MAJOR: removed mex and spline support, rewrote to use spikehist.m ins…

…tead of spikehist_mex.m
pillowlab · Jan 25, 2016 · 47b10ab · 47b10ab
1 parent 987d1b7
commit 47b10ab
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Showing 30 changed files with 26 additions and 1,328 deletions.
diff --git a/glmtools_fitting/Loss_splineNlin_GLMlogli.m b/glmtools_fitting/Loss_splineNlin_GLMlogli.m
diff --git a/glmtools_fitting/MLfit_GLMspline.m b/glmtools_fitting/MLfit_GLMspline.m
diff --git a/glmtools_fitting/MLfit_splineNlin_GLM.m b/glmtools_fitting/MLfit_splineNlin_GLM.m
diff --git a/glmtools_fitting/initfit_sphistDesignMat.m b/glmtools_fitting/initfit_sphistDesignMat.m
@@ -24,23 +24,21 @@
 else Xstruct.ihflag = 1;
 end
 
-% Compute vector binary (logical) spike times 
+% Vector of binary (0 or 1) spike counts
 Xstruct.bsps = sparse(gg.sps>0);
 
 % ---- Create Spike-history Design Matrix ------------------------
 Xsp = zeros(Xstruct.rlen,nh+nh2*nCoupled); % allocate
-twin = [1 Xstruct.rlen]; % time window for convolution (entire length)
 
 % Design matrix for self-coupling filter 
 if nh>0
-    Xsp(:,1:nh) = spikefilt_mex(find(Xstruct.bsps),gg.ihbas,twin);
+    Xsp(:,1:nh) = spikefilt(full(double(gg.sps>0)),gg.ihbas);
 end
 
-% Design matrix fo cross-coupling filters
+% Design matrix for cross-coupling filters
 for jcpl = 1:nCoupled
-    spInds_jcpl = find(gg.sps2(:,jcpl)); % spike times of coupled neuron
     inds = nh+nh2*(jcpl-1)+1:nh+nh2*jcpl; % column indices
-    Xsp(:,inds) = spikefilt_mex(spInds_jcpl,gg.ihbas2,twin); % insert into design matrix
+    Xsp(:,inds) = spikefilt(full(double(gg.sps2(:,jcpl))),gg.ihbas2); % insert into design matrix
 end
 
 % ---- Set fields of Xstruct -------------------------------------

diff --git a/glmtools_fitting/neglogli_GLM.m b/glmtools_fitting/neglogli_GLM.m
@@ -39,7 +39,6 @@
 Itot = kron(I0,ones(upsampfactor,1)) + dc;
 
 % -------------- Compute net h current --------------------------------
-spInds = find(gg.sps);
 
 % Check if post-spike filters are present
 if isempty(gg.ihw), gg.ihbas = []; end
@@ -48,9 +47,9 @@
 % Compute convolution of post-spike filters with spike history
 nCoupled = length(gg.couplednums); % # cells coupled to this one
 if ~isempty(ih)
-    Itot = Itot + spikefilt_mex(spInds,ih(:,1),[1,rlen]);  % self-coupling filter
+    Itot = Itot + spikefilt(gg.sps,ih(:,1));  % self-coupling filter
     for j = 1:nCoupled   % coupling filters from other neurons
-            Itot = Itot + spikefilt_mex(find(gg.sps2(:,j)),ih(:,j+1),[1,rlen]);
+            Itot = Itot + spikefilt(gg.sps2(:,j),ih(:,j+1));
     end
 end
 
@@ -75,7 +74,7 @@
 % Spike-history filter output
 if nargout > 5
     Ih = zeros(length(Itot),1);
-    if ~isempty(ih), Ih = spikefilt_mex(spInds,ih(:,1),[1,rlen]);
+    if ~isempty(ih), Ih = spikefilt(gg.sps,ih(:,1));
     end
 end
 
@@ -84,7 +83,7 @@
     Icpl = zeros(length(Itot),size(ih,2)-1);
     if ~isempty(ih)
         for j = 1:nCoupled
-            Icpl(:,j) = spikefilt_mex(find(gg.sps2(:,j)),ih(:,j+1),[1,rlen]);
+            Icpl(:,j) = spikefilt(gg.sps2(:,j),ih(:,j+1));
         end
     end
 end
diff --git a/glmtools_mex/initialize_mexcode.m b/glmtools_mex/initialize_mexcode.m
diff --git a/glmtools_mex/spikefilt_mex.c b/glmtools_mex/spikefilt_mex.c
diff --git a/glmtools_mex/spikefilt_mex.mexmaci64 b/glmtools_mex/spikefilt_mex.mexmaci64
diff --git a/glmtools_misc/simGLM.m b/glmtools_misc/simGLM.m
@@ -18,7 +18,7 @@
 %              column corresponding to a different pixel / regressor.
 % Output:
 %   tsp - list of spike times (in s)
-%   sps - sparse binary matrix with spike times (at resolution dtSp).
+%   sps - binary matrix with spike times (at resolution dtSp).
 %  Itot - summed filter outputs 
 %  Istm - just the spike-history filter output
 
@@ -60,7 +60,7 @@
 
 % --------------- Set up simulation dynamics variables ---------------
 nsp = 0; % number of spikes
-sps = logical(sparse(rlen,1)); % sparse spike time matrix
+sps = zeros(rlen,1); % sparse spike time matrix
 jbin = 1; % current time bin
 tspnext = exprnd(1);  % time of next spike (in rescaled time)
 rprev = 0;  % Integrated rescaled time up to current point
@@ -174,7 +174,7 @@
 end
 
 % Remove any extra bins from tsp and compute binned spike train 'sps'
-sps = logical(sparse(rlen,ncells));
+sps = zeros(rlen,ncells);
 for jj = 1:ncells
     tsp{jj} = tsp{jj}(1:nsp(jj));
     if ~isempty(tsp{jj})