Consider citing the following publications if you have used any of the related functions in your work:
- J. Friedrich, P. Zhou, and L. Paninski, “Fast online deconvolution of calcium imaging data,” PLOS Computational Biology, vol. 13, iss. 3, p. e1005423, 2017.
- H. V. Ribeiro, M. Jauregui, L. Zunino, and E. K. Lenzi, “Characterizing time series via complexity-entropy curves,” PHYSICAL REVIEW E, vol. 95, 2017.
- N. Marshall, N. M. Timme, N. Bennett, M. Ripp, E. Lautzenhiser, and J. M. Beggs, “Analysis of Power Laws, Shape Collapses, and Neural Complexity: New Techniques and MATLAB Support via the NCC Toolbox,” Frontiers in Physiology, vol. 7, iss. 163, pp. 1-18, 2016.
- T. Deneux, A. Kaszas, G. Szalay, G. Katona, T. Lakner, A. Grinvald, B. Rózsa, and I. Vanzetta, “Accurate spike estimation from noisy calcium signals for ultrafast three-dimensional imaging of large neuronal populations in vivo,” Nature Communications, vol. 7, p. 12190, 2016.
- J. G. Orlandi, O. Stetter, J. Soriano, T. Geisel, and D. Battaglia, “Transfer Entropy Reconstruction and Labeling of Neuronal Connections from Simulated Calcium Imaging,” PLoS ONE, vol. 9, iss. 6, p. e98842, 2014.
- M. Gavish and D. L. Donoho, “The Optimal Hard Threshold for Singular Values is 4/sqrt(3),” , 2013.
- M. Geissbuehler and T. Lasser, “How to display data by color schemes compatible with red-green color perception deficiencies,” Optics Express, vol. 21, iss. 8, p. 9862, 2013.
- O. Stetter, D. Battaglia, J. Soriano, and T. Geisel, “Model-Free Reconstruction of Excitatory Neuronal Connectivity from Calcium Imaging Signals,” PLoS Computational Biology, vol. 8, iss. 8, p. e1002653, 2012.
- J. T. Vogelstein, A. M. Packer, T. A. Machado, T. Sippy, B. Babadi, R. Yuste, and L. Paninski, “Fast nonnegative deconvolution for spike train inference from population calcium imaging.,” Journal of neurophysiology, vol. 104, iss. 6, pp. 3691-3704, 2010.
- B. F. Grewe, D. Langer, H. Kasper, B. M. Kampa, and F. Helmchen, “High-speed in vivo calcium imaging reveals neuronal network activity with near-millisecond precision.,” Nature Methods, vol. 7, iss. 5, pp. 399-405, 2010.
- E. A. Mukamel, A. Nimmerjahn, and M. J. Schnitzer, “Automated Analysis of Cellular Signals from Large-Scale Calcium Imaging Data,” Neuron, vol. 63, iss. 6, pp. 747-760, 2009.
- H. Shimazaki and S. Shinomoto, “A Method for Selecting the Bin Size of a Time Histogram,” Neural Computation, vol. 19, iss. 6, pp. 1503-1527, 2007.
- G. Tononi, O. Sporns, and G. M. Edelman, “A measure for brain complexity: Relating functional segregation and integration in the nervous system,” Neurobiology, vol. 91, pp. 5033-5037, 1994.
- MATLAB v9.0.0
- System Identification Toolbox v9.4
- Image Processing Toolbox v7.2
- Statistics and Machine Learning Toolbox v10.2
- Fuzzy Logic Toolbox v2.2.23
- Computer Vision System Toolbox v7.1
- Econometrics Toolbox v3.4
- Parallel Computing Toolbox v6.8
- MATLAB Distributed Computing Server 6.8
External MATLAB Toolboxes
External MATLAB functions
- Java Tree Wrapper
- Drag & Drop functionality for JAVA GUI components
- ametrine, isolum, morgenstemning colormaps
- Histogram Binwidth Optimization
- IoSR Toolbox
- Fast SVD and PCA
- Schmitt trigger
- Raster Plotting
- Toolbox signal
- Toolbox sparse optimization
Peer-reviewed codes, toolboxes and algorithms
- For the complete list of external licenses check the licenses folder within NETCAL
- Yair Altman from Undocumented MATLAB, most GUI-features would not have been possible without his website and codes