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nncorr

Crross correlation between neural network time series

Syntax

```nncorr(a,b,maxlag,'flag') ```

Description

`nncorr(a,b,maxlag,'flag')` takes these arguments,

 `a` Matrix or cell array, with columns interpreted as timesteps, and having a total number of matrix rows of `N`. `b` Matrix or cell array, with columns interpreted as timesteps, and having a total number of matrix rows of `M`. `maxlag` Maximum number of time lags `flag` Type of normalization (default = `'none'`)

and returns an `N`-by-`M` cell array where each `{i,j}` element is a `2*maxlag+1` length row vector formed from the correlations of `a` elements (i.e., matrix row) `i` and `b` elements (i.e., matrix column) `j`.

If `a` and `b` are specified with row vectors, the result is returned in matrix form.

The options for the normalization `flag` are:

• `'biased'` — scales the raw cross-correlation by 1/N.

• `'unbiased'` — scales the raw correlation by `1/(N-abs(k))`, where `k` is the index into the result.

• `'coeff'` — normalizes the sequence so that the correlations at zero lag are 1.0.

• `'none'` — no scaling. This is the default.

Examples

Here the autocorrelation of a random 1-element, 1-sample, 20-timestep signal is calculated with a maximum lag of 10.

```a = nndata(1,1,20) aa = nncorr(a,a,10) ```

Here the cross-correlation of the first signal with another random 2-element signal are found, with a maximum lag of 8.

```b = nndata(2,1,20) ab = nncorr(a,b,8) ```

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