Extracellular Neural Recordings Research Articles

Deep learning based decoding of single local field potential events

How is information processed in the cerebral cortex? In most cases, recorded brain activity is averaged over many (stimulus) repetitions, which erases the fine-structure of the neural signal. However, the brain is obviously a single-trial processor. Thus, we here demonstrate that an unsupervised machine learning approach can be used to extract meaningful information from electro-physiological recordings on a single-trial basis. We use an auto-encoder network to reduce the dimensions of single local field potential (LFP) events to create interpretable clusters of different neural activity patterns. Strikingly, certain LFP shapes correspond to latency differences in different recording channels. Hence, LFP shapes can be used to determine the direction of information flux in the cerebral cortex. Furthermore, after clustering, we decoded the cluster centroids to reverse-engineer the underlying prototypical LFP event shapes. To evaluate our approach, we applied it to both extra-cellular neural recordings in rodents, and intra-cranial EEG recordings in humans. Finally, we find that single channel LFP event shapes during spontaneous activity sample from the realm of possible stimulus evoked event shapes. A finding which so far has only been demonstrated for multi-channel population coding.

Decoding the Spike-Band Subthreshold Motor Cortical Activity

Intracortical Brain-Computer Interfaces (iBCI) use single-unit activity (SUA), multiunit activity (MUA) and local field potentials (LFP) to control neuroprosthetic devices. SUA and MUA are usually extracted from the bandpassed recording through amplitude thresholding, while subthreshold data are ignored. Here, we show that subthreshold data can actually be decoded to determine behavioral variables with test set accuracy of up to 100%. Although the utility of SUA, MUA and LFP for decoding behavioral variables has been explored previously, this study investigates the utility of spike-band subthreshold activity exclusively. We provide evidence suggesting that this activity can be used to keep decoding performance at acceptable levels even when SUA quality is reduced over time. To the best of our knowledge, the signals that we derive from the subthreshold activity may be the weakest neural signals that have ever been extracted from extracellular neural recordings, while still being decodable with test set accuracy of up to 100%. These results are relevant for the development of fully data-driven and automated methods for amplitude thresholding spike-band extracellular neural recordings in iBCIs containing thousands of electrodes.

Toward Completely Sampled Extracellular Neural Recording During fMRI.

This work aims to estimate severe fMRI scanning artifacts in extracellular neural recordings made at ultrahigh magnetic field strengths in order to remove the artifact interferences and uncover the complete neural electrophysiology signal. We build on previous work that used PCA to denoise EEG recorded during fMRI, adapting it to cover the much larger frequency range (1-6000 Hz) of the extracellular field potentials (EFPs) observed by extracellular neural recordings. We examine the singular value decomposition (SVD)-PCA singular value shrinkage (SVS) and compare two shrinkage rules and a sliding template subtraction approach. Additionally, we present a new technique for estimating the singular value upper bounds in spontaneous neural activity recorded in the isoflurane anesthetized rat that uses the temporal first difference of the neural signal. The approaches are tested on artificial datasets to examine their efficacy in detecting extracellular action potentials (EAPs: 300-6000 Hz) recorded during fMRI gradient interferences. Our results indicate that it is possible to uncover the EAPs recorded during gradient interferences. The methods are then tested on natural (non-artificial) datasets recorded from the cortex of isoflurane anesthetized rats, where both local field potential (LFP: 1-300 Hz) and EAP signals are analyzed. The SVS methods are shown to be advantageous compared to sliding template subtraction, especially in the high frequency range corresponding to EAPs. Our novel approach moves us towards simultaneous fMRI and completely sampled neural recording (1-6000Hz with no temporal gaps), providing the opportunity for further study of spontaneous brain function and neurovascular coupling at ultrahigh field in the isoflurane anesthetized rat.

ProbeInterface: A Unified Framework for Probe Handling in Extracellular Electrophysiology.

Recording neuronal activity with penetrating extracellular multi-channel electrode arrays, more commonly known as neural probes, is one of the most widespread approaches to probe neuronal activity. Despite a plethora of available extracellular probe designs, the time-consuming process of mapping of electrode channel order and relative geometries, as required by spike-sorting software is invariably left to the end-user. Consequently, this manual process is prone to mis-mapping mistakes, which in turn lead to undesirable spike-sorting errors and inefficiencies. Here, we introduce ProbeInterface, an open-source project that aims to unify neural probe metadata descriptions by removing the manual step of probe mapping prior to spike-sorting for the analysis of extracellular neural recordings. ProbeInterface is first of all a Python API, which enables users to create and visualize probes and probe groups at any required complexity level. Second, ProbeInterface facilitates the generation of comprehensive wiring description in a reproducible fashion for any specific data-acquisition setup, which usually involves the use of a recording probe, a headstage, adapters, and an acquisition system. Third, we collaborate with probe manufacturers to compile an open library of available probes, which can be downloaded at run time using our Python API. Finally, with ProbeInterface we define a file format for probe handling which includes all necessary information for a FAIR probe description and is compatible with and complementary to other open standards in neuroscience.

Enhanced Sparse Representations of Spike Waveforms Obtained by using the Basis Pursuit Approach

In the extracellular neural recordings, the spike waveforms formed by the neurons nearby the recording electrode must be sorted according to their morphology. This process is called as spike sorting and it is an important prerequisite in neural decoding algorithms. Low Q-factor wavelet transforms are frequently being used as feature extractors to detect the discriminative patterns between adjacent neurons’ activity. However, the wavelet coefficients are highly sensitive to noise that may occur due to the employed instrumentation system and the local field potentials defined as the total activity of nearby neurons. However, enhanced sparse representations of the spike wave forms, having reduced noise activity, can be attained by using the basis pursuit method that is applied to the tunable Q-factor wavelet transform coefficients. In the tunable Q-factor wavelet transform, the Q-factor of the wavelet filters can be tuned according to the signal of interest with a controllable redundancy. In the proposed study, enhanced sparse representations of the spike waveforms were obtained by using the basis pursuit approach. Later, the energy values of the decomposed subbands were employed as features that can discriminate morphological differences in spike shapes. Finally, the obtained features were fed to k-nearest neighbors and decision trees learning models in an unbiased cross-validation scheme to objectively measure the effect of the enhanced sparsity decomposition. The qualitative and quantitative results show that the enhanced sparsity-based energy features are superior to the traditional low Q-factor based wavelet decomposition in terms of the accuracy metric.

The significance of neural inter-frequency power correlations

It is of great interest in neuroscience to determine what frequency bands in the brain have covarying power. This would help us robustly identify the frequency signatures of neural processes. However to date, to the best of the author’s knowledge, a comprehensive statistical approach to this question that accounts for intra-frequency autocorrelation, frequency-domain oversampling, and multiple testing under dependency has not been undertaken. As such, this work presents a novel statistical significance test for correlated power across frequency bands for a broad class of non-stationary time series. It is validated on synthetic data. It is then used to test all of the inter-frequency power correlations between 0.2 and 8500 Hz in continuous intracortical extracellular neural recordings in Macaque M1, using a very large, publicly available dataset. The recordings were Current Source Density referenced and were recorded with a Utah array. The results support previous results in the literature that show that neural processes in M1 have power signatures across a very broad range of frequency bands. In particular, the power in LFP frequency bands as low as 20 Hz was found to almost always be statistically significantly correlated to the power in kHz frequency ranges. It is proposed that this test can also be used to discover the superimposed frequency domain signatures of all the neural processes in a neural signal, allowing us to identify every interesting neural frequency band.

Removing Noise from Extracellular Neural Recordings Using Fully Convolutional Denoising Autoencoders.

Extracellular recordings are severely contaminated by a considerable amount of noise sources, rendering the denoising process an extremely challenging task that should be tackled for efficient spike sorting. To this end, we propose an end-to-end deep learning approach to the problem, utilizing a Fully Convolutional Denoising Autoencoder, which learns to produce a clean neuronal activity signal from a noisy multichannel input. The experimental results on simulated data show that our proposed method can improve significantly the quality of noise-corrupted neural signals, outperforming widely-used wavelet denoising techniques.

Lossless Compression of Intracortical Extracellular Neural Recordings using Non-Adaptive Huffman Encoding.

This paper investigates the effectiveness of four Huffman-based compression schemes for different intracortical neural signals and sample resolutions. The motivation is to find effective lossless, low-complexity data compression schemes for Wireless Intracortical Brain-Machine Interfaces (WI-BMI). The considered schemes include pre-trained Lone 1st and 2nd order encoding [1], pre-trained Delta encoding, and pre-trained Linear Neural Network Time (LNNT) encoding [2]. Maximum codeword-length limited versions are also considered to protect against overfit to training data. The considered signals are the Extracellular Action Potential signal, the Entire Spiking Activity signal, and the Local Field Potential signal. Sample resolutions of 5 to 13 bits are considered. The result show that overfit-protection dramatically improves compression, especially at higher sample resolutions. Across signals, 2nd order encoding generally performed best at lower sample resolutions, and 1st order, Delta and LNNT encoding performed best at higher sample resolutions. The proposed methods should generalise to other remote sensing applications where the distribution of the sensed data can be estimated a priori.

Robust detection of neural spikes using sparse coding based features.

The detection of neural spikes plays an important role in studying and processing extracellular recording signals, which promises to be able to extract the necessary spike data for all subsequent analyses. The existing algorithms for spike detection have achieved great progress but there still remains much room for improvement in terms of the robustness to noise and the flexibility in the spike shape. To address this issue, this paper presents a novel method for spike detection based on the theory of sparse representation. By analyzing the characteristics of extracellular neural recordings, a targetdriven sparse representation framework is firstly constructed, with which the neural spike signals can be effectively separated from background noise. In addition, considering the fact that the spikes emitted by different neurons have different shapes, we then learn a universal dictionary to give a sparse representation of various spike signals. Finally, the information (location and number) of spikes in the recorded signal are achieved by comprehensively analyzing the sparse features. Experimental results demonstrate that the proposed method outperforms the existing methods in the spike detection problem.

How does the presence of neural probes affect extracellular potentials?

Objective. Mechanistic modeling of neurons is an essential component of computational neuroscience that enables scientists to simulate, explain, and explore neural activity. The conventional approach to simulation of extracellular neural recordings first computes transmembrane currents using the cable equation and then sums their contribution to model the extracellular potential. This two-step approach relies on the assumption that the extracellular space is an infinite and homogeneous conductive medium, while measurements are performed using neural probes. The main purpose of this paper is to assess to what extent the presence of the neural probes of varying shape and size impacts the extracellular field and how to correct for them. Approach. We apply a detailed modeling framework allowing explicit representation of the neuron and the probe to study the effect of the probes and thereby estimate the effect of ignoring it. We use meshes with simplified neurons and different types of probe and compare the extracellular action potentials with and without the probe in the extracellular space. We then compare various solutions to account for the probes’ presence and introduce an efficient probe correction method to include the probe effect in modeling of extracellular potentials. Main results. Our computations show that microwires hardly influence the extracellular electric field and their effect can therefore be ignored. In contrast, multi-electrode arrays (MEAs) significantly affect the extracellular field by magnifying the recorded potential. While MEAs behave similarly to infinite insulated planes, we find that their effect strongly depends on the neuron-probe alignment and probe orientation. Significance. Ignoring the probe effect might be deleterious in some applications, such as neural localization and parameterization of neural models from extracellular recordings. Moreover, the presence of the probe can improve the interpretation of extracellular recordings, by providing a more accurate estimation of the extracellular potential generated by neuronal models.

Object-dependent sparse representation for extracellular spike detection

Extracellular neural recordings have become an essential technique for studying and monitoring neural activity, which is of vital importance in understanding brain functions. However, how to accurately and effectively detect spikes from the extracellular recording signals is still a major related challenge. The existing methods for spike detection have achieved much progress while they are vulnerable to the background noise. In this paper, we develop an object-dependent sparse representation framework for high accuracy and robust extracellular spike detection. Specifically, by exploiting the structural similarities of spikes, we construct an object-dependent dictionary to achieve a sparse and comprehensive representation of the recorded signals. Thus, the problem of spike detection can be formulated as a convex sparse optimization problem. Through systematically analyzing the optimal solution, the number and locations of spikes in the recorded signal are finally determined. In addition, singular value decomposition (SVD) is introduced to further improve the flexibility and robustness of the proposed method. Experimental results on both synthesized extracellular neural recordings and real data show that the proposed method outperforms the existing methods.

A silicon neural probe fabricated using DRIE on bonded thin silicon.

A silicon neural probe fabricated using a deep reactive ion etching based process on 250 μm thin silicon wafers was developed. The fabricated probes replicate the design of soft parylene-C based probes embedded in dissolvable needles and can therefore also be used to test the encapsulation properties of parylene-C in-vivo without introducing additional effects introduced by the dissolvable gel. The process also demonstrates the possibility of performing conventional photolithography on substrates bonded to a handle wafer using a backgrinding liquid wax (BGL7080) as an adhesive. This technique would allow integration of Si wafer thinning into the fabrication of neural probes, potentially allowing a range of neural probes of different thicknesses to be fabricated. Fabricated probes were characterized using electrochemical impedance spectroscopy (EIS) yielding a measured impedance value of ~80 kΩ at 1 kHz for 15 μm by 115 μm platinum electrodes, indicating that extracellular neural recordings are possible. The neural probes were inserted into the substantia nigra of a mouse that showed successful recording of neural activity. Probes fabricated using this technique can thus be potentially used in the study of Parkinson's disease.

A real-time spike classification method based on dynamic time warping for extracellular enteric neural recording with large waveform variability

BackgroundComputationally efficient spike recognition methods are required for real-time analysis of extracellular neural recordings. The enteric nervous system (ENS) is important to human health but less well-understood with few appropriate spike recognition algorithms due to large waveform variability. New methodHere we present a method based on dynamic time warping (DTW) with high tolerance to variability in time and magnitude. Adaptive temporal gridding for “fastDTW” in similarity calculation significantly reduces the computational cost. The automated threshold selection allows for real-time classification for extracellular recordings. ResultsOur method is first evaluated on synthesized data at different noise levels, improving both classification accuracy and computational complexity over the conventional cross-correlation based template-matching method (CCTM) and PCA+k-means clustering without time warping. Our method is then applied to analyze the mouse enteric neural recording with mechanical and chemical stimuli. Successful classification of biphasic and monophasic spikes is achieved even when the spike variability is larger than millisecond in width and millivolt in magnitude. Comparison with existing method(s)In comparison with conventional template matching and clustering methods, the fastDTW method is computationally efficient with high tolerance to waveform variability. ConclusionsWe have developed an adaptive fastDTW algorithm for real-time spike classification of ENS recording with large waveform variability against colony motility, ambient changes and cellular heterogeneity.

An Adaptive Recovery Method in Compressed Sensing of Extracellular Neural Recording

A novel adaptive recovery method in the emerging compressed sensing theory is described and applied to extracellular neural recordings in order to reduce data rate in wireless neural recording systems. To strike a balance between high compression ratio and high spike reconstruction quality, a novel method that employs a group-sparsity recovery algorithm, prior information about the input neural signal, learning prior supports of spikes, and a matched wavelet technique is introduced. Our simulation results, using four different sets of real extracellular recordings from four distinct neural sources, show that our proposed method is effective, viable, and outperforms the state-of-the-art compressed sensing-based methods, in particular, when the number of the measurement is two times of the sparsity.

Spike Detection and Clustering With Unsupervised Wavelet Optimization in Extracellular Neural Recordings

Automatic and accurate detection of action potentials of unknown waveforms in noisy extracellular neural recordings is an important requirement for developing brain-computer interfaces. This study introduces a new, wavelet-based manifestation variable that combines the wavelet shrinkage denoising with multiscale edge detection for robustly detecting and finding the occurrence time of action potentials in noisy signals. To further improve the detection performance by eliminating the dependence of the method to the choice of the mother wavelet, we propose an unsupervised optimization for best basis selection. Moreover, another unsupervised criterion based on a correlation similarity measure was defined to update the wavelet selection during the clustering to improve the spike sorting performance. The proposed method was compared to several previously proposed methods by using a wide range of realistic simulated data as well as selected experimental recordings of intracortical signals from freely moving rats. The detection performance of the proposed method substantially surpassed previous methods for all signals tested. Moreover, updating the wavelet selection for the clustering task was shown to improve the classification performance with respect to maintaining the same wavelet as for the detection stage.

Determination of firing frequencies from multiple spike train record

Determination of firing frequencies from multiple spike train record

A comparison of active hearing in male and female Aedes (Stegomyia) aegypti, the dengue-fever mosquito

Event Abstract Back to Event A comparison of active hearing in male and female Aedes (Stegomyia) aegypti, the dengue-fever mosquito Kathleen M. Lucas1* and Daniel Robert1 1 University of Bristol, School of Biological Sciences, United Kingdom As weak sound emitters, mosquitoes are faced with the difficult task of detecting the flight sounds of passing conspecifics, and as a result are equipped with sophisticated hearing organs for sound detection. Male and female mosquitoes use these flight sounds to find each other in a swarm by detecting the particle velocity component of sound using their antennae. Recent studies show that both males and females use sound not only to find each other, but also for sexual recognition, species identification, and mate selection by engaging in harmonic convergence behaviour. While sound detection is important for both male and female mosquitoes, the morphology of their antennae differs, suggesting a functional difference may exist. To explore this, we are studying hearing in the dengue-fever mosquito Aedes (Stegomyia) aegypti. Johnston’s organ (JO), which is the mechanosensory structure at the base of the antennal flagellum responding to nanoscale vibrations, contains over 16 000 sensory neurons in male S. aegypti and 7500 in females. These neurons have been shown to have a dual sensory and motor function that actively enhances the mechanical response of the antenna to sound increasing its detection range. Based on the discrepancy in number of JO neurons, we asked if male and female mosquitoes have different active hearing responses to biologically-relevant stimuli, and whether these differences are reflected at the neural level. When presented with an amplitude-modulated single-frequency tone to mimic a conspecific flyby, both male and female antenna displayed amplification, though in the case of the male this amplification was higher and displayed hysteresis. Simultaneous extracellular neural recordings in JO showed that males have a larger proportion of cells firing at twice the stimulus frequency as females. This twice-frequency forcing is an expression of the synchronization of sets of neurons that mediate active sensing as predicted by modelling. These results suggest that while males and females may both use sound during courtship, the males are equipped with more sensitive hearing in order to detect and follow a female. Acknowledgements Human Frontier Science Program Keywords: Active audition, Extracellular neurophysiology, Hearing, Laser Döppler Vibrometry, mosquito Conference: Tenth International Congress of Neuroethology, College Park. Maryland USA, United States, 5 Aug - 10 Aug, 2012. Presentation Type: Poster (but consider for participant symposium and student poster award) Topic: Communication Citation: Lucas KM and Robert D (2012). A comparison of active hearing in male and female Aedes (Stegomyia) aegypti, the dengue-fever mosquito. Conference Abstract: Tenth International Congress of Neuroethology. doi: 10.3389/conf.fnbeh.2012.27.00342 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 30 Apr 2012; Published Online: 07 Jul 2012. * Correspondence: Ms. Kathleen M Lucas, University of Bristol, School of Biological Sciences, Bristol, United Kingdom, katiemlucas@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Kathleen M Lucas Daniel Robert Google Kathleen M Lucas Daniel Robert Google Scholar Kathleen M Lucas Daniel Robert PubMed Kathleen M Lucas Daniel Robert Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

NeuroQuest: A comprehensive analysis tool for extracellular neural ensemble recordings

Analyzing the massive amounts of neural data collected using microelectrodes to extract biologically relevant information is a major challenge. Many scientific findings rest on the ability to overcome these challenges and to standardize experimental analysis across labs. This can be facilitated in part through comprehensive, efficient and practical software tools disseminated to the community at large. We have developed a comprehensive, MATLAB-based software package – entitled NeuroQuest – that bundles together a number of advanced neural signal processing algorithms in a user-friendly environment. Results demonstrate the efficiency and reliability of the software compared to other software packages, and versatility over a wide range of experimental conditions.

Setting Adaptive Spike Detection Threshold for Smoothed TEO Based on Robust Statistics Theory

We propose a novel approach aimed at adaptively setting the threshold of the smoothed Teager energy operator (STEO) detector to be used in extracellular recording of neural signals. In this proposed approach, to set the adaptive threshold of the STEO detector, we derive the relationship between the low-order statistics of its input signal and the ones of its output signal. This relationship is determined with only the background noise component assumed to be present at the input. Robust statistics theory techniques were used to achieve an unbiased estimation of these low-order statistics of the background noise component directly from the neural input signal. In this paper, the emphasis is made on extracellular neural recordings. However, the proposed method can be used in the analysis of different biomedical signals where spikes are important for diagnostic (e.g., ECG, EEG, etc.). We validated the efficacy of the proposed method using synthetic neural signals constructed from real neural recordings signals. Four different sets of extracellular recordings from four distinct neural sources have been exploited to that purpose. The first dataset is recorded from an adult male monkey using the Utath 10×10 microelectrode array implemented in the prefrontal cortex, the second one was obtained from the visual cortex of a rat using a stainless-steel-tipped microelectrode, the third dataset came from recording in a human medial lobe using intracranial electrode, and finally, the fourth one was extracted from recordings in a macaque parietal cortex using a single tetrode. Simulation results show that our approach is effective and robust, and outperforms state-of-the-art adaptive detection methods in its category (i.e., efficient and simple, and do not require a priori knowledge about neural spike waveforms shapes).

Altered Neural Responses to Sounds in Primate Primary Auditory Cortex during Slow-Wave Sleep

How sounds are processed by the brain during sleep is an important question for understanding how we perceive the sensory environment in this unique behavioral state. While human behavioral data have indicated selective impairments of sound processing during sleep, brain imaging and neurophysiology studies have reported that overall neural activity in auditory cortex during sleep is surprisingly similar to that during wakefulness. This responsiveness to external stimuli leaves open the question of how neural responses during sleep differ, if at all, from wakefulness. Using extracellular neural recordings in the primary auditory cortex of naturally sleeping common marmosets, we show that slow-wave sleep (SWS) alters neural responses in the primate auditory cortex in two specific ways. SWS reduced the sensitivity of auditory cortex such that quiet sounds elicited weak responses in SWS compared with wakefulness, while loud sounds evoked similar responses in SWS and wakefulness. Furthermore, SWS reduced the extent of sound-evoked response suppression. This pattern of alterations was not observed during rapid eye movement sleep and could not be easily explained by the presence of slow rhythms in SWS. The alteration of excitatory and inhibitory responses during SWS suggests limitations in auditory processing and provides novel insights for understanding why certain sounds are processed while others are missed during deep sleep.