- #Matlab random permute how to#
- #Matlab random permute series#
The most common method involves obtaining a single number statistic quantifying a specific feature of the peri-event dF, such as the Area Under the Curve (AUC) or peak dF. To determine the presence of ERT, the dF around defined events can be collated and analyzed.
#Matlab random permute how to#
Like all analysis strategies, the experimenter is confronted with a variety of choices such as whether to select these strategies before ( a priori) or after ( post hoc) data collection, how to avoid Type 1 (false positive) errors whilst achieving appropriate power to avoid Type II errors (false negative).
#Matlab random permute series#
The recording time series is typically normalized into a delta F (dF) to represent relative activity change. A biosensor readout is a proxy for some underlying biological process (receptor binding, action potential, etc.,), so units of measurement are generally arbitrary. We introduce the rationale behind these approaches, describe the results of Monte Carlo simulations evaluating their effectiveness at controlling Type I and Type II error rates, and offer some recommendations for selecting appropriate analysis strategies for fiber photometry experiments.Ī widespread issue faced by researchers when using fiber photometry is how to best analyze the rich datasets they produce. We highlight the various issues with this approach and overview straightforward alternatives: waveform confidence intervals (CIs) and permutation tests. A simple and popular approach to identifying ERT is to summarize peri-event signal and perform standard analyses on this summary statistic. A focus of this technique is to identify functionally-relevant changes in activity around particular environmental and/or behavioral events, i.e., event-related activity transients (ERT).
School of Psychology, University of New South Wales, Sydney, NSW, Australiaįiber photometry has enabled neuroscientists to easily measure targeted brain activity patterns in awake, freely behaving animal. You could probably do a more efficient algorithm by writing a C MEX function that operates completely in place, but I assume you'd rather not do that.Philip Jean-Richard-dit-Bressel *, Colin W. The algorithm is going to have to visit each memory location at least twice. Yes, that's two loops with 60,000 and 80,000 iterations each, but internally that's going to have to happen regardless. This way you'll only be creating temporary variables that are, at worst, 80,000 by 1. However, I believe if you do randperm along rows after that, you'll end up with a fully permuted matrix. I think you were on the right track by operating on columns, but the problem is that it will only scramble along columns. In the case of the MATLAB solution, I think you'll be possibly creating two extra temporary copies, depending on how reshape works internally. Since this is a huge matrix, that's pretty painful. (Permuting the rows just means reordering the papers.) As Jonathan pointed out, this has the advantage of not making a new copy of the whole matrix, and it sounds like the other options all will.īoth solutions above are great, and will work, but I believe both will involve making a completely new copy of the entire matrix in memory while doing the work. I think that just permuting the columns will work, then, because each paper gets assigned a random set of term frequencies. And I want to generate a random set of papers to compare to. I'm using pdist to calculate cosine similarities between all pairs of papers. Each column corresponds to one term in the vocabulary. Thanks for all the thoughtful answers! Some more info: the rows of the matrix represent documents, and the data in each row is a vector of tf-idf weights for that document. I've also been working with numpy/scipy, so if you know of a good option in python, that would be great as well. (Or, I could equally well do rows.) Since this involves a loop with 60K iterations, I'm wondering if anyone can suggest a more efficient option? 
I believe it'll work if I loop over the columns, and use randperm to randomly permute each column. 80,000 X 60,000), and I basically want to scramble all the entries (that is, randomly permute both rows and columns independently).
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