Kristian Löwe's Web Pages

Software


conan
CONnectivity ANalysis tools in Matlab
Languages used:
C, Matlab
Supported OSs:
64-bit Linux
Repository:
https://github.com/kloewe/conan
References: Loewe K, Donohue SE, Schoenfeld MA, Kruse R, Borgelt C (2016). Memory-efficient analysis of dense functional connectomes. Frontiers in Neuroinformatics 10:50. frontiersin.org
Loewe K, Grueschow M, Stoppel CM, Kruse R, Borgelt C (2014). Fast construction of voxel-level functional connectivity graphs. BMC Neuroscience 15:78. doi


util-m
Matlab utilities
Languages used:
Matlab
Supported OSs:
64-bit Linux
Download:
util-m-20161110.tar.gz (2b63e3a)
Repository:
https://github.com/kloewe/util-m
Functions:
util-m/
   flat.m flatten an array of data
util-m/data/
   clipData.m clip data according to the specified clipping limits
   cutData.m cut data according to the specified limits
   rescaleData.m rescale (linearly transform) data to the specified range
util-m/file/
   fileCountLines.m count the number of lines in a file
   fileExists.m check if a given file exists
   fileGetDir.m get directory from full path
   fileGetExt.m get extension from full path
   fileGetName.m get filename from full path
   fileGlob.m filename expansion
util-m/mri/
   readImgData.m read neuroimaging data
   readImgHdr.m read header information for neuroimaging data
   writeImgData.m write neuroimaging data to a NIFTI file
util-m/perf/
   getPageSize.m get page size using getconf
   monMem.m monitor memory usage using the proc filesystem
   monPerf.m monitor performance based on event counters using perf
util-m/plot/
   blend.m composite image A over image B
   pngcolorbar.m save a colorbar in png format
util-m/pool/
   poolmgr.m unified parallel pool management across Matlab versions
util-m/ui/
   drawOutline.m draw 2D outline
   getAxesBelowPointer.m get axes below the mouse pointer


cpuinfo-m
Matlab tools for processor information queries
Languages used:
C, Matlab
Supported OSs:
64-bit Linux
Download:
cpuinfo-m-20161125.tar.gz (7c03845)
Repository:
https://github.com/kloewe/cpuinfo-m
Functions:
cpuinfo-m/
   corecnt.m determine the number of processor cores
   cpuinfo.m linear index (wrt upper triangle) from matrix subscripts
   proccnt.m determine the number of logical processors


corr-m
Matlab tools for computing pairwise correlations from fMRI data
Facilitates fast correlation computation for pairwise internodal functional connectivity estimation based on fMRI data. So far, two measures of association are supported: Pearson's r and the tetrachoric correlation coefficient. With Pearson's r, efficient implementation (based on CPU instruction set extensions) and parallelization yield speedups up to ~3x compared to Matlab's corrcoef (R2011b). With the tetrachoric correlation coefficient, data reduction in the temporal domain based on binarization of nodal time series combined with tetrachoric correlation estimation and efficient implementation (based on a 16-bit lookup table or CPU instruction set extensions) and parallelization yield speedups up to ~20x compared to Matlab's corrcoef (R2011b).
Languages used: C, Matlab
Supported OSs: 64-bit Linux
Download: corr-m-0.4.1.tar.gz (2016-02-17)
Repository: https://github.com/kloewe/corr-m
Functions:
corr-m/
   pcc.m compute pairwise Pearson correlation coefficients
   sub2utm.m linear index (wrt upper triangle) from matrix subscripts
   tetracc.m compute pairwise tetrachoric correlation coefficients
   toSymMat.m create symmetric matrix from upper triangular elements
References: Loewe K, Grueschow M, Stoppel CM, Kruse R, Borgelt C (2014). Fast construction of voxel-level functional connectivity graphs. BMC Neuroscience 15:78. doi


coconad-m
CoCoNAD for Matlab/Octave
CoCoNAD (Continuous-time Closed Neuron Assembly Detection) is a method to find frequent synchronous joint events in parallel point processes, which has applications in the analysis of parallel spike trains. The idea is to provide a method to test the temporal coincidence coding hypothesis, that is, that stimuli are encoded by temporally coincident spiking of groups of neurons, sometimes called cell assemblies.
Languages used: C, Matlab
Supported OSs: 64-bit Linux
Download: coconad-m-0.9.1.tar.gz (2016-02-12)
Functions:
coconad-m/
   coconad.m continuous-time closed neuron assembly detection
   estpsp.m estimate a pattern spectrum from original data
   genpsp.m generate a pattern spectrum from surrogate data
   patred.m pattern set reduction based on a preference relation
   pats2file.m export patterns to text file
   psp2bdr.m extract a decision border from a pattern spectrum
   readTrains.m read spike trains from file
See also: CoCoNAD is also available for Python (PyCoCo), R (CoCo4R), Java (JNICoCo), and as a command line program. A GUI based on CoCoNAD for Java (CoCoGUI) is available here.
References: Picado-Muiño D, Borgelt C (2014). Frequent item set mining for sequential data: synchrony in neuronal spike trains. Intelligent Data Analysis 18(6):997-1012. doi
Borgelt C, Picado-Muiño D (2013). Finding frequent patterns in parallel point processes. Proc. 12th Int. Symposium on Intelligent Data Analysis, 116-126. doi
Picado-Muiño D, Borgelt C, Berger D, Gerstein G, Grün S (2013). Finding neural assemblies with frequent item set mining. Frontiers in Neuroinformatics 7:9. doi
Torre E, Picado-Muiño D, Denker M, Borgelt C, Grün S (2013). Statistical evaluation of synchronous spike patterns extracted by frequent itemset mining. Frontiers in Computational Neuroscience, 7:132. doi
Borgelt C, Picado-Muiño D (2014). Simple pattern spectrum estimation for fast pattern filtering with CoCoNAD. Proc. 13th Int. Symposium on Intelligent Data Analysis, 37-48. doi



License

All programs are free and open source software. Which license applies depends on the module. The license file for each module can be found in the directory <module-name>/doc in the archive of the programs.