Detailed analysis of trajectories in the Morris water maze (Gehring et al. 2015)

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Accession:185090
MATLAB code that can be used for detailed behavioural analyzes of the trajectories of animals be means of a semi-supervised clustering algorithm. The method is applied here to trajectories in the Morris Water Maze (see Gehring, T. V. et al., Scientific Reports, 2015) but the code can easily be adapted to other types experiments. For more information and the latest version of the code please refer to https://bitbucket.org/tiagogehring/mwm_trajectories
Reference:
1 . Gehring TV, Luksys G, Sandi C, Vasilaki E (2015) Detailed classification of swimming paths in the Morris Water Maze: multiple strategies within one trial. Sci Rep 5:14562 [PubMed]
Model Information (Click on a link to find other models with that property)
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Simulation Environment: MATLAB;
Model Concept(s): Methods;
Implementer(s):
function MM=robustcsvread(filename, varargin)
% ROBUSTCSVREAD reads in CSV files with
% different number of columns on different
% lines
%
% This can easily be extended to give back
% a numeric matrix or mixed numeric and 
% string cell array.
%
% IT'S NOT FANCY, BUT IT WORKS

% robbins@bloomberg.net
% michael.robbins@us.cibc.com

% Tiago Gehring, Mar/2015: added delimiter options, started messing the
% code. Figured why not?
[line_delim, delim] = process_options(varargin, ...
            'LineDelimiter', '\n', 'Delimiter', ',' ...
          );            

fid=fopen(filename,'r');
slurp=fscanf(fid,'%c');
slurp=regexprep(slurp,'\t',',');
fclose(fid);

M = strread(slurp,'%s','delimiter', line_delim);
for i=1:length(M)
    temp=strread(M{i},'%s','delimiter', delim);
    for j=1:length(temp)
        MM{i,j}=temp{j};
    end
end

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