_signal.slice(t_start=0, duration=1*second) / mV, time_units=ms);
Reproducibility¶
v.print_reproducibility_info(verbose=True)
This cell was last run by lpxtf3 on DUIP74576
on Thu 28 Oct 2021, at 15:09 (UTC+0100).
Last git commit (Thu 28 Oct 2021, 14:11).
Uncommited changes to:
M notebooks/2021-09-16__vary_E_vs_I.ipynb
Platform:
Windows-10
CPython 3.9.6 (C:\miniforge3\python.exe)
Intel(R) Xeon(R) W-2123 CPU @ 3.60GHz
Dependencies of voltage_to_wiring_sim and their installed versions:
numpy 1.21.1
matplotlib 3.4.2
numba 0.53.1
joblib 1.0.1
seaborn 0.11.1
scipy 1.7.0
preload 2.2
nptyping 1.4.2
Full conda list:
# packages in environment at C:\miniforge3:
#
# Name Version Build Channel
argon2-cffi 20.1.0 py39hb82d6ee_2 conda-forge
async_generator 1.10 py_0 conda-forge
attrs 21.2.0 pyhd8ed1ab_0 conda-forge
backcall 0.2.0 pyh9f0ad1d_0 conda-forge
backports 1.0 py_2 conda-forge
backports.functools_lru_cache 1.6.4 pyhd8ed1ab_0 conda-forge
bleach 3.3.1 pyhd8ed1ab_0 conda-forge
brotlipy 0.7.0 py39hb82d6ee_1001 conda-forge
ca-certificates 2019.11.28 hecc5488_0 conda-forge/label/cf202003
certifi 2021.10.8 py39hcbf5309_0 conda-forge
cffi 1.14.6 py39h0878f49_0 conda-forge
chardet 4.0.0 py39hcbf5309_1 conda-forge
charset-normalizer 2.0.0 pyhd8ed1ab_0 conda-forge
click 7.1.2 pypi_0 pypi
colorama 0.4.4 pyh9f0ad1d_0 conda-forge
conda 4.10.3 py39hcbf5309_2 conda-forge
conda-package-handling 1.7.3 py39hb3671d1_0 conda-forge
cryptography 3.4.7 py39hd8d06c1_0 conda-forge
cycler 0.10.0 pypi_0 pypi
debugpy 1.4.1 py39h415ef7b_0 conda-forge
decorator 5.0.9 pyhd8ed1ab_0 conda-forge
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icu 68.2 h0e60522_0 conda-forge
idna 3.1 pyhd3deb0d_0 conda-forge
importlib-metadata 4.6.1 py39hcbf5309_0 conda-forge
ipykernel 6.0.3 py39h832f523_0 conda-forge
ipython 7.25.0 py39h832f523_1 conda-forge
ipython_genutils 0.2.0 py_1 conda-forge
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