Intelligent software for spike separation in multiunit recordings

Identification of reliable spike templates in multi-unit extracellular recordings using fuzzy clustering

A method for extracting single-unit spike trains from extracellular recordings containing the activity of several simultaneously active cells is presented. The technique is particularly effective when spikes overlap temporally. It is capable of identifying the exact number of neurons contributing to a recording and of creating reliable spike templates. The procedure is based on fuzzy clustering and its performance is controlled by minimizing a cluster-validity index which optimizes the compactness and separation of the identified clusters. Application examples with synthetic spike trains generated from real spikes and segments of background noise show the advantage of the fuzzy method over conventional template-creation approaches in a wide range of signal-to-noise ratios.

Detection, classification, and superposition resolution of action potentials in multiunit single-channel recordings by an on-line real-time neural network

Determination of single-unit spike trains from multiunit recordings obtained during extracellular recording has been the focus of many studies over the last two decades. In multiunit recordings, superpositions can occur with high frequency if the firing rates of the neurons are high or correlated, making superposition resolution imperative for accurate spike train determination. In this work, a connectionist neural network (NN) was applied to the spike sorting challenge. A novel training scheme was developed which enabled the NN to resolve some superpositions using single-channel recordings. Simulated multiunit spike trains were constructed from templates and noise segments that were extracted from real extracellular recordings. The simulations were used to determine the performances of the NN and a simple matched template filter (MTF), which was used as a basis for comparison. The network performed as well as the MTF in identifying nonoverlapping spikes, and was significantly better in resolving superpositions and rejecting noise. An on-line, real-time implementation of the NN discriminator, using a high-speed digital signal processor mounted inside an IBM-PC, is now in use in six laboratories.

Multi-unit spike discrimination using wavelet transforms

A new spike discrimination procedure addressing the specific problem of spike superposition is described. The method, based on a shift-invariant wavelet transform and its amplitude-and-phase representation, has the advantage of both reducing the effect of noise present in the data and correcting the latency of specific components in a waveform. When spikes overlap and produce unknown patterns, the procedure extracts the constituent spikes and also estimates their exact time of occurrence. Fast implementation algorithms, having complexity of at most O (N log N), allow the use of the method in real-time applications.

Single unit components of the hypothalamic multiunit electrical activity associated with the central signal generator that directs the pulsatile secretion of gonadotropic hormones

Vertebrate reproduction is dependent on the operation of a central signal generator that directs the episodic release of gonadotropin-releasing hormone, a neuropeptide that stimulates secretion of the pituitary gonadotropic hormones and, thereby, controls gonadal function. The electrophysiological correlates of this pulse generator are characterized by abrupt increases in hypothalamic multiunit electrical activity (MUA volleys) invariably associated with the initiation of secretory episodes of luteinizing hormone. Using cluster analysis, we extracted single units from the multiunit signals recorded from the mediobasal hypothalamus of four intact and four ovariectomized rhesus monkeys. Of the 40 individual units identified in this manner, 24 increased their frequency with the MUA volleys. The onset and termination of these single-unit bursts occurred coincidently with those of the MUA volleys in both intact and ovariectomized animals, indicating that the longer duration of the MUA volleys characteristic of the gonadectomized animals was due not to the sequential activation of different units but to the longer bursts of the individual cells. Four other units showed decreases in firing rate during the MUA volleys, while the frequency of the remainder did not change. All the examined units were active during the intervals between the volleys of electrical activity. The results indicate that the MUA volleys associated with the activity of the gonadotropin-releasing hormone pulse generator represent the simultaneous increase in firing rate of some individual hypothalamic neurons and the decrease in the frequency of others.

Optimal detection, classification, and superposition resolution in neural waveform recordings

The effects of noise autocorrelation on neural waveform recognition (detection, classification, and superposition resolution) are investigated in this study using microelectrode recordings from the cortex of a monkey. Optimal waveform recognition is accomplished by passing the data through a whitening filter before matched filtering for detection or template matching for classification and superposition resolution. Template matching without whitening requires about 40% higher signal-to-noise ratio than template matching with whitening for comparable classification and superposition resolution. The comparable difference for detection is 15%.

Automatic classification and analysis of microneurographic spike data using a PC/AT

Using a standard PC-AT with a commercial analog data interface a system was designed which supports microneurographic experiments and which may also be used for other types of extracellular spike recordings. The signal is sampled on-line at 25 kHz and a spike is detected if the signal passes a certain threshold. The spikes are displayed on the screen and stored on disk. A second on-line mode records the responses of the examined unit to electrical stimulations, which are used to identify the type of fibre and to test the subsequent spike classification. The spikes are classified off-line using a template matching algorithm, which has unsupervised learning and discrimination phases. The results are displayed in a time-frequency plot and may be checked with the responses to electrical stimulations. Artifacts from EMG and other electrical fields are reliably sorted out. In recordings, which include more than one unit, their spikes are discriminated with a low error rate.