2022-05-02• STA mean vs peak-to-peak
Contents
2022-05-02• STA mean vs peak-to-peak¶
Peak-to-peak cannot distinguish excitatory vs inhibitory (it’s just the height of the bump – no matter whether it’s upwards or downwards).
To do that, we might instead use sth like “either max-median or min-median, whatever’s largest in absolute terms”.
For now we don’t try to detect this difference, and ‘cheat’ in our code (special-case the inhibitory / ptp case when comparing p-values with α).
Setup¶
#
using Revise
using MyToolbox
using VoltageToMap
[ Info: Precompiling VoltageToMap [b3b8fdc5-3c26-4000-a0c8-f17415fdf48e]
using PyPlot
using VoltageToMap.Plot
[ Info: Precompiling Sciplotlib [61be95e5-9550-4d5f-a203-92a5acbc3116]
Params¶
N_excs = [
4, # => N_inh = 1
17, # Same as in `previous_N_30_input`.
80,
320,
1280,
5200,
];
rngseeds = [0:16;];
get_params((N_exc, STA_test_statistic, rngseed)) = ExperimentParams(
sim = SimParams(
duration = 10 * minutes,
imaging = get_VI_params_for(cortical_RS, spike_SNR = Inf),
input = PoissonInputParams(; N_exc);
rngseed,
),
conntest = ConnTestParams(; STA_test_statistic, rngseed);
evaluation = EvaluationParams(; rngseed)
);
variableparams = collect(product(N_excs, ["ptp", "mean"], rngseeds));
paramsets = get_params.(variableparams);
print(summary(paramsets))
6×2×17 Array{ExperimentParams, 3}
dumps(paramsets[1])
ExperimentParams
sim: SimParams
duration: 600.0
Δt: 0.0001
num_timesteps: 6000000
rngseed: 0
input: PoissonInputParams
N_unconn: 100
N_exc: 4
N_inh: 1
N_conn: 5
N: 105
spike_rates: LogNormal
μ: 1.08629
σ: 0.774597
synapses: SynapseParams
avg_stim_rate_exc: 1.0e-10
avg_stim_rate_inh: 4.0e-10
E_exc: 0.0
E_inh: -0.065
g_t0: 0.0
τ: 0.007
izh_neuron: IzhikevichParams
C: 1.0e-10
k: 7.0e-7
v_rest: -0.06
v_thr: -0.04
a: 30.0
b: -2.0e-9
v_peak: 0.035
v_reset: -0.05
Δu: 1.0e-10
v_t0: -0.06
u_t0: 0.0
imaging: VoltageImagingParams
spike_SNR: Inf
spike_SNR_dB: Inf
spike_height: 0.095
σ_noise: 0.0
conntest: ConnTestParams
STA_window_length: 0.1
num_shuffles: 100
STA_test_statistic: ptp
rngseed: 0
evaluation: EvaluationParams
α: 0.05
num_tested_neurons_per_group: 40
rngseed: 0
Run¶
perfs = similar(paramsets, NamedTuple)
for i in eachindex(paramsets)
(N_exc, stat, seed) = variableparams[i]
paramset = paramsets[i]
println((; N_exc, stat, seed), " ", cachefilename(paramset))
perf = cached(sim_and_eval, [paramset])
perfs[i] = perf
end
simcache (10’ x 6 N x 4 seeds): 3GB95 à 142MB per 10’ sim
perfcache: 0.3MB – so that could go in git
Prepare plot¶
We want to plot dots.
We can either have
N = [5, 21]
and TPR_exc = [1 .9 1; .8 .7 .8] (matrix notation. 3 seeds).
or
N = [5, 5, 5, 21, 21, 21] (i.e. repeat)
and TPR_exc = [1, .9, 1, .8, .7, .8].
"""
Create an array of the same shape as the one given, but with just
the values stored under `name` in each element of the given array.
"""
function extract(name::Symbol, arr #=an array of NamedTuples or structs =#)
getval(index) = getproperty(arr[index], name)
out = similar(arr, typeof(getval(firstindex(arr))))
for index in eachindex(arr)
out[index] = getval(index)
end
return out
end;
extract(:TPR_exc, perfs);
import PyPlot
[ Info: Precompiling Sciplotlib [61be95e5-9550-4d5f-a203-92a5acbc3116]
using VoltageToMap.Plot
function make_figure(perfs)
xticklabels = [p.sim.input.N_conn for p in paramsets[:,1,1]]
xs = [1:length(xticklabels);]
fig, ax = plt.subplots()
plot_detection_rate(rate; kw...) = plot_samples_and_means(xs, rate, ax; kw...)
plot_detection_rate(extract(:TPR_exc, perfs), label="for excitatory inputs", c=color_exc)
plot_detection_rate(extract(:TPR_inh, perfs), label="for inhibitory inputs", c=color_inh)
plot_detection_rate(extract(:FPR, perfs), label="for unconnected spikers", c=color_unconn)
set(ax; xtype=:categorical, ytype=:fraction, xticklabels, xlabel="Number of connected inputs")
add_α_line(ax, paramsets[1].evaluation.α)
l = ax.legend(title="Detection rate", ncol=2, loc="lower right", bbox_to_anchor=(1.06, 1.1))
l._legend_box.align = "left"
return fig, ax
end;
