2021-07-30 • Inhibitory connections
Contents
2021-07-30 • Inhibitory connections¶
Hello back. We’re re-running the simulation as before, only this time ‘stimulating’ our one Izhikevich neuron with inhibitory spike trains; that is: we’ll lower the reversal potential of the synapse.
What we did previously: aggregate all incoming spikes together (in N_to_1_experiment.simulate), then use this one spike train to calculate a conductance signal (exp decay; in synapses.py); then feed this conductance signal to sim_izh, where a synaptic current I_syn is calculated as g_syn[i] * (v[i] - v_syn), with v_syn a fixed synaptic reversal potential.
We’ll need to change this architecture:
We have to calculate a conductance signal for every incoming spike train separately (we can keep
synapses.pyas is).sim_izhneeds to take not just oneg_synsignal; but one for every spike train.v_synis no longer a property of the neuron; it is a property of every incoming spike train.
Our new simulation code in the codebase will no longer work in older notebooks (but that’s why we have git and the repro info).
Where to start? What is our eventual goal here?
Well the question is if we can still detect connections from activities alone. We did it with STA height previously. Won’t work now right?
Let’s start by plotting izh output (v_mem) and STA with a negative v_syn.
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 tfiers on yoga on Mon 23 Aug 2021, at 22:28 (UTC+0200).
Last git commit (Mon 23 Aug 2021, 19:05).
Uncommited changes to 1 file.
Neuron input¶
Here we generate a bunch of spike trains, each paired with a synaptic reversal potential.
v_syn_E = 0 * mV
v_syn_I = -50 * mV;
We choose the inhibitory reversal potential to be 10 mV higher than the resting potential. This is what’s found e.g. here for GABA neurons: https://www.nature.com/articles/ncomms1377.pdf
num_spike_trains = 30
p_inhibitory = 0.33
p_connected = 0.6
spike_rate = 20 * Hz;
Other parameters, same as in previous notebooks:
from voltage_to_wiring_sim.sim.izhikevich_neuron import cortical_RS
sim_duration = 10 * minute
timestep = 0.1 * ms
spike_rate = 20 * Hz
Δg_syn = 0.8 * nS
τ_syn = 7 * ms
neuron_params = cortical_RS
imaging_spike_SNR = 20;
Now for the actual input generation.
from voltage_to_wiring_sim.sim.poisson_spikes import generate_Poisson_spikes
from random import random
input_spike_trains = np.empty(num_spike_trains, dtype=object)
v_syn = np.empty(num_spike_trains, dtype=float)
is_connected = np.zeros(num_spike_trains, dtype=bool)
for i in range(num_spike_trains):
input_spike_trains[i] = generate_Poisson_spikes(spike_rate, sim_duration)
if random() < p_connected:
is_connected[i] = True
if random() < p_inhibitory:
v_syn[i] = v_syn_I
else:
v_syn[i] = v_syn_E
connected_and_E = indices_where(is_connected & (v_syn == v_syn_E))
connected_and_I = indices_where(is_connected & (v_syn == v_syn_I))
not_connected = indices_where(~is_connected);
Izhikevich simulation¶
I edited the sim_izh to take a list of v_syns and g_syn signals.
Let’s make the g_syn signals.
from voltage_to_wiring_sim.sim.synapses import calc_synaptic_conductance
def indices_where(bool_array):
return np.nonzero(bool_array)[0]
g_syn = []
for i in indices_where(is_connected):
g_syn.append(calc_synaptic_conductance(sim_duration, timestep, input_spike_trains[i], Δg_syn, τ_syn))
v_syn_connected = v_syn[indices_where(is_connected)];
..and now pass them to the izh sim code.
from voltage_to_wiring_sim.sim.izhikevich_neuron import simulate_izh_neuron
izh_output = simulate_izh_neuron(sim_duration, timestep, neuron_params, v_syn_connected, g_syn);
from voltage_to_wiring_sim.support.signal import plot_signal
plot_slice = dict(t_start=10 * second, duration=400 * ms);
plot_signal(izh_output.V_m.slice(**plot_slice) / mV);
all_connected_I_spikes = np.concatenate([st for st in input_spike_trains[connected_and_I]])
v.support.spike_train.plot_spike_train(all_connected_I_spikes, [10, 10.4]);
all_connected_E_spikes = np.concatenate([st for st in input_spike_trains[connected_and_E]])
v.support.spike_train.plot_spike_train(all_connected_E_spikes, [10, 10.4]);
VI noise & STAs¶
VI_signal = v.sim.imaging.add_VI_noise(izh_output.V_m, neuron_params, imaging_spike_SNR);
plot_signal(VI_signal.slice(**plot_slice) / mV);
window_duration = 100 * ms;
def plot_STA(spike_train_index):
STA = v.conntest.STA.calculate_STA(VI_signal, input_spike_trains[spike_train_index], window_duration)
ax = plot_signal(STA / mV, time_units=ms)
# ax.set_ylim([-53, -50]) # mV
ax.set_ylim([-48.5, -45]) # mV
Results¶
No shuffle test for now; just visually inspecting the STAs.
Connected excitatory and unconnected are as before:
plot_STA(connected_and_E[0])
plot_STA(not_connected[0])
Inhibitory STA does not look much different from unconnected (i.e. same as shuffled) STA, alas:
for i in range(len(connected_and_I)):
plot_STA(connected_and_I[i])
Reproducibility¶
v.print_reproducibility_info(verbose=True)
This cell was last run by tfiers on yoga
on Mon 23 Aug 2021, at 18:13 (UTC+0200).
Last git commit (Fri 20 Aug 2021, 21:41).
Uncommited changes to:
M codebase/tests/test_run_embedded_tests.py
M codebase/voltage_to_wiring_sim/N_to_1_experiment.py
M codebase/voltage_to_wiring_sim/__init__.py
M codebase/voltage_to_wiring_sim/sim/__init__.py
M codebase/voltage_to_wiring_sim/sim/izhikevich_neuron.py
M codebase/voltage_to_wiring_sim/sim/neuron_params.py
RM codebase/voltage_to_wiring_sim/sim/input_spike_trains.py -> codebase/voltage_to_wiring_sim/sim/poisson_spikes.py
M codebase/voltage_to_wiring_sim/sim/synapses.py
M notebooks/2021-07-30__inhibitory.ipynb
Platform:
Windows-10
CPython 3.8.6 (C:\conda\python.exe)
Intel(R) Core(TM) i7-10510U CPU @ 1.80GHz
Dependencies of voltage_to_wiring_sim and their installed versions:
numpy 1.19.5
matplotlib 3.3.4
numba 0.52.0
joblib 1.0.1
seaborn 0.11.1
scipy 1.6.0
preload 2.2
nptyping 1.4.0
Full conda list:
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