function[obj] = deconvolve(obj, settings)

%% Perform pupil deconvolution based on the methods
%  described in Wierda et al PNAS 2012

o=optimset;
o.MaxFunEvals = 10000;
o.MaxIter = 10000;

fs = 50; % Hz downsampling

y = obj.data.baseline_corrected.interpolated;

t_orig = obj.time;

nt = round((t_orig(end) - t_orig(1)) / (1000/fs));
t_ds = linspace(t_orig(1), t_orig(end), nt);
yq = interp1(t_orig, y, t_ds);


obj.deconvolution.time  = t_ds;
obj.deconvolution.input = yq;

init_params = [ 1 zeros(1,length(obj.labels)-1) ];
init_slope = (obj.deconvolution.input(end) -obj.deconvolution.input(1)) / length(t_ds);

%% perform search with initial params : slope = 0; pulse weight 1

final_params = fminsearch(@obj.evaluate_model, [init_slope init_params], o);

obj.deconvolution.params = final_params;
obj.deconvolution.output = obj.prf_convolve(obj.stick_model(final_params(2:end)),final_params(1));