eLife

Here I go over the scripts that accompied:

Yunzhe Liu, Raymond J Dolan, Cameron Higgins, Hector Penagos, Mark W Woolrich, H Freyja Ólafsdóttir, Caswell Barry, Zeb Kurth-Nelson, Timothy E Behrens (2021) Temporally delayed linear modelling (TDLM) measures replay in both animals and humans eLife https://doi.org/10.7554/eLife.66917

Specifically: https://github.com/YunzheLiu/TDLM/blob/master/Simulate_Replay.m

Original code, has dark background, my julia translation is in grey.

Of course it makes use of the functions within this package:

using TDLM

Training Decoders

Simulating Data

Much of the details of the data simulation has been abstracted away and the functions are availible in the sub-package TDLM.Simulate

using TDLM.Simulate

Add other needed packages:

using Distributions, Lasso

Some parameters:

nSensors = 273;
nStates = 8;
nTrainPerStim = 18;

Here we sample commonPattern from a normal distribution and create copies with 50% noise. See documentation for Simulate.Noise.

commonPattern = randn(1, nSensors);
patterns = repeat(commonPattern, 1, 1, nStates) + Noise();

A special pattern is only noise, therefore zeros concatinated (noise pattern is first).

patterns = cat(zeros(1, nSensors), patterns, dims = 3);

We create samples by adding irreducible error sd = 4, and obtain a three dimensionam matrix with dims: 1. observation, 2. sensor, 3. pattern/stimulus

trainingData = repeat(patterns, nTrainPerStim) + Noise(Normal(0, 4));
size(trainingData) == (nTrainPerStim, nSensors, nStates + 1)

true

Four states get more noise.

trainingData[:, :, sample(1:nStates, 4)] += Noise();

Flatten trainingData (from 3dim to 2dim)

trainingData = reduce(vcat, trainingData[:, :, i] for i in axes(trainingData, 3));

trainingLabels = hcat(repeat((0:nStates), inner = nTrainPerStim));

reduce(hcat, coef(fit(LassoPath,
        trainingData,
        vec(trainingLabels .== i),
        Binomial(); α=1.0, nλ=100), select = MinAICc()) for i in 1:nStates)

274×8 SparseArrays.SparseMatrixCSC{Float64, Int64} with 69 stored entries:
⎡⢩⢏⎤
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⎢⣰⡁⎥
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⎢⠀⠄⎥
⎢⠀⡪⎥
⎢⠸⢅⎥
⎢⠠⠀⎥
⎢⡐⡄⎥
⎢⢰⡀⎥
⎢⠰⡄⎥
⎢⠀⠀⎥
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⎢⢀⠂⎥
⎢⢈⢀⎥
⎣⢀⠀⎦

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