- Neural Spike Train Analysis Toolbox (nSTAT)
An object-oriented Matlab toolbox to analyze, decode and visualize neural spike trains and other signals. nSTAT provides both point process and Gaussian models and algorithms for neural data analysis, such as routines for point process generalized linear model estimation; simulation of spike trains with specified parameters; goodness of fit tests such as the time-rescaling theorem; adaptive point process, Poisson and Gaussian (Kalman) filters; and a range of other tools.
If you use this code, please cite the following paper:
I. Cajigas, W. Q. Malik, and E. N. Brown, "nSTAT: Open-source neural spike train analysis toolbox for Matlab," J. Neurosci. Meth., vol. 211, no. 2, pp. 245-264, Nov. 2012 
The current release version, including all help files and example data files, can be downloaded here: nSTAT release.
The most up-to-date source code is available at nSTAT source on GitHub. Please use this repository for contributing code and tracking issues.
nSTAT is protected by the GPL Open Source License.
- Steady-State Kalman Filter
A computationally efficient implementation of the Kalman filter applicable to time-invariant systems that reduces the algorithmic complexity from O(n3) for the standard Kalman filter to O(n), where n is the dimensionality of the observation vector. Efficient filter calibration routines are included for least-squares estimation of state-space system matrices and closed-form estimation of steady-state Kalman filter gain matrix using the eigenstructure method.
If you use this code, please cite the following paper:
W. Q. Malik, W. Truccolo, E. N. Brown, and L. R. Hochberg, "Efficient decoding with steady-state Kalman filter in neural interface systems," IEEE Trans. Neural Syst. Rehabil. Eng., vol. 19, no. 1, pp.25-34, Feb. 2011
Software download link
- Image Denoising
A low-complexity algorithm for removal of structured (colored) noise from a time series, suitable for high-throughput analysis and denoising of neural imaging data with physiologic noise and artifacts, such as two-photon calcium imaging and fMRI data. This approach uses a signal-plus-colored-noise model and a rapidly convergent cyclic descent procedure for parameter estimation.
If you use this code, please cite the following paper:
W. Q. Malik, J. Schummers, M. Sur, and E. N. Brown, "Denoising two-photon calcium imaging data," PLoS ONE, vol. 6, no. 6, pp. e20490, Jun. 2011
This work is protected by
W. Q. Malik, J. Schummers, M. Sur, and E. N. Brown, "Noise reduction of imaging data," United States Patent 2012/0093376 A1, Apr. 2012
Please contact me for a copy of the software and MIT TLO for licensing information.
Please contact me for bug reports, comments and suggestions.
