Cross-channel correlations in tetrode recordings: implications for spike-sorting

A temporal integration mechanism enhances frequency selectivity of broadband inputs to inferior colliculus

Accurately resolving frequency components in sounds is essential for sound recognition, yet there is little direct evidence for how frequency selectivity is preserved or newly created across auditory structures. We demonstrate that prepotentials (PPs) with physiological properties resembling presynaptic potentials from broadly tuned brainstem inputs can be recorded concurrently with postsynaptic action potentials in inferior colliculus (IC). These putative brainstem inputs (PBIs) are broadly tuned and exhibit delayed and spectrally interleaved excitation and inhibition not present in the simultaneously recorded IC neurons (ICNs). A sharpening of tuning is accomplished locally at the expense of spike-timing precision through nonlinear temporal integration of broadband inputs. A neuron model replicates the finding and demonstrates that temporal integration alone can degrade timing precision while enhancing frequency tuning through interference of spectrally in- and out-of-phase inputs. These findings suggest that, in contrast to current models that require local inhibition, frequency selectivity can be sharpened through temporal integration, thus supporting an alternative computational strategy to quickly refine frequency selectivity.

Sparse Coding and Compressive Sensing for Overlapping Neural Spike Sorting

Spike sorting is one of the key techniques to understand brain activity. With the development of modern electrophysiology technology, some recent multi-electrode technologies have been able to record the activity of thousands of neuronal spikes simultaneously. In this scenario, however, the recorded activity may be the overlap of multi-neuron spikes, which will degrade the sorting performance of existing cluster-based algorithms. In this paper, we introduce methods for overlapping spike sorting. The introduced methods start from a convolution model, where a sparse vector could be obtained via sparse coding or compressive sensing. Then, we use a maximum a posteriori estimate to optimize the sparse vector, which makes the overlapped spike sorting completed successfully. The advantage of the introduced method is that it performs better than traditional methods when the waveforms of the spikes are similar. In experiments, some synthetic and real spike data are used to testify the methods. The experiment results show that the introduced methods' average sorting detection, defined as the ratio of successfully sorted spikes to the total spikes is nearly 4% higher than the traditional methods, under the condition of the experimental data with similar waveforms.

Poly (3, 4-ethylenedioxythiophene)-ionic liquid coating improves neural recording and stimulation functionality of MEAs

In vivo multi-electrode arrays (MEAs) can sense electrical signals from a small set of neurons or modulate neural activity through micro-stimulation. Electrode's geometric surface area (GSA) and impedance are important for both unit recording and neural stimulation. Smaller GSA is preferred due to enhanced selectivity of neural signal, but it tends to increase electrode impedance. Higher impedance leads to increased electrical noise and signal loss in single unit neural recording. It also yields a smaller charge injection window for safe neural stimulation. To address these issues, poly (3, 4-ethylenedioxythiophene) - ionic liquid (PEDOT-IL) conducting polymers were electrochemically polymerized on the surface of the microelectrodes. The PEDOT-IL coating reduced the electrode impedance modulus by over 35 times at 1 kHz. It also exhibited compelling nanostructure in surface morphology and significant impedance reduction in other physiologically relevant range (100Hz-1000Hz). PEDOT-IL coated electrodes exhibited a Charge Storage Capacity (CSC) that was about 20 times larger than that of bare electrodes. The neural recording performance of PEDOT-IL coated electrodes was also compared with uncoated electrodes and PEDOT-poly (styrenesulfonate) (PSS) coated electrodes in rat barrel cortex (SI). Spontaneous neural activity and sensory evoked neural response were utilized for characterizing the electrode performance. The PEDOT-IL electrodes exhibited a higher unit yield and signal-to-noise ratio (SNR) in vivo. The local field potential recording was benefited from the low impedance PEDOT-IL coating in noise and artifact reduction as well. Moreover, cell culture on PEDOT-IL coating demonstrated that the material is safe for neural tissue and reduces astrocyte fouling. Taken together, PEDOT-IL coating has the potential to benefit neural recording and stimulation electrodes, especially when integrated with novel small GSA electrode arrays designed for high recording density, minimal insertion damage and alleviated tissue reaction.

Spike detection methods for polytrodes and high density microelectrode arrays

This paper compares the ability of different methods to detect and resolve spikes recorded extracellularly with polytrode and high-density microelectrode arrays (MEAs). Detecting spikes on such arrays is more complex than with single electrodes or tetrodes since a single spike from a neuron may cause threshold crossings on several adjacent channels, giving rise to multiple events. These initial events have to be recognized as belonging to a single spike. Combining them is, in essence, a clustering problem. A conflicting need is to be able to resolve spike waveforms that occur close together in space and time. We first evaluated three different detection methods, using simulated data in which spike shape waveforms obtained from real recordings were added to noise with an amplitude and temporal structure similar to that found in real recordings. Performance was assessed by calculating the percentage of correctly identified spikes vs. the false positive rate. Using the best of these detection methods, two different methods for avoiding multiple detections per spike were tested: one based on windowing and the other based on clustering. Using parameters that avoided spatial and temporal duplication, the spatiotemporal resolution of the two methods was next evaluated. The method based on clustering gave slightly better results. Both methods could resolve spikes occurring 1 ms or more apart, regardless of their spatial separation. There was no restriction on the temporal resolution of spike pairs for units more than 200 μm apart.

A unified framework and method for automatic neural spike identification

Automatic identification of action potentials from one or more extracellular electrode recordings is generally achieved by clustering similar segments of the measured voltage trace, a method that fails (or requires substantial human intervention) for spikes whose waveforms overlap. We formulate the problem in terms of a simple probabilistic model, and develop a unified method to identify spike waveforms along with continuous-valued estimates of their arrival times, even in the presence of overlap. Specifically, we make use of a recent algorithm known as Continuous Basis Pursuit for solving linear inverse problems in which the component occurrences are sparse and are at arbitrary continuous-valued times. We demonstrate significant performance improvements over current state-of-the-art clustering methods for four simulated and two real data sets with ground truth, each of which has previously been used as a benchmark for spike sorting. In addition, performance of our method on each of these data sets surpasses that of the best possible clustering method (i.e., one that is specifically optimized to minimize errors on each data set). Finally, the algorithm is almost completely automated, with a computational cost that scales well for multi-electrode arrays.

A model-based spike sorting algorithm for removing correlation artifacts in multi-neuron recordings

We examine the problem of estimating the spike trains of multiple neurons from voltage traces recorded on one or more extracellular electrodes. Traditional spike-sorting methods rely on thresholding or clustering of recorded signals to identify spikes. While these methods can detect a large fraction of the spikes from a recording, they generally fail to identify synchronous or near-synchronous spikes: cases in which multiple spikes overlap. Here we investigate the geometry of failures in traditional sorting algorithms, and document the prevalence of such errors in multi-electrode recordings from primate retina. We then develop a method for multi-neuron spike sorting using a model that explicitly accounts for the superposition of spike waveforms. We model the recorded voltage traces as a linear combination of spike waveforms plus a stochastic background component of correlated Gaussian noise. Combining this measurement model with a Bernoulli prior over binary spike trains yields a posterior distribution for spikes given the recorded data. We introduce a greedy algorithm to maximize this posterior that we call "binary pursuit". The algorithm allows modest variability in spike waveforms and recovers spike times with higher precision than the voltage sampling rate. This method substantially corrects cross-correlation artifacts that arise with conventional methods, and substantially outperforms clustering methods on both real and simulated data. Finally, we develop diagnostic tools that can be used to assess errors in spike sorting in the absence of ground truth.

An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes

For the analysis of neuronal cooperativity, simultaneously recorded extracellular signals from neighboring neurons need to be sorted reliably by a spike sorting method. Many algorithms have been developed to this end, however, to date, none of them manages to fulfill a set of demanding requirements. In particular, it is desirable to have an algorithm that operates online, detects and classifies overlapping spikes in real time, and that adapts to non-stationary data. Here, we present a combined spike detection and classification algorithm, which explicitly addresses these issues. Our approach makes use of linear filters to find a new representation of the data and to optimally enhance the signal-to-noise ratio. We introduce a method called “Deconfusion” which de-correlates the filter outputs and provides source separation. Finally, a set of well-defined thresholds is applied and leads to simultaneous spike detection and spike classification. By incorporating a direct feedback, the algorithm adapts to non-stationary data and is, therefore, well suited for acute recordings. We evaluate our method on simulated and experimental data, including simultaneous intra/extra-cellular recordings made in slices of a rat cortex and recordings from the prefrontal cortex of awake behaving macaques. We compare the results to existing spike detection as well as spike sorting methods. We conclude that our algorithm meets all of the mentioned requirements and outperforms other methods under realistic signal-to-noise ratios and in the presence of overlapping spikes.

Building spike representation in tetrodes

This paper presents a new technique for analyzing the recorded information from tetrodes in freely behaving rats, based on independent component analysis (ICA). The ion-specific pumps and channels allow fast transfer of charges such as Na+, K+, Cl- and eventually Ca2+ during each action potential (AP). These groups of charges under an electrical field have distinct spatial trajectories. Therefore, the generated signals within a tetrode are considered to be composed mainly by statistically independent signal sources that can be obtained by performing ICA. In order to compute the position of independent sources during AP generation, the triangulation method uses an iterative Newton-Raphson algorithm. The representation of the independent signal sources in three-dimensional tetrode space is then obtained. Since the charge movements are extensively spread on the neuron's surface, the representation in tetrode space reveals electrical spatial patterns of activation during each AP. The analysis of several spikes coming from the same neuron reveals small changes from spike to spike in the 3D shape. Since information within spikes is highly transferred by ionic fluxes these electrical patterns of activation reflect neuronal computation occurring during each AP.

Optimal filtering for spike sorting of multi-site electrode recordings

We derive an optimal linear filter, to reduce the distortions of the peak amplitudes of action potentials in extracellular multitrode recordings, which are due to background activity and overlapping spikes. This filter is being learned very efficiently from the raw recordings in an unsupervised manner and responds to the average waveform with an impulse of minimal width. The average waveform does not have to be known in advance, but is learned together with the optimal filter. The peak amplitude of a filtered waveform is a more reliable estimate for the amplitude of an action potential than the peak of the biphasic waveform and can improve the accuracy of the event detection and clustering procedures. We demonstrate a spike-sorting application, in which events are detected using the Mahalanobis distance in the N-dimensional space of filtered recordings as a distance measure, and the event amplitudes of the filtered recordings are clustered to assign events to individual units. This method is fast and robust, and we show its performance by applying it to real tetrode recordings of spontaneous activity in the visual cortex of an anaesthetized cat and to realistic artificial data derived therefrom.

Spike source localization with tetrodes

The paper presents in detail a method for approximating the spatial position of neural spike activity from tetrode recordings. The method uses a nonlinear mapping of a set of tetrode tip amplitudes into three-dimensional (3D) space, followed by a self-organizing map clustering technique. Viewed as a spike sorting method, it performs better than tetrode peak amplitudes and it is roughly equivalent with amplitude ratios. The technique's appeal to physical location may be of advantage in many investigations.

Tracking neurons recorded from tetrodes across time

Tetrodes allow isolation of multiple neurons at a single recording site by clustering spikes. Due to electrode drift and perhaps due to time-varying neuronal properties, positions and shapes of clusters change in time. As data is typically collected in sequential files, to track neurons across files one has to decide which clusters from different files belong to the same neuron. We report on a semi-automated neuron tracking procedure that uses computed similarities between the mean spike waveforms of the clusters. The clusters with the most similar waveforms are assigned to the same neuron, provided their similarity exceeds a threshold. To set this threshold, we calculate two distributions: of within-file similarities, and of best matches in the across adjacent file similarities. The threshold is set to the value that optimally separates the two distributions. We compare different measures of similarity (metrics) by their ability to separate these distributions. We find that these metrics do not differ drastically in their performance, but that taking into account the cross-channel noise correlation significantly improves performance of all metrics. We also demonstrate the method on an independent dataset and show that neurons, as assigned by the procedure, have consistent physiological properties across files.

Fractionated analysis of paired-electrode nerve recordings

Multi-unit activity recorded from two electrodes positioned at a distance on a nerve may be analysed by cross-correlation, but units similar in direction and velocity of propagation cannot be distinguished and separately evaluated by this method. To overcome this limit, we added two features, represented by the impulse amplitudes of the paired recordings, to the dimension given by the impulse delay. The analysis was fractionated according to the new dimensions. In experimental recordings from the locomotor appendage of the lobster Homarus americanus, the fractionated analysis proved capable of identifying the contributions of single active units, even if these were superimposed and indiscernible in the global cross-correlation histogram. Up to 5 motor and 10 sensory units could be identified. The shape of the paired impulses was evaluated by an averaging procedure. Analogous evaluations on simulated recordings made it possible to estimate the influences exerted on performance by variations in noise level and in the number and firing rate of active units. The global signal could be resolved into single units even under the worst conditions. Accuracy in evaluating the amount of unit activity varied, exceeding 90% in about half of the cases tested; a similar performance was attained by evaluation of the impulse shapes.

Signal-to-noise ratio improvement in multiple electrode recording

Recordings of spike trains made with microwires or silicon electrodes include more noise from various sources that contaminate the observed spike shapes compared with recordings using sharp microelectrodes. This is a particularly serious problem if spike shape sorting is required to separate the several trains that might be observed on a particular electrode. However, if recordings are made with an array of such electrodes, there are several mathematical methods to improve the effective signal (spikes) to noise ratio, thus considerably reducing inaccuracy in spike detection and shape sorting. We compare the theoretical basis of three such methods and evaluate their performance with simulated and real data.