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fit_model.m
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fit_model.m
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% Initialize termination condition (convergence or max iterations)
converged = 0;
% Prior to alternation, fit data with vanilla GLM
X_all = cell2mat(X_cell(:,1,1));
% Include spike history terms if desired
if include_spk_history
X_all = [X_all spk_hist_cov];
end
% Standardize covariates
X_all = stdize(X_all);
% Convert y from cell to matrix
y_all = cell2mat(spikes);
% Which covariates to "warp" - i.e., the experimental ones, not SH
X_col_to_warp = 1:num_exp_cov;
num_BS_GLM = 1; % LEGACY: Number of times to bootstrap the Pseudo-R2 during fitting. Default is 1.
%% Fit initial GLM
iter = 1; % Initialize at iter 1
parfor nrn_idx = 1:length(neurons)
nrn_num = neurons(nrn_idx);
% Define which covariates to include
cov_to_include = [1:num_exp_cov];
% Fit GLM without DTW to get baseline
disp(['Now fitting neuron: ' num2str(nrn_num)])
[predictions_combined{nrn_idx,1}, ...
fit_parameters{nrn_idx,1}, ...
fit_info{nrn_idx,1}, ...
pseudo_R2{nrn_idx,1}] = ...
fit_poiss_GLM( X_all(:,cov_to_include), y_all(:,nrn_num), ...
num_CV, ...
dt, ...
lambda, ... % lambda
alpha, ... % alpha
fit_method, ...
bins_per_trial, ...
num_BS_GLM);
% Fit GLM with spike history but without DTW
if include_spk_history
disp(['Now fitting neuron: ' num2str(nrn_num) ' with spike history.'])
cov_to_include = [1:num_exp_cov idx_spk_hist_cov{nrn_num}]; % Define which covariates to include
[predictions_combined_SH{nrn_idx,1}, ...
fit_parameters_SH{nrn_idx,1}, ...
fit_info_SH{nrn_idx,1}, ...
pseudo_R2_SH{nrn_idx,1}] = ...
fit_poiss_GLM( X_all(:,cov_to_include), y_all(:,nrn_num), ...
num_CV, ...
dt, ...
lambda, ... % lambda
alpha, ... % alpha
fit_method, ...
bins_per_trial, ...
num_BS_GLM);
end
end
% Transfer some stuff
if ~include_spk_history
predictions_combined_SH = predictions_combined;
end
for nrn_idx = 1:length(neurons)
% Break up predictions for all trials into trialwise predictions
predictions(:,nrn_idx,1) = mat2cell(nansum(cell2mat(predictions_combined{nrn_idx,1}),1)', ...
bins_per_trial,1);
predictions_SH(:,nrn_idx,1) = mat2cell(nansum(cell2mat(predictions_combined_SH{nrn_idx,1}),1)', ...
bins_per_trial,1);
predictions2(:,nrn_idx,1) = mat2cell(nansum(cell2mat(predictions_combined{nrn_idx,1}),1)', ...
bins_per_trial,1);
end
% Calculate LLHD for initial GLM
opt.type = 'poisson';
[LLHD_total(1), LLHD_mean(1)] = calc_LLHD(y_all,predictions_combined(:,1),opt);
%% Now do alternation: 1) fit DTW, 2) fit GLM
while ~converged && (iter < max_iter)
% Update iteration counter
iter = iter + 1;
disp(['Beginning iteration: ' num2str(iter)])
weights = mean(y_all,1); % Avg firing rates across neurons
% Fit DTW
[acc_matrix(:,:,iter), cost_matrix(:,:,iter), ...
warp_path(:,:,iter), warp_path_matrix(:,:,iter), ...
WV_folds{iter}, ...
LLHD_test(:,:,iter), LLHD_test_diag(:,:,iter)] = ...
align_signals_trialwise_WV( ...
predictions2(:,:,iter-1), spikes_warped(:,:,1), transition_priors, weights, num_WV); % Fit new warp to data from last iteration
% Apply warp to original covariates
X_cell(:,:,iter) = apply_warp_cov_trialwise_WV( ...
warp_path_matrix(:,:,iter), X_cell(:,:,1), X_col_to_warp);
y_all = cell2mat(spikes);
% Fit GLM to all neurons by WV fold
for WV = 1:num_WV
disp(['Iteration: ' num2str(iter) ', WV: ' num2str(WV)])
wv_test_nrns = find(WV_folds{iter}(WV).test);
X_all = cell2mat(X_cell(:,WV,iter));
X2 = cell2mat(X_cell(:,1,1));
% Standardize covariates for regularization
X_all = stdize(X_all);
X2 = stdize(X2);
% Fit GLM (test set neurons for given warp validation)
parfor nrn_idx = 1:length(wv_test_nrns)
nrn_num = wv_test_nrns(nrn_idx);
y = y_all(:,nrn_num);
% Choose exp covariates only
cov_to_use = [1:num_exp_cov];
% Fit GLM
disp(['Now fitting neuron: ' num2str(wv_test_nrns(nrn_idx))])
[predictions_combined_temp{nrn_idx,1}, ... % These indexes are fine; just have to use 1 instead of iter for parfor reasons
fit_parameters_temp{nrn_idx,1}, ...
fit_info_temp{nrn_idx,1}, ...
pseudo_R2_temp{nrn_idx,1}] = ...
fit_poiss_GLM( X_all(:,cov_to_use), y, ...
num_CV, ...
dt, ...
lambda, ... % lambda
alpha, ... % alpha
fit_method, ...
bins_per_trial, ...
num_BS_GLM);
end
% Transfer some stuff
for nrn_idx = 1:length(wv_test_nrns)
nrn_num = wv_test_nrns(nrn_idx);
cov_to_use = [1:num_exp_cov];
% Make predictions using WEIGHTS from current GLM fit but NOT
% covariates from current GLM fit
predictions_combined2_temp = predictions_combined_temp;
for cv = 1:num_CV
idx = ~isnan(predictions_combined_temp{nrn_idx}{cv}); % Find data points in test set
predictions_combined2_temp{nrn_idx,1}{cv}(idx) = glmval( ... % Predict on test data
fit_parameters_temp{nrn_idx,1}{cv}, ...
X2(idx,cov_to_use), ...
'log', ...
'constant', 'on', ...
'offset',repmat(log(dt),size(X2(idx,1))));
end
% Replace everything as needed
predictions_combined{nrn_num,iter} = predictions_combined_temp{nrn_idx,1};
predictions_combined2{nrn_num,iter} = predictions_combined2_temp{nrn_idx,1};
fit_parameters{nrn_num,iter} = fit_parameters_temp{nrn_idx};
fit_info{nrn_num,iter} = fit_info_temp{nrn_idx};
pseudo_R2{nrn_num,iter} = pseudo_R2_temp{nrn_idx};
% Break up predictions for all trials into trialwise predictions
predictions(:,nrn_num,iter) = mat2cell(nansum(cell2mat(predictions_combined{nrn_num,iter}),1)', ...
bins_per_trial,1);
predictions2(:,nrn_num,iter) = mat2cell(nansum(cell2mat(predictions_combined2{nrn_num,iter}),1)', ...
bins_per_trial,1);
end
end
% Calculate LLHD
[LLHD_total(iter), LLHD_mean(iter)] = calc_LLHD(y_all,predictions_combined(:,iter),opt);
% Determine if fit has coverged
% Choose tolerance calculation type
LL0 = LLHD_mean(iter-1);
LL1 = LLHD_mean(iter);
switch tol_type
case 'relTol'
delta = abs(LL1 - LL0) / min(abs(LL0), abs(LL1));
converged = delta < conv_thresh;
case 'absTol'
delta = abs(LL1 - LL0);
converged = delta < conv_thresh;
case 'maxIter'
delta = 0;
converged = (iter >= max_iter);
end
disp(['Model convergence: ' num2str(converged) '. Delta: ' num2str(delta) '. Using ' tol_type])
end
%% If including spike history
if include_spk_history
% Increment iter so as not to overwrite last one
iter = iter + 1;
% Keep DTW info from last iter (iter-1). Not fitting DTW again.
acc_matrix(:,:,iter) = acc_matrix(:,:,iter-1);
cost_matrix(:,:,iter) = cost_matrix(:,:,iter-1);
warp_path(:,:,iter) = warp_path(:,:,iter-1);
warp_path_matrix(:,:,iter) = warp_path_matrix(:,:,iter-1);
WV_folds{iter} = WV_folds{iter-1};
LLHD_test(:,:,iter) = LLHD_test(:,:,iter-1);
LLHD_test_diag(:,:,iter) = LLHD_test_diag(:,:,iter-1);
X_cell(:,:,iter) = X_cell(:,:,iter-1);
% Fit GLM to all neurons by WV fold
for WV = 1:num_WV
disp(['Iteration: ' num2str(iter) ', WV: ' num2str(WV)])
wv_test_nrns = find(WV_folds{iter}(WV).test);
% Form covariate matrix
X_all = cell2mat(X_cell(:,WV,iter));
% Include spike history terms
X_all = [X_all spk_hist_cov];
% Fit GLM (test set neurons for given warp validation)
parfor nrn_idx = 1:length(wv_test_nrns)
nrn_num = wv_test_nrns(nrn_idx);
y = y_all(:,nrn_num);
% Overhead for spike history terms
cov_to_use = [1:num_exp_cov, idx_spk_hist_cov{nrn_num}];
% Fit GLM
disp(['Now fitting neuron: ' num2str(wv_test_nrns(nrn_idx))])
[predictions_combined_temp{nrn_idx,1}, ... % These indexes are fine; just have to use 1 instead of iter for parfor reasons
fit_parameters_temp{nrn_idx,1}, ...
fit_info_temp{nrn_idx,1}, ...
pseudo_R2_temp{nrn_idx,1}] = ...
fit_poiss_GLM( X_all(:,cov_to_use), y, ...
num_CV, ...
dt, ...
lambda, ... % lambda
alpha, ... % alpha
fit_method, ...
bins_per_trial, ...
num_BS_GLM);
end
% Transfer some stuff
for nrn_idx = 1:length(wv_test_nrns)
nrn_num = wv_test_nrns(nrn_idx);
cov_to_use = [1:num_exp_cov, idx_spk_hist_cov{nrn_num}];
predictions_combined2_temp = predictions_combined_temp;
% Replace everything as needed
predictions_combined{nrn_num,iter} = predictions_combined_temp{nrn_idx,1};
predictions_combined2{nrn_num,iter} = predictions_combined2_temp{nrn_idx,1};
fit_parameters{nrn_num,iter} = fit_parameters_temp{nrn_idx};
fit_info{nrn_num,iter} = fit_info_temp{nrn_idx};
pseudo_R2{nrn_num,iter} = pseudo_R2_temp{nrn_idx};
% Break up predictions for all trials into trialwise predictions
predictions(:,nrn_num,iter) = mat2cell(nansum(cell2mat(predictions_combined{nrn_num,iter}),1)', ...
bins_per_trial,1);
predictions2(:,nrn_num,iter) = mat2cell(nansum(cell2mat(predictions_combined2{nrn_num,iter}),1)', ...
bins_per_trial,1);
end
end
% Calculate LLHD
[LLHD_total(iter), LLHD_mean(iter)] = calc_LLHD(y_all,predictions_combined(:,iter),opt);
end
%% If doing shuffle control
% Shuffle trials for shuffle control. Will apply warp to "wrong trials"
% with the same reach direction
if do_shuffle
% Increment iter so as not to overwrite last one
iter = iter + 1;
% Keep DTW info from last iter (iter-1). Not fitting DTW again.
acc_matrix(:,:,iter) = acc_matrix(:,:,iter-1);
cost_matrix(:,:,iter) = cost_matrix(:,:,iter-1);
warp_path(:,:,iter) = warp_path(:,:,iter-1);
warp_path_matrix(:,:,iter) = warp_path_matrix(:,:,iter-1);
warp_path_matrix_shuffle(:,:,iter) = warp_path_matrix(:,:,iter-1);
WV_folds{iter} = WV_folds{iter-1};
LLHD_test(:,:,iter) = LLHD_test(:,:,iter-1);
LLHD_test_diag(:,:,iter) = LLHD_test_diag(:,:,iter-1);
X_cell(:,:,iter) = X_cell(:,:,iter-1);
X_shuffle = X_cell;
% Find all of the binned reach directions (e.g., 1:8, -8:8)
possible_reach_dirs = unique(cell2mat(Data_win.target_dir_num));
% For each of these, shuffle the covariate matrices
for dir_reach_idx = 1:length(possible_reach_dirs);
dir_reach = possible_reach_dirs(dir_reach_idx);
trials_to_shuffle = find(cell2mat(Data_win.target_dir_num)==dir_reach); % Find the trials with this reach direction
idx_shuffle = trials_to_shuffle(randperm(length(trials_to_shuffle))); % Mix up the trial numbers
warp_path_matrix_shuffle(trials_to_shuffle,:,iter) = warp_path_matrix_shuffle(idx_shuffle,:,iter);
end
% Apply shuffled warp matrices to trials
X_shuffle(:,:,iter) = apply_warp_cov_trialwise_WV( ...
warp_path_matrix_shuffle(:,:,iter), X_cell(:,:,1), X_col_to_warp);
% Fit GLM to all neurons by WV fold
for WV = 1:num_WV
disp(['Iteration: ' num2str(iter) ', WV: ' num2str(WV)])
wv_test_nrns = find(WV_folds{iter}(WV).test);
X_all = cell2mat(X_shuffle(:,WV,iter));
% Fit GLM (test set neurons for given warp validation)
parfor nrn_idx = 1:length(wv_test_nrns)
nrn_num = wv_test_nrns(nrn_idx);
y = y_all(:,nrn_num);
% Overhead for spike history terms
cov_to_use = [1:num_exp_cov];
% Fit GLM
disp(['Now fitting neuron: ' num2str(wv_test_nrns(nrn_idx))])
[predictions_combined_temp{nrn_idx,1}, ... % These indexes are fine; just have to use 1 instead of iter for parfor reasons
fit_parameters_temp{nrn_idx,1}, ...
fit_info_temp{nrn_idx,1}, ...
pseudo_R2_temp{nrn_idx,1}] = ...
fit_poiss_GLM( X_all(:,cov_to_use), y, ...
num_CV, ...
dt, ...
lambda, ... % lambda
alpha, ... % alpha
fit_method, ...
bins_per_trial, ...
num_BS_GLM);
end
% Transfer some stuff
for nrn_idx = 1:length(wv_test_nrns)
nrn_num = wv_test_nrns(nrn_idx);
% If including spike history terms
if include_spk_history
cov_to_use = [1:num_exp_cov, idx_spk_hist_cov{nrn_num}];
end
predictions_combined2_temp = predictions_combined_temp;
% Replace everything as needed
predictions_combined{nrn_num,iter} = predictions_combined_temp{nrn_idx,1};
predictions_combined2{nrn_num,iter} = predictions_combined2_temp{nrn_idx,1};
fit_parameters{nrn_num,iter} = fit_parameters_temp{nrn_idx};
fit_info{nrn_num,iter} = fit_info_temp{nrn_idx};
pseudo_R2{nrn_num,iter} = pseudo_R2_temp{nrn_idx};
% Break up predictions for all trials into trialwise predictions
predictions(:,nrn_num,iter) = mat2cell(nansum(cell2mat(predictions_combined{nrn_num,iter}),1)', ...
bins_per_trial,1);
predictions2(:,nrn_num,iter) = mat2cell(nansum(cell2mat(predictions_combined2{nrn_num,iter}),1)', ...
bins_per_trial,1);
end
end
end
% Calculate LLHD
[LLHD_total(iter), LLHD_mean(iter)] = calc_LLHD(y_all,predictions_combined(:,iter),opt);
pr2 = cell2mat(pseudo_R2(:,1));
[~,idx_sort] = sort(weights,'descend');
%% Bookkeeping for simulation repetitions
if ~real_data
% Save simulated (ground truth) and inferred DTW parameters
Results(1).warp_matrices_sim = cellfun(@sparse,Data_win.warp_matrices,'UniformOutput',false); % Save as sparse matrix
Results(rep).warp_matrices_inf = cellfun(@sparse,warp_path_matrix,'UniformOutput',false); % Save as sparse matrix
% Save simulated (ground truth) GLM parameters
Results(1).beta_sim = Data_win.parameters.beta(nrn_to_use_orig_idx,:); % Get the sim parameters from the *actually chosen* simulated neurons
% Collect inferred GLM parameters
beta_inferred = nan*Data_win.parameters.beta(nrn_to_use_orig_idx,:); % initialize
beta_inferred = repmat(beta_inferred,[1 1 num_CV]); % make a copy for each cv fold
beta_inferred_noDTW = beta_inferred;
for nrn_idx = 1:length(neurons)
nrn_num = neurons(nrn_idx);
for cv = 1:num_CV
% Model with DTW
fit_param_temp = fit_parameters{nrn_num,iter}{cv}; % stored as column (bad design, i know)
fit_param_temp = fit_param_temp'; % convert to row
beta_inferred(nrn_num,:,cv) = fit_param_temp;
% Model without DTW
fit_param_temp2 = fit_parameters{nrn_num,1}{cv}; % stored as column (bad design, i know)
fit_param_temp2 = fit_param_temp2'; % convert to row
beta_inferred_noDTW(nrn_num,:,cv) = fit_param_temp2;
end
end
Results(rep).beta_inf = beta_inferred;
Results(rep).beta_inf_noDTW = beta_inferred_noDTW;
end
% Save results for analyses
description.num_nrn = num_nrn;
description.num_trials = num_trials;
description.sim_file = data_fname;
parameters.transition_priors = transition_priors;
parameters.transition_prior_scale = transition_prior_scale;
parameters.fit_method = fit_method;
parameters.num_CV = num_CV;
parameters.num_WV = num_WV;
parameters.dt = dt;
parameters.num_iter = iter;
parameters.max_iter = max_iter;
parameters.tol_type = tol_type;
parameters.conv_thresh = conv_thresh;
parameters.delta = delta;
parameters.LLHD_mean = LLHD_mean;
parameters.alpha = alpha;
parameters.lambda = lambda;
parameters.do_shuffle = do_shuffle;
parameters.include_spk_history = include_spk_history;
parameters.num_spk_history_bf = num_spk_history_bf;
Results(1).description = description;
Results(1).parameters = parameters;
% Save results
if ~quest
save([Results_fpath Results_fname],'Results','-v7.3')
else
save(Results_fname,'Results','-v7.3')
end
disp(['Results file saved: ' Results_fname])