Nev2lkit: a New Open Source Tool for Handling neuronal Event Files from Multi-electrode recordings

The analysis and discrimination of action potentials, or "spikes", is a central issue to systems neuroscience research. Here we introduce a free open source software for the analysis and discrimination of neural spikes based on principal component analysis and different clustering algorithms. The main objective is to supply a friendly user interface that links the experimental data to a basic set of routines for analysis, visualization and classification of spikes in a consistent framework. The tool has been tested on artificial data sets, on multi-electrode extracellular recordings from ganglion cell populations in isolated superfused mouse, rabbit and turtle retinas, and on electrophysiological recordings from mouse visual cortex. Our results show that NEV2lkit is very reliable and able to satisfy the experimental demands in terms of accuracy, efficiency and consistency across experiments. It performs fast unit sorting in single or multiple experiments and allows the extraction of spikes from over large time intervals in continuously recorded data streams. The tool is implemented in C++ and runs cross-platform on Linux, OS X and Windows systems. To facilitate the adaptation and extension as well as the addition of new routines, tools and algorithms for data analysis, the source code, binary distributions for different operating systems and documentation are all freely available at http://nev2lkit.sourceforge.net .

[1]  Hiroshi Tamura,et al.  Tracking Spike-Amplitude Changes to Improve the Quality of Multineuronal Data Analysis , 2007, IEEE Transactions on Biomedical Engineering.

[2]  C. Schwarz,et al.  MEA-Tools: an open source toolbox for the analysis of multi-electrode data with matlab , 2002, Journal of Neuroscience Methods.

[3]  Hojjat Adeli,et al.  Spiking Neural Networks , 2009, Int. J. Neural Syst..

[4]  Jan Müller,et al.  High-density microelectrode array recordings and real-time spike sorting for closed-loop experiments: an emerging technology to study neural plasticity , 2012, Front. Neural Circuits.

[5]  J. Csicsvari,et al.  Accuracy of tetrode spike separation as determined by simultaneous intracellular and extracellular measurements. , 2000, Journal of neurophysiology.

[6]  Miguel A. L. Nicolelis,et al.  Methods for Neural Ensemble Recordings , 1998 .

[7]  Eduardo Fernández,et al.  Conditioned spikes: a simple and fast method to represent rates and temporal patterns in multielectrode recordings , 2004, Journal of Neuroscience Methods.

[8]  R B Reilly,et al.  Automated spike sorting algorithm based on Laplacian eigenmaps and k-means clustering , 2011, Journal of neural engineering.

[9]  Ahmed Bouridane,et al.  2D and 3D palmprint information, PCA and HMM for an improved person recognition performance , 2013, Integr. Comput. Aided Eng..

[10]  Tomoki Fukai,et al.  Spike sorting of heterogeneous neuron types by multimodality-weighted PCA and explicit robust variational Bayes , 2012, Front. Neuroinform..

[11]  Daniel Novak,et al.  Performance comparison of extracellular spike sorting algorithms for single-channel recordings , 2012, Journal of Neuroscience Methods.

[12]  Hojjat Adeli,et al.  Enhanced probabilistic neural network with local decision circles: A robust classifier , 2010, Integr. Comput. Aided Eng..

[13]  Hojjat Adeli,et al.  Improved spiking neural networks for EEG classification and epilepsy and seizure detection , 2007, Integr. Comput. Aided Eng..

[14]  Dimitrios A. Adamos,et al.  Performance evaluation of PCA-based spike sorting algorithms , 2008, Comput. Methods Programs Biomed..

[15]  Holger Kantz,et al.  Practical implementation of nonlinear time series methods: The TISEAN package. , 1998, Chaos.

[16]  Hojjat Adeli,et al.  A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection , 2009, Neural Networks.

[17]  Ueli Rutishauser,et al.  Online detection and sorting of extracellularly recorded action potentials in human medial temporal lobe recordings, in vivo , 2006, Journal of Neuroscience Methods.

[18]  Z Tiganj,et al.  A non-parametric method for automatic neural spike clustering based on the non-uniform distribution of the data. , 2011, Journal of neural engineering.

[19]  Ulrik Söderström,et al.  Reconstruction of occluded facial images using asymmetrical Principal Component Analysis , 2011, 2011 18th International Conference on Systems, Signals and Image Processing.

[20]  Hojjat Adeli,et al.  Principal Component Analysis-Enhanced Cosine Radial Basis Function Neural Network for Robust Epilepsy and Seizure Detection , 2008, IEEE Transactions on Biomedical Engineering.

[21]  M. Cohen,et al.  Measuring and interpreting neuronal correlations , 2011, Nature Neuroscience.

[22]  Eduardo Fernandez,et al.  High-resolution spatio-temporal mapping of visual pathways using multi-electrode arrays , 2001, Vision Research.

[23]  K. Obermayer,et al.  Optimal filtering for spike sorting of multi-site electrode recordings. , 2005, Network.

[24]  Eduardo Ros,et al.  From Sensors to Spikes: Evolving Receptive Fields to Enhance Sensorimotor Information in a Robot-Arm , 2012, Int. J. Neural Syst..

[25]  Marc M. Van Hulle,et al.  Decoding Grating Orientation from microelectrode Array Recordings in Monkey Cortical Area V4 , 2010, Int. J. Neural Syst..

[26]  R. Normann,et al.  Population coding in spike trains of simultaneously recorded retinal ganglion cells 1 1 Published on the World Wide Web on 7 November 2000. , 2000, Brain Research.

[27]  Markus Bongard,et al.  Retinal ganglion cell synchronization by fixational eye movements improves feature estimation , 2002, Nature Neuroscience.

[28]  P. Tresco,et al.  A new high-density (25 electrodes/mm2) penetrating microelectrode array for recording and stimulating sub-millimeter neuroanatomical structures , 2013, Journal of neural engineering.

[29]  M. Schnitzer,et al.  Multineuronal Firing Patterns in the Signal from Eye to Brain , 2003, Neuron.

[30]  P. M. Horton,et al.  Spike sorting based upon machine learning algorithms (SOMA) , 2007, Journal of Neuroscience Methods.

[31]  Yoshio Sakurai,et al.  Automatic sorting for multi-neuronal activity recorded with tetrodes in the presence of overlapping spikes. , 2003, Journal of neurophysiology.

[32]  E. N. Brown,et al.  nSTAT: Open-source neural spike train analysis toolbox for Matlab , 2012, Journal of Neuroscience Methods.

[33]  Chenhui Yang,et al.  The M-Sorter: An automatic and robust spike detection and classification system , 2012, Journal of Neuroscience Methods.

[34]  Hojjat Adeli,et al.  Wavelet‐Clustering‐Neural Network Model for Freeway Incident Detection , 2003 .

[35]  Jadin C. Jackson,et al.  Quantitative measures of cluster quality for use in extracellular recordings , 2005, Neuroscience.

[36]  Shy Shoham,et al.  Robust, automatic spike sorting using mixtures of multivariate t-distributions , 2003, Journal of Neuroscience Methods.

[37]  María P. Bonomini,et al.  DATA-MEAns: An open source tool for the classification and management of neural ensemble recordings , 2005, Journal of Neuroscience Methods.

[38]  Wai Keung Wong,et al.  Relationship between Applicability of Current-Based Synapses and Uniformity of Firing Patterns , 2012, Int. J. Neural Syst..

[39]  Nicholas G Hatsopoulos,et al.  The science of neural interface systems. , 2009, Annual review of neuroscience.

[40]  Michael J. Black,et al.  Assistive technology and robotic control using motor cortex ensemble‐based neural interface systems in humans with tetraplegia , 2007, The Journal of physiology.

[41]  Tomoki Fukai,et al.  Accurate spike sorting for multi‐unit recordings , 2010, The European journal of neuroscience.

[42]  Koichi Sameshima,et al.  Trends in Multichannel Neural Ensemble Recording Instrumentation , 1998 .

[43]  D. Gardner Neurodatabase.org: networking the microelectrode , 2004, Nature Neuroscience.

[44]  John P. Donoghue,et al.  Automated spike sorting using density grid contour clustering and subtractive waveform decomposition , 2007, Journal of Neuroscience Methods.

[45]  Miriam Zacksenhouse,et al.  Cortical Ensemble Adaptation to Represent Velocity of an Artificial Actuator Controlled by a Brain-Machine Interface , 2005, The Journal of Neuroscience.

[46]  Marc M. Van Hulle,et al.  Enhancing the Yield of High-Density electrode Arrays through Automated electrode Selection , 2012, Int. J. Neural Syst..

[47]  Luping Shi,et al.  Presynaptic Learning and Memory with a Persistent Firing Neuron and a Habituating Synapse: a Model of Short Term Persistent Habituation , 2012, Int. J. Neural Syst..

[48]  Jon A. Mukand,et al.  Neuronal ensemble control of prosthetic devices by a human with tetraplegia , 2006, Nature.

[49]  Miguel A L Nicolelis,et al.  Orbitofrontal ensemble activity monitors licking and distinguishes among natural rewards. , 2006, Journal of neurophysiology.