Spike detection, characterization, and discrimination using feature analysis software written in LabVIEW

Rapid and accurate discrimination of single units from extracellular recordings is a fundamental process for the analysis and interpretation of electrophysiological recordings. We present an algorithm that performs detection, characterization, discrimination, and analysis of action potentials from extracellular recording sessions. The program was entirely written in LabVIEW (National Instruments), and requires no external hardware devices or a priori information about action potential shapes. Waveform events are detected by scanning the digital record for voltages that exceed a user-adjustable trigger. Detected events are characterized to determine nine different time and voltage levels for each event. Various algebraic combinations of these waveform features are used as axis choices for 2-D Cartesian plots of events. The user selects axis choices that generate distinct clusters. Multiple clusters may be defined as action potentials by manually generating boundaries of arbitrary shape. Events defined as action potentials are validated by visual inspection of overlain waveforms. Stimulus-response relationships may be identified by selecting any recorded channel for comparison to continuous and average cycle histograms of binned unit data. The algorithm includes novel aspects of feature analysis and acquisition, including higher acquisition rates for electrophysiological data compared to other channels. The program confirms that electrophysiological data may be discriminated with high-speed and efficiency using algebraic combinations of waveform features derived from high-speed digital records.

[1]  D J Mishelevich,et al.  On-line real-time digital computer separation of extracellular neuroelectric signals. , 1970, IEEE transactions on bio-medical engineering.

[2]  Edward M. Schmidt,et al.  Computer separation of multi-unit neuroelectric data: a review , 1984, Journal of Neuroscience Methods.

[3]  David Stagg,et al.  Computer acquisition of multiunit nerve-spike signals , 1973, Medical and biological engineering.

[4]  Bruce L. McNaughton,et al.  The stereotrode: A new technique for simultaneous isolation of several single units in the central nervous system from multiple unit records , 1983, Journal of Neuroscience Methods.

[5]  W. A. Clark,et al.  Simultaneous Studies of Firing Patterns in Several Neurons , 1964, Science.

[6]  Urs R. Wyss,et al.  STAP-12: A library system for on-line assimilation and off-line analysis of event/time data , 1971 .

[7]  M. Abeles,et al.  Multispike train analysis , 1977, Proceedings of the IEEE.

[8]  A. Fuchs,et al.  A method for measuring horizontal and vertical eye movement chronically in the monkey. , 1966, Journal of applied physiology.

[9]  B. Wheeler,et al.  SEPARATION OF COCKROACH GIANT ACTION POTENTIALS USING MULTIUNIT ANALYSIS TECHNIQUES , 1979 .

[10]  S.R. Smith,et al.  A real-time multiprocessor system for acquisition of multichannel neural data , 1988, IEEE Transactions on Biomedical Engineering.

[11]  E. John,et al.  Single cell activity in chronic unit recording: A quantitative study of the unit amplitude spectrum , 1976, Brain Research Bulletin.

[12]  A. Fuchs,et al.  Discharge patterns in nucleus prepositus hypoglossi and adjacent medial vestibular nucleus during horizontal eye movement in behaving macaques. , 1992, Journal of neurophysiology.

[13]  W. Wiemer,et al.  Peak discrimination as a method for quantitative evaluation of neural activity by computer , 1975, Medical and biological engineering.

[14]  B L McNaughton,et al.  Dynamics of the hippocampal ensemble code for space. , 1993, Science.

[15]  Bradley C. Wheeler,et al.  Automatic Discrimination of Single Units , 1998 .

[16]  Ryo Takeuchi,et al.  Interpolation models with multiple hyperparameters , 1998, Stat. Comput..

[17]  R J Radna,et al.  Computer assisted unit data acquistion/reduction. , 1978, Electroencephalography and clinical neurophysiology.

[18]  M. Quirk,et al.  Interaction between spike waveform classification and temporal sequence detection , 1999, Journal of Neuroscience Methods.

[19]  Michael S. Lewicki,et al.  Bayesian Modeling and Classification of Neural Signals , 1993, Neural Computation.

[20]  B. McNaughton,et al.  Tetrodes markedly improve the reliability and yield of multiple single-unit isolation from multi-unit recordings in cat striate cortex , 1995, Journal of Neuroscience Methods.

[21]  R. L. Schoenfeld,et al.  Minicomputer identification and timing of nerve impulses mixed in a single recording channel , 1973 .

[22]  M S Lewicki,et al.  A review of methods for spike sorting: the detection and classification of neural action potentials. , 1998, Network.

[23]  E. M. Glaser,et al.  ON-LINE SEPARATION OF INTERLEAVED NEURONAL PULSE SEQUENCES* , 1968 .

[24]  Bruce C. Wheeler,et al.  A Comparison of Techniques for Classification of Multiple Neural Signals , 1982, IEEE Transactions on Biomedical Engineering.