Employing ICA and SOM for spike sorting of multielectrode recordings from CNS

DualSort: online spike sorting with a running neural network

Objective.Spike sorting, i.e. the detection and separation of measured action potentials from different extracellularly recorded neurons, remains one of the bottlenecks in deciphering the brain. In recent years, the application of neural networks (NNs) for spike sorting has garnered significant attention. Most methods focus on specific sub-problems within the conventional spike sorting pipeline, such as spike detection or feature extraction, and attempt to solve them with complex network architectures. This paper presents DualSort, a simple NN that gets combined with downstream post-processing for real-time spike sorting. It shows high efficiency, low complexity, and requires a comparatively small amount of human interaction.Approach.Synthetic and experimentally obtained extracellular single-channel recordings were utilized to train and evaluate the proposed NN. For training, spike waveforms were labeled with respect to their associated neuron and position in the signal, allowing the detection and categorization of spikes in unison. DualSort classifies a single spike multiple times in succession, as it runs over the signal in a step-by-step manner and uses a post-processing algorithm that transmits the network output into spike trains. Main results.With the used datasets, DualSort was able to detect and distinguish different spike waveforms and separate them from background activity. The post-processing algorithm significantly strengthened the overall performance of the model, making the system more robust as a whole. Although DualSort is an end-to-end solution that efficiently transforms filtered signals into spike trains, it competes with contemporary state-of-the-art technologies that exclusively target single sub-problems in the conventional spike sorting pipeline.Significance.This work demonstrates that even under high noise levels, complex NNs are not necessary by any means to achieve high performance in spike detection and sorting. The utilization of data augmentation on a limited quantity of spikes could substantially decrease hand-labeling compared to other studies. Furthermore, the proposed framework can be utilized without human interaction when combined with an unsupervised technique that provides pseudo labels for DualSort. Due to the low complexity of our network, it works efficiently and enables real-time processing on basic hardware. The proposed approach is not limited to spike sorting, as it may also be used to process different signals, such as electroencephalogram (EEG), which needs to be investigated in future research.

Global Tactile Coding in Rat Barrel Cortex in the Absence of Local Cues

Although whisker-related perception is based predominantly on local, near-instantaneous coding, global, intensive coding, which integrates the vibrotactile signal over time, has also been shown to play a role given appropriate behavioral conditions. Here, we study global coding in isolation by studying head-fixed rats that identified pulsatile stimuli differing in pulse frequency but not in pulse waveforms, thus abolishing perception based on local coding. We quantified time locking and spike counts as likely variables underpinning the 2 coding schemes. Both neurometric variables contained substantial stimulus information, carried even by spikes of single barrel cortex neurons. To elucidate which type of information is actually used by the rats, we systematically compared psychometric with neurometric sensitivity based on the 2 coding schemes. Neurometric performance was calculated by using a population-encoding model incorporating the properties of our recorded neuron sample. We found that sensitivity calculated from spike counts sampled over long periods (>1 s) matched the performance of rats better than the one carried by spikes time-locked to the stimulus. We conclude that spike counts are more relevant to tactile perception when instantaneous kinematic parameters are not available.

Scaling Spike Detection and Sorting for Next-Generation Electrophysiology

Reliable spike detection and sorting, the process of assigning each detected spike to its originating neuron, are essential steps in the analysis of extracellular electrical recordings from neurons. The volume and complexity of the data from recently developed large-scale, high-density microelectrode arrays and probes, which allow recording from thousands of channels simultaneously, substantially complicate this task conceptually and computationally. This chapter provides a summary and discussion of recently developed methods to tackle these challenges and discusses the important aspect of algorithm validation, and assessment of detection and sorting quality.

Will big data yield new mathematics? An evolving synergy with neuroscience

New mathematics has often been inspired by new insights into the natural world. Here we describe some ongoing and possible future interactions among the massive data sets being collected in neuroscience, methods for their analysis and mathematical models of the underlying, still largely uncharted neural substrates that generate these data. We start by recalling events that occurred in turbulence modelling when substantial space-time velocity field measurements and numerical simulations allowed a new perspective on the governing equations of fluid mechanics. While no analogous global mathematical model of neural processes exists, we argue that big data may enable validation or at least rejection of models at cellular to brain area scales and may illuminate connections among models. We give examples of such models and survey some relatively new experimental technologies, including optogenetics and functional imaging, that can report neural activity in live animals performing complex tasks. The search for analytical techniques for these data is already yielding new mathematics, and we believe their multi-scale nature may help relate well-established models, such as the Hodgkin-Huxley equations for single neurons, to more abstract models of neural circuits, brain areas and larger networks within the brain. In brief, we envisage a closer liaison, if not a marriage, between neuroscience and mathematics.

Vibrotactile discrimination in the rat whisker system is based on neuronal coding of instantaneous kinematic cues

Which physical parameter of vibrissa deflections is extracted by the rodent tactile system for discrimination? Particularly, it remains unclear whether perception has access to instantaneous kinematic parameters (i.e., the details of the trajectory) or relies on temporally integration of the movement trajectory such as frequency (e.g., spectral information) and intensity (e.g., mean speed). Here, we use a novel detection of change paradigm in head-fixed rats, which presents pulsatile vibrissa stimuli in seamless sequence for discrimination. This procedure ensures that processes of decision making can directly tap into sensory signals (no memory functions involved). We find that discrimination performance based on instantaneous kinematic cues far exceeds the ones provided by frequency and intensity. Neuronal modeling based on barrel cortex single units shows that small populations of sensitive neurons provide a transient signal that optimally fits the characteristic of the subject's perception. The present study is the first to show that perceptual read-out is superior in situations allowing the subject to base perception on detailed trajectory cues, that is, instantaneous kinematic variables. A possible impact of this finding on tactile systems of other species is suggested by evidence for instantaneous coding also in primates.

Applicability of independent component analysis on high-density microelectrode array recordings

Emerging complementary metal oxide semiconductor (CMOS)-based, high-density microelectrode array (HD-MEA) devices provide high spatial resolution at subcellular level and a large number of readout channels. These devices allow for simultaneous recording of extracellular activity of a large number of neurons with every neuron being detected by multiple electrodes. To analyze the recorded signals, spiking events have to be assigned to individual neurons, a process referred to as "spike sorting." For a set of observed signals, which constitute a linear mixture of a set of source signals, independent component (IC) analysis (ICA) can be used to demix blindly the data and extract the individual source signals. This technique offers great potential to alleviate the problem of spike sorting in HD-MEA recordings, as it represents an unsupervised method to separate the neuronal sources. The separated sources or ICs then constitute estimates of single-neuron signals, and threshold detection on the ICs yields the sorted spike times. However, it is unknown to what extent extracellular neuronal recordings meet the requirements of ICA. In this paper, we evaluate the applicability of ICA to spike sorting of HD-MEA recordings. The analysis of extracellular neuronal signals, recorded at high spatiotemporal resolution, reveals that the recorded data cannot be modeled as a purely linear mixture. As a consequence, ICA fails to separate completely the neuronal signals and cannot be used as a stand-alone method for spike sorting in HD-MEA recordings. We assessed the demixing performance of ICA using simulated data sets and found that the performance strongly depends on neuronal density and spike amplitude. Furthermore, we show how postprocessing techniques can be used to overcome the most severe limitations of ICA. In combination with these postprocessing techniques, ICA represents a viable method to facilitate rapid spike sorting of multidimensional neuronal recordings.

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.

Fuzzy logic-based spike sorting system

We present a new method for autonomous real-time spike sorting using a fuzzy logic inference engine. The engine assigns each detected event a 'spikiness index' from zero to one that quantifies the extent to which the detected event is like an ideal spike. Spikes can then be sorted by simply clustering the spikiness indices. The sorter is defined in terms of natural language rules that, once defined, are static and thus require no user intervention or calibration. The sorter was tested using extracellular recordings from three animals: a macaque, an owl monkey and a rat. Simulation results show that the fuzzy sorter performed equal to or better than the benchmark principal component analysis (PCA) based sorter. Importantly, there was no degradation in fuzzy sorter performance when the spikes were not temporally aligned prior to sorting. In contrast, PCA sorter performance dropped by 27% when sorting unaligned spikes. Since the fuzzy sorter is computationally trivial and requires no spike alignment, it is suitable for scaling into large numbers of parallel channels where computational overhead and the need for operator intervention would preclude other spike sorters.

Integration of vibrotactile signals for whisker-related perception in rats is governed by short time constants: comparison of neurometric and psychometric detection performance

Rats explore environments by sweeping their whiskers across objects and surfaces. Both sensor movement and repetitive sweeping typical for this behavior require that vibrotactile signals are integrated over time. While temporal integration properties of neurons along the whisker somatosensory pathway have been studied extensively, the consequences for behavior are unknown. Here, we investigate the ability of head-fixed rats to integrate information over time for the detection of near-threshold pulsatile deflection sequences applied to a single whisker. Psychometric detection performance was assessed with whisker stimuli composed of different numbers of pulses (1-31) delivered at varying frequencies (10, 20, 100 Hz). Detection performance indeed improved with increasing number and frequency of pulses, albeit this improvement was much lower than predicted by probabilistic combination, suggesting highly sublinear integration of pulses. This behavioral observation was reflected in the firing properties of concomitantly recorded barrel cortex neurons, which showed substantial response adaptation to repetitive whisker deflection. To estimate the integration time with which barrel cortex neuronal activity must be read out to match behavior, we constructed a model monitoring spiking activity of simulated neuronal pools, where spike trains were channeled through a leaky integrator with exponential decay. Detection was accomplished by simple threshold crossings. This simple model gave an excellent match of neurometric and psychometric data at surprisingly small time constants tau of 5-8 ms, thus limiting integration largely to <25 ms. This result carries important implications regarding sensory processing for whisker-mediated perception.

A miniaturized chronic microelectrode drive for awake behaving head restrained mice and rats

The present work introduces an electrode microdrive system small enough to be placed into two distinct brain areas in head-fixed mice. To meet the space constraint imposed by the size of mice and the additional presence of a head post, the size and weight of the components were minimized. In the current version, an individual microdrive moves an array of four Reitboeck type electrodes. We report about successful implantation in rats and mice using one or two microdrives. Using two of these devices in individual mice/rats, the recording of parallel single and multi-unit as well as local field potential from prefrontal, motor, somatosensory cortex and hippocampus is demonstrated. The system can be easily constructed with machinery and equipment present in most neurophysiology labs.

Psychophysical and neurometric detection performance under stimulus uncertainty

Signal detection theoretical analyses of spike counts have revealed that some cortical neurons can exceed psychophysical sensitivity in cases where a sensory signal is specified exactly. It is not known whether this finding holds in the more natural situation where signal occurrence is temporally uncertain. We investigated the ability of rat barrel cortex neurons to detect faint and transient whisker deflections occurring at unspecified times. The progression from fully specified stimuli to temporal uncertainty degraded neuronal sensitivity such that it seems highly unlikely that single neurons can provide the basis for decoding uncertain perceptual events. However, modeling the sensitivity of neuronal pools on basis of spike timing precision across several neurons in an optimal encoding window of 25 ms showed that the subject's perceptual sensitivity could be based on the occurrence of coincident spikes from four to five neurons.

Responses of Rat Trigeminal Ganglion Neurons to Longitudinal Whisker Stimulation

Responses of rat trigeminal ganglion neurons to longitudinal whisker stimulation. Rats use their mobile set of whiskers to actively explore their environment. Parameters that play a role to generate movement dynamics of the whisker shaft within the follicle, thus activating primary afferents, are manifold: among them are mechanical properties of the whiskers (curvature, elasticity and taper), active movements (head, body, and whiskers), and finally, object characteristics (surface, geometry, position, and orientation). Hence the whisker system is confronted with forces along all three axes in space. Movements along the two latitudinal axes of the whisker (horizontal and vertical) have been well studied. Here we focus on movement along the whisker's longitudinal axis that has been neglected so far. We employed ramp-and-hold movements that pushed the whisker shaft toward the skin and quantified the resulting activity in trigeminal first-order afferents in anesthetized rats. Virtually all recorded neurons were highly sensitive to longitudinal movement. Neurons could be perfectly segregated into two groups according to their modulation by stimulus amplitude and velocity, respectively. This classification regimen correlated perfectly with the presence or absence of slowly adapting responses in longitudinal stimulation but agreed with classification derived from latitudinal stimulation only if the whisker was engaged in its optimal direction and set point. We conclude that longitudinal stimulation is an extremely effective means to activate the tactile pathway and thus is highly likely to play an important role in tactile coding on the ascending somatosensory pathway. In addition, compared with latitudinal stimulation, it provides a reliable and easy to use method to classify trigeminal first-order afferents.

Real-time fetal heart monitoring in biomagnetic measurements using adaptive real-time ICA

Electrophysiological signals of the developing fetal brain and heart can be investigated by fetal magnetoencephalography (fMEG). During such investigations, the fetal heart activity and that of the mother should be monitored continuously to provide an important indication of current well-being. Due to physical constraints of an fMEG system, it is not possible to use clinically established heart monitors for this purpose. Considering this constraint, we developed a real-time heart monitoring system for biomagnetic measurements and showed its reliability and applicability in research and for clinical examinations. The developed system consists of real-time access to fMEG data, an algorithm based on Independent Component Analysis (ICA), and a graphical user interface (GUI). The algorithm extracts the current fetal and maternal heart signal from a noisy and artifact-contaminated data stream in real-time and is able to adapt automatically to continuously varying environmental parameters. This algorithm has been named Adaptive Real-time ICA (ARICA) and is applicable to real-time artifact removal as well as to related blind signal separation problems.

Simulation of sleep spindles and spike and wave discharges using a novel method for the calculation of field potentials in rats

We suggest a new method for calculation of extracellular field potentials generated by a large population of pyramidal cells (PCs), using a single PC compartmental model. Similar methods described earlier use the assumption that the intracellular potential or current distributions of the cells within the population are much alike as a result of simultaneous activation at about the same longitudinal location (i.e., all the PCs in the population are located on the same level and are ideally synchronized). However, the degree of synchronization of natural firing even during synchronized rhythmic discharges in the cortex is not as high. We introduce the possibility to vary the degree of synchronization of the PCs' activity in the population, thus taking into account disperse timing of cortical pyramidal cells' firing. The temporal variability in cell firing is described by a Gaussian distribution, the width of which defines the degree of synchronization/desynchronization. In addition, the suggested method allows for certain spatial spread of PCs in the population along longitudinal axis of the PCs. The method was applied to test the assumption that the transition from sleep spindles to rhythmic spike and wave discharges (SWDs) observed in absence epilepsy may occur due to an increase in pyramidal cells' firing synchronization. We show that in case of weak synchronization of PC firing in the population, the shape of field potential during rhythmic thalamic input is similar to the oscillations during a sleep spindle, while at stronger synchronization of PCs, it looks much more as a SWD, with clear expressed spikes and waves. This suggests that in large population of pyramidal cells the changes in the degree of synchronization of cell firing may explain the changes in the shape of field potential from spindle oscillations to SWDs and vice versa.