NASS: An empirical approach to spike sorting with overlap resolution based on a hybrid noise-assisted methodology

A data-driven spike sorting feature map for resolving spike overlap in the feature space

Objective.Spike sorting is the process of extracting neuronal action potentials, or spikes, from an extracellular brain recording, and assigning each spike to its putative source neuron. Spike sorting is usually treated as a clustering problem. However, this clustering process is known to be affected by overlapping spikes. Existing methods for resolving spike overlap typically require an expensive post-processing of the clustering results. In this paper, we propose the design of a domain-specific feature map, which enables the resolution of spike overlap directly in the feature space.Approach.The proposed domain-specific feature map is based on a neural network architecture that is trained to simultaneously perform spike sorting and spike overlap resolution. Overlapping spikes clusters can be identified in the feature space through a linear relation with the single-neuron clusters for which the neurons contribute to the overlapping spikes. To aid the feature map training, a data augmentation procedure is presented that is based on biophysical simulations.Main results.We demonstrate the potential of our method on independent and realistic test data. We show that our novel approach for resolving spike overlap generalizes to unseen and realistic test data. Furthermore, the sorting performance of our method is shown to be similar to the state-of-the-art, but our method does not assume the availability of spike templates for resolving spike overlap.Significance.Resolving spike overlap directly in the feature space, results in an overall simplified spike sorting pipeline compared to the state-of-the-art. For the state-of-the-art, the overlapping spike snippets exhibit a large spread in the feature space and do not appear as concentrated clusters. This can lead to biased spike template estimates which affect the sorting performance of the state-of-the-art. In our proposed approach, overlapping spikes form concentrated clusters and spike overlap resolution does not depend on the availability of spike templates.

Dysregulation of external globus pallidus-subthalamic nucleus network dynamics in parkinsonian mice during cortical slow-wave activity and activation

KEY POINTS

Reciprocally connected GABAergic external globus pallidus (GPe) and glutamatergic subthalamic nucleus (STN) neurons form a key network within the basal ganglia. In Parkinson's disease and its models, abnormal rates and patterns of GPe-STN network activity are linked to motor dysfunction. Using cell class-specific optogenetic identification and inhibition during cortical slow-wave activity and activation, we report that, in dopamine-depleted mice, (1) D2 dopamine receptor expressing striatal projection neurons (D2-SPNs) discharge at higher rates, especially during cortical activation, (2) prototypic parvalbumin-expressing GPe neurons are excessively patterned by D2-SPNs even though their autonomous activity is upregulated, (3) despite being disinhibited, STN neurons are not hyperactive, and (4) STN activity opposes striatopallidal patterning. These data argue that in parkinsonian mice abnormal, temporally offset prototypic GPe and STN neuron firing results in part from increased striatopallidal transmission and that compensatory plasticity limits STN hyperactivity and cortical entrainment.

ABSTRACT

Reciprocally connected GABAergic external globus pallidus (GPe) and glutamatergic subthalamic nucleus (STN) neurons form a key, centrally positioned network within the basal ganglia. In Parkinson's disease and its models, abnormal rates and patterns of GPe-STN network activity are linked to motor dysfunction. Following the loss of dopamine, the activities of GPe and STN neurons become more temporally offset and strongly correlated with cortical oscillations below 40 Hz. Previous studies utilized cortical slow-wave activity and/or cortical activation (ACT) under anaesthesia to probe the mechanisms underlying the normal and pathological patterning of basal ganglia activity. Here, we combined this approach with in vivo optogenetic inhibition to identify and interrupt the activity of D2 dopamine receptor-expressing striatal projection neurons (D2-SPNs), parvalbumin-expressing prototypic GPe (PV GPe) neurons, and STN neurons. We found that, in dopamine-depleted mice, (1) the firing rate of D2-SPNs was elevated, especially during cortical ACT, (2) abnormal phasic suppression of PV GPe neuron activity was ameliorated by optogenetic inhibition of coincident D2-SPN activity, (3) autonomous PV GPe neuron firing ex vivo was upregulated, presumably through homeostatic mechanisms, (4) STN neurons were not hyperactive, despite being disinhibited, (5) optogenetic inhibition of the STN exacerbated abnormal GPe activity, and (6) exaggerated beta band activity was not present in the cortex or GPe-STN network. Together with recent studies, these data suggest that in dopamine-depleted mice abnormally correlated and temporally offset PV GPe and STN neuron activity is generated in part by elevated striatopallidal transmission, while compensatory plasticity prevents STN hyperactivity and limits cortical entrainment.

Effective Dimensionality Reduction for Visualizing Neural Dynamics by Laplacian Eigenmaps

With the development of neural recording technology, it has become possible to collect activities from hundreds or even thousands of neurons simultaneously. Visualization of neural population dynamics can help neuroscientists analyze large-scale neural activities efficiently. In this letter, Laplacian eigenmaps is applied to this task for the first time, and the experimental results show that the proposed method significantly outperforms the commonly used methods. This finding was confirmed by the systematic evaluation using nonhuman primate data, which contained the complex dynamics well suited for testing. According to our results, Laplacian eigenmaps is better than the other methods in various ways and can clearly visualize interesting biological phenomena related to neural dynamics.

WMsorting: Wavelet Packets Decomposition and Mutual Information Based Spike Sorting Method

In recent years, the signal processing opportunities with the multi-channel recording and the high precision detection provided by the development of new extracellular multielectrodes are increasing. Hence, designing new spike sorting algorithms are both attractive and challenging. These algorithms are used to distinguish the individual neurons' activity from the dense and simultaneously recorded neural action potentials with high accuracy. However, since the overlapping phenomenon often inevitably arises in the recorded data, they are not accurate enough in practical situations especially when the noise level is high. In this paper, a spike feature extraction method based on the Wavelet Packets Decomposition and the Mutual Information is proposed. This is a highly accurate semi-supervised solution with a short training phase for performing the automation of the spike sorting framework. Further, the evaluations are performed on different public datasets. The raw data is not only suffered from multiple noises (from 5% level to 20% level) but also includes various degrees of overlapping spikes at different times. The clustering results demonstrate the effectiveness of our proposed algorithm. Also, it achieves a good anti-noise performance with ensuring a high clustering accuracy (up to 99.76%) compared with the state of art methods.

Spatial extreme learning machines: An application on prediction of disease counts

Extreme learning machines have gained a lot of attention by the machine learning community because of its interesting properties and computational advantages. With the increase in collection of information nowadays, many sources of data have missing information making statistical analysis harder or unfeasible. In this paper, we present a new model, coined spatial extreme learning machine, that combine spatial modeling with extreme learning machines keeping the nice properties of both methodologies and making it very flexible and robust. As explained throughout the text, the spatial extreme learning machines have many advantages in comparison with the traditional extreme learning machines. By a simulation study and a real data analysis we present how the spatial extreme learning machine can be used to improve imputation of missing data and uncertainty prediction estimation.

Sorting Overlapping Spike Waveforms from Electrode and Tetrode Recordings

One of the outstanding problems in the sorting of neuronal spike trains is the resolution of overlapping spikes. Resolving these spikes can significantly improve a range of analyses, such as response variability, correlation, and latency. In this paper, we describe a partially automated method that is capable of resolving overlapping spikes. After constructing template waveforms for well-isolated and distinct single units, we generated pair-wise combinations of those templates at all possible time shifts from each other. Subsequently, overlapping waveforms were identified by cluster analysis, and then assigned to their respective single-unit combinations. We examined the performance of this method using simulated data from an earlier study, and found that we were able to resolve an average of 83% of the overlapping waveforms across various signal-to-noise ratios, an improvement of approximately 32% over the results reported in the earlier study. When applied to additional simulated data sets generated from single-electrode and tetrode recordings, we were able to resolve 91% of the overlapping waveforms with a false positive rate of 0.19% for single-electrode data, and 95% of the overlapping waveforms with a false positive rate of 0.27% for tetrode data. We also applied our method to electrode and tetrode data recorded from the primary visual cortex, and the results obtained for these datasets suggest that our method provides an efficient means of sorting overlapping waveforms. This method can easily be added as an extra step to commonly used spike sorting methods, such as KlustaKwik and MClust software packages, and can be applied to datasets that have already been sorted using these methods.

Spontaneous Up states in vitro: a single-metric index of the functional maturation and regional differentiation of the cerebral cortex

Understanding the development and differentiation of the neocortex remains a central focus of neuroscience. While previous studies have examined isolated aspects of cellular and synaptic organization, an integrated functional index of the cortical microcircuit is still lacking. Here we aimed to provide such an index, in the form of spontaneously recurring periods of persistent network activity -or Up states- recorded in mouse cortical slices. These coordinated network dynamics emerge through the orchestrated regulation of multiple cellular and synaptic elements and represent the default activity of the cortical microcircuit. To explore whether spontaneous Up states can capture developmental changes in intracortical networks we obtained local field potential recordings throughout the mouse lifespan. Two independent and complementary methodologies revealed that Up state activity is systematically modified by age, with the largest changes occurring during early development and adolescence. To explore possible regional heterogeneities we also compared the development of Up states in two distinct cortical areas and show that primary somatosensory cortex develops at a faster pace than primary motor cortex. Our findings suggest that in vitro Up states can serve as a functional index of cortical development and differentiation and can provide a baseline for comparing experimental and/or genetic mouse models.

Functional aspects of the EGF-induced MAP kinase cascade: a complex self-organizing system approach

The EGF-induced MAP kinase cascade is one of the most important and best characterized networks in intracellular signalling. It has a vital role in the development and maturation of living organisms. However, when deregulated, it is involved in the onset of a number of diseases. Based on a computational model describing a "surface" and an "internalized" parallel route, we use systems biology techniques to characterize aspects of the network's functional organization. We examine the re-organization of protein groups from low to high external stimulation, define functional groups of proteins within the network, determine the parameter best encoding for input intensity and predict the effect of protein removal to the system's output response. Extensive functional re-organization of proteins is observed in the lower end of stimulus concentrations. As we move to higher concentrations the variability is less pronounced. 6 functional groups have emerged from a consensus clustering approach, reflecting different dynamical aspects of the network. Mutual information investigation revealed that the maximum activation rate of the two output proteins best encodes for stimulus intensity. Removal of each protein of the network resulted in a range of graded effects, from complete silencing to intense activation. Our results provide a new "vista" of the EGF-induced MAP kinase cascade, from the perspective of complex self-organizing systems. Functional grouping of the proteins reveals an organizational scheme contrasting the current understanding of modular topology. The six identified groups may provide the means to experimentally follow the dynamics of this complex network. Also, the vulnerability analysis approach may be used for the development of novel therapeutic targets in the context of personalized medicine.

Trends in extreme learning machines: a review

Extreme learning machine (ELM) has gained increasing interest from various research fields recently. In this review, we aim to report the current state of the theoretical research and practical advances on this subject. We first give an overview of ELM from the theoretical perspective, including the interpolation theory, universal approximation capability, and generalization ability. Then we focus on the various improvements made to ELM which further improve its stability, sparsity and accuracy under general or specific conditions. Apart from classification and regression, ELM has recently been extended for clustering, feature selection, representational learning and many other learning tasks. These newly emerging algorithms greatly expand the applications of ELM. From implementation aspect, hardware implementation and parallel computation techniques have substantially sped up the training of ELM, making it feasible for big data processing and real-time reasoning. Due to its remarkable efficiency, simplicity, and impressive generalization performance, ELM have been applied in a variety of domains, such as biomedical engineering, computer vision, system identification, and control and robotics. In this review, we try to provide a comprehensive view of these advances in ELM together with its future perspectives.

High-Throughput Hardware for Real-Time Spike Overlap Decomposition in Multi-Electrode Neuronal Recording Systems

Spike overlaps occur frequently in dense neuronal network recordings, creating difficulties for spike sorting. Brainmachine interfaces and in vivo studies of neuronal network dynamics often require that an accurate spike sorting be done in real time, with low execution latency (on the order of milliseconds). Moreover, modern neuronal recording systems that feature thousands of electrodes require processing of several tens or hundreds of neurons in parallel. The existing algorithms capable of performing spike overlap decomposition are generally very complex and unsuitable for real-time implementation, especially for an on-chip implementation. Here we present a hardware device capable of processing pair-wise spike overlaps in real time. A previously-published spike sorting algorithm, which is not suitable for processing data of large neuronal networks with low latency, has been optimized for high-throughput, low-latency hardware implementation. The designed hardware architecture has been verified on an FPGA platform. Low spike sorting error rates (0.05) for overlapping spikes have been achieved with a latency of 2.75 ms, rendering the system particularly suitable for use in closed-loop experiments.

Multichannel electrophysiological spike sorting via joint dictionary learning and mixture modeling

We propose a methodology for joint feature learning and clustering of multichannel extracellular electrophysiological data, across multiple recording periods for action potential detection and classification (sorting). Our methodology improves over the previous state of the art principally in four ways. First, via sharing information across channels, we can better distinguish between single-unit spikes and artifacts. Second, our proposed "focused mixture model" (FMM) deals with units appearing, disappearing, or reappearing over multiple recording days, an important consideration for any chronic experiment. Third, by jointly learning features and clusters, we improve performance over previous attempts that proceeded via a two-stage learning process. Fourth, by directly modeling spike rate, we improve the detection of sparsely firing neurons. Moreover, our Bayesian methodology seamlessly handles missing data. We present the state-of-the-art performance without requiring manually tuning hyperparameters, considering both a public dataset with partial ground truth and a new experimental dataset.

In quest of the missing neuron: spike sorting based on dominant-sets clustering

Spike sorting algorithms aim at decomposing complex extracellular signals to independent events from single neurons in the electrode's vicinity. The decision about the actual number of active neurons is still an open issue, with sparsely firing neurons and background activity the most influencing factors. We introduce a graph-theoretical algorithmic procedure that successfully resolves this issue. Dimensionality reduction coupled with a modern, efficient and progressively executable clustering routine proved to achieve higher performance standards than popular spike sorting methods. Our method is validated extensively using simulated data for different levels of SNR.