### Table of Contents

# convones

## Motivation

The main motivation to use convones is to have a faster routine for the special case where one of the arguments of a one-dimensional convolution is a series of all-1 values.

## Reference

convones - convoles a function with a number of consecutive ones FORMAT: cfunc = convones(f, cnum [, w]) Input fields: f function to convolve cnum number of consecutive ones to use w if 1x1 true, weight (divide by number of samples) Output fields: cfunc ones-convolved function See also conv

## Used by

The main internal use for this function is the creation of design matrices. Opposed to SPM (which typically uses a 16 timebin-per-TR resolution), the design matrix creation in NeuroElf uses a millisecond resolution. While differences between convolved time courses are small (the HRF does not have frequency components for which the maximally occurring shift would matter much w.r.t. their phase), I like the idea of being precise wherever possible. And since onset times are commonly given in milliseconds (and people seem to make a whole lot of fuss about stimulus timing these days), I figured it would be nice to have this implemented…

## Exemplary use

- conv_ones_rectwindow.m
% create a large gaussian kernel k = smoothkern(2000); % convolve with a all-ones vector of equal length tic, ko = convones(k, numel(k)); toc % reference: Elapsed time is 0.024565 seconds. % and now the same with conv tic, koc = conv(k, ones(numel(k), 1)); toc % reference: Elapsed time is 0.240042 seconds.

Note: the difference gets larger the longer the vectors become!!