Spike sorting for large, dense electrode arrays

Collection of Simulated Data from a Thalamocortical Network Model

A major challenge in experimental data analysis is the validation of analytical methods in a fully controlled scenario where the justification of the interpretation can be made directly and not just by plausibility. In some sciences, this could be a mathematical proof, yet biological systems usually do not satisfy assumptions of mathematical theorems. One solution is to use simulations of realistic models to generate ground truth data. In neuroscience, creating such data requires plausible models of neural activity, access to high performance computers, expertise and time to prepare and run the simulations, and to process the output. To facilitate such validation tests of analytical methods we provide rich data sets including intracellular voltage traces, transmembrane currents, morphologies, and spike times. Moreover, these data can be used to study the effects of different tissue models on the measurement. The data were generated using the largest publicly available multicompartmental model of thalamocortical network (Traub et al., Journal of Neurophysiology, 93(4), 2194–2232 (Traub et al. 2005)), with activity evoked by different thalamic stimuli.

Automated long-term recording and analysis of neural activity in behaving animals

Addressing how neural circuits underlie behavior is routinely done by measuring electrical activity from single neurons in experimental sessions. While such recordings yield snapshots of neural dynamics during specified tasks, they are ill-suited for tracking single-unit activity over longer timescales relevant for most developmental and learning processes, or for capturing neural dynamics across different behavioral states. Here we describe an automated platform for continuous long-term recordings of neural activity and behavior in freely moving rodents. An unsupervised algorithm identifies and tracks the activity of single units over weeks of recording, dramatically simplifying the analysis of large datasets. Months-long recordings from motor cortex and striatum made and analyzed with our system revealed remarkable stability in basic neuronal properties, such as firing rates and inter-spike interval distributions. Interneuronal correlations and the representation of different movements and behaviors were similarly stable. This establishes the feasibility of high-throughput long-term extracellular recordings in behaving animals.

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.

Model-based spike sorting with a mixture of drifting t-distributions

BACKGROUND

Chronic extracellular recordings are a powerful tool for systems neuroscience, but spike sorting remains a challenge. A common approach is to fit a generative model, such as a mixture of Gaussians, to the observed spike data. Even if non-parametric methods are used for spike sorting, such generative models provide a quantitative measure of unit isolation quality, which is crucial for subsequent interpretation of the sorted spike trains.

NEW METHOD

We present a spike sorting strategy that models the data as a mixture of drifting t-distributions. This model captures two important features of chronic extracellular recordings-cluster drift over time and heavy tails in the distribution of spikes-and offers improved robustness to outliers.

RESULTS

We evaluate this model on several thousand hours of chronic tetrode recordings and show that it fits the empirical data substantially better than a mixture of Gaussians. We also provide a software implementation that can re-fit long datasets in a few seconds, enabling interactive clustering of chronic recordings.

COMPARISON WITH EXISTING METHODS

We identify three common failure modes of spike sorting methods that assume stationarity and evaluate their impact given the empirically-observed cluster drift in chronic recordings. Using hybrid ground truth datasets, we also demonstrate that our model-based estimate of misclassification error is more accurate than previous unit isolation metrics.

CONCLUSIONS

The mixture of drifting t-distributions model enables efficient spike sorting of long datasets and provides an accurate measure of unit isolation quality over a wide range of conditions.

TaiNi: Maximizing research output whilst improving animals' welfare in neurophysiology experiments

Understanding brain function at the cell and circuit level requires representation of neuronal activity through multiple recording sites and at high sampling rates. Traditional tethered recording systems restrict movement and limit the environments suitable for testing, while existing wireless technology is still too heavy for extended recording in mice. Here we tested TaiNi, a novel ultra-lightweight (<2 g) low power wireless system allowing 72-hours of recording from 16 channels sampled at ~19.5 KHz (9.7 KHz bandwidth). We captured local field potentials and action-potentials while mice engaged in unrestricted behaviour in a variety of environments and while performing tasks. Data was synchronized to behaviour with sub-second precision. Comparisons with a state-of-the-art wireless system demonstrated a significant improvement in behaviour owing to reduced weight. Parallel recordings with a tethered system revealed similar spike detection and clustering. TaiNi represents a significant advance in both animal welfare in electrophysiological experiments, and the scope for continuously recording large amounts of data from small animals.

Nanoelectronic Coating Enabled Versatile Multifunctional Neural Probes

Brain function can be best studied by simultaneous measurements and modulation of the multifaceted signaling at the cellular scale. Extensive efforts have been made to develop multifunctional neural probes, typically involving highly specialized fabrication processes. Here, we report a novel multifunctional neural probe platform realized by applying ultrathin nanoelectronic coating (NEC) on the surfaces of conventional microscale devices such as optical fibers and micropipettes. We fabricated the NECs by planar photolithography techniques using a substrate-less and multilayer design, which host arrays of individually addressed electrodes with an overall thickness below 1 μm. Guided by an analytic model and taking advantage of the surface tension, we precisely aligned and coated the NEC devices on the surfaces of these conventional microprobes and enabled electrical recording capabilities on par with the state-of-the-art neural electrodes. We further demonstrated optogenetic stimulation and controlled drug infusion with simultaneous, spatially resolved neural recording in a rodent model. This study provides a low-cost, versatile approach to construct multifunctional neural probes that can be applied to both fundamental and translational neuroscience.

Behaviorally Selective Engagement of Short-Latency Effector Pathways by Motor Cortex

Blocking motor cortical output with lesions or pharmacological inactivation has identified movements that require motor cortex. Yet, when and how motor cortex influences muscle activity during movement execution remains unresolved. We addressed this ambiguity using measurement and perturbation of motor cortical activity together with electromyography in mice during two forelimb movements that differ in their requirement for cortical involvement. Rapid optogenetic silencing and electrical stimulation indicated that short-latency pathways linking motor cortex with spinal motor neurons are selectively activated during one behavior. Analysis of motor cortical activity revealed a dramatic change between behaviors in the coordination of firing patterns across neurons that could account for this differential influence. Thus, our results suggest that changes in motor cortical output patterns enable a behaviorally selective engagement of short-latency effector pathways. The model of motor cortical influence implied by our findings helps reconcile previous observations on the function of motor cortex.

Precise spike timing dynamics of hippocampal place cell activity sensitive to cholinergic disruption

New memory formation depends on both the hippocampus and modulatory effects of acetylcholine. The mechanism by which acetylcholine levels in the hippocampus enable new encoding remains poorly understood. Here, we tested the hypothesis that cholinergic modulation supports memory formation by leading to structured spike timing in the hippocampus. Specifically, we tested if phase precession in dorsal CA1 was reduced under the influence of a systemic cholinergic antagonist. Unit and field potential were recorded from the dorsal CA1 of rats as they completed laps on a circular track for food rewards before and during the influence of the systemically administered acetylcholine muscarinic receptor antagonist scopolamine. We found that scopolamine significantly reduced phase precession of spiking relative to the field theta, and that this was due to a decrease in the frequency of the spiking rhythmicity. We also found that the correlation between position and theta phase was significantly reduced. This effect was not due to changes in spatial tuning as tuning remained stable for those cells analyzed. Similarly, it was not due to changes in lap‐to‐lap reliability of spiking onset or offset relative to either position or phase as the reliability did not decrease following scopolamine administration. These findings support the hypothesis that memory impairments that follow muscarinic blockade are the result of degraded spike timing in the hippocampus.

Multisite Electrophysiology Recordings in Mice to Study Cross-Regional Communication During Anxiety

Recording neural activity in awake, freely moving mice is a powerful and flexible technique for dissecting the neural circuit mechanisms underlying pathological behavior. This unit describes protocols for designing a drive and recording single neurons and local field potentials during anxiety-related paradigms. We also include protocols for integrating pharmacologic and optogenetic means for circuit manipulations, which, when combined with electrophysiological recordings, demonstrate input-specific and cell-specific contributions to circuit-wide activity. We discuss the planning, execution, and troubleshooting of physiology experiments during anxiety-like behavior. © 2017 by John Wiley & Sons, Inc.

Cell diversity and network dynamics in photosensitive human brain organoids

In vitro models of the developing brain such as three-dimensional brain organoids offer an unprecedented opportunity to study aspects of human brain development and disease. However, the cells generated within organoids and the extent to which they recapitulate the regional complexity, cellular diversity and circuit functionality of the brain remain undefined. Here we analyse gene expression in over 80,000 individual cells isolated from 31 human brain organoids. We find that organoids can generate a broad diversity of cells, which are related to endogenous classes, including cells from the cerebral cortex and the retina. Organoids could be developed over extended periods (more than 9 months), allowing for the establishment of relatively mature features, including the formation of dendritic spines and spontaneously active neuronal networks. Finally, neuronal activity within organoids could be controlled using light stimulation of photosensitive cells, which may offer a way to probe the functionality of human neuronal circuits using physiological sensory stimuli.

Distinct gamma oscillations in the distal dendritic fields of the dentate gyrus and the CA1 area of mouse hippocampus

The molecular layer of the dentate gyrus and the anatomically adjacent stratum lacunosum-moleculare of CA1 area, represent afferent areas at distinct levels of the hippocampal trisynaptic loop. Afferents to the dentate gyrus and CA1 area originate from different cell populations, including projection cells in entorhinal cortex layers two and three, respectively. To determine the organization of oscillatory activities along these terminal fields, we recorded local field potentials from multiple sites in the dentate gyrus and CA1 area of the awake mice, and localized gamma frequency (30–150 Hz) oscillations in different layers by means of current source density analysis. During theta oscillations, we observed different temporal and spectral organization of gamma oscillations in the dendritic layers of the dentate gyrus and CA1 area, with a sharp transition across the hippocampal fissure. In CA1 stratum lacunosum-moleculare, transient mid-frequency gamma oscillations (CA1-gamma_M; 80 Hz) occurred on theta cycle peaks, while in the dentate gyrus, fast (DG-gamma_F; 110 Hz), and slow (DG-gamma_S; 40 Hz) gamma oscillations preferentially occurred on troughs of theta waves. Units in dentate gyrus, in contrast to units in CA1 pyramidal layer, phase-coupled to DG-gamma_F, which was largely independent from CA1 fast gamma oscillations (CA1-gamma_F) of similar frequency and timing. Spike timing of units recorded in either CA1 area or dentate gyrus were modulated by CA1-gamma_M. Our experiments disclosed a set of gamma oscillations that differentially regulate neuronal activity in the dentate gyrus and CA1 area, and may allow flexible segregation and integration of information across different levels of hippocampal circuitry.

Complementary codes for odor identity and intensity in olfactory cortex

The ability to represent both stimulus identity and intensity is fundamental for perception. Using large-scale population recordings in awake mice, we find distinct coding strategies facilitate non-interfering representations of odor identity and intensity in piriform cortex. Simply knowing which neurons were activated is sufficient to accurately represent odor identity, with no additional information about identity provided by spike time or spike count. Decoding analyses indicate that cortical odor representations are not sparse. Odorant concentration had no systematic effect on spike counts, indicating that rate cannot encode intensity. Instead, odor intensity can be encoded by temporal features of the population response. We found a subpopulation of rapid, largely concentration-invariant responses was followed by another population of responses whose latencies systematically decreased at higher concentrations. Cortical inhibition transforms olfactory bulb output to sharpen these dynamics. Our data therefore reveal complementary coding strategies that can selectively represent distinct features of a stimulus.

DOI:http://dx.doi.org/10.7554/eLife.22630.001

Organization of prefrontal network activity by respiration-related oscillations

The medial prefrontal cortex (mPFC) integrates information from cortical and sub-cortical areas and contributes to the planning and initiation of behaviour. A potential mechanism for signal integration in the mPFC lies in the synchronization of neuronal discharges by theta (6–12 Hz) activity patterns. Here we show, using in vivo local field potential (LFP) and single-unit recordings from awake mice, that prominent oscillations in the sub-theta frequency band (1–5 Hz) emerge during awake immobility in the mPFC. These oscillation patterns are distinct from but phase-locked to hippocampal theta activity and occur synchronized with nasal respiration (hence termed prefrontal respiration rhythm [PRR]). PRR activity modulates the amplitude of prefrontal gamma rhythms with greater efficacy than theta oscillations. Furthermore, single-unit discharges of putative pyramidal cells and GABAergic interneurons are entrained by prefrontal PRR and nasal respiration. Our data thus suggest that PRR activity contributes to information processing in the prefrontal neuronal network.

Robust spike sorting of retinal ganglion cells tuned to spot stimuli

We propose an automatic spike sorting approach for the data recorded from a microelectrode array during visual stimulation of wild type retinas with tiled spot stimuli. The approach first detects individual spikes per electrode by their signature local minima. With the mixture probability distribution of the local minima estimated afterwards, it applies a minimum-squared-error clustering algorithm to sort the spikes into different clusters. A template waveform for each cluster per electrode is defined, and a number of reliability tests are performed on it and its corresponding spikes. Finally, a divisive hierarchical clustering algorithm is used to deal with the correlated templates per cluster type across all the electrodes. According to the measures of performance of the spike sorting approach, it is robust even in the cases of recordings with low signal-to-noise ratio.

Population Coding in an Innately Relevant Olfactory Area

Different olfactory cortical regions are thought to harbor distinct sensory representations, enabling each area to play a unique role in odor perception and behavior. In the piriform cortex (PCx), spatially dispersed sensory inputs evoke activity in distributed ensembles of neurons that act as substrates for odor learning. In contrast, the posterolateral cortical amygdala (plCoA) receives hardwired inputs that may link specific odor cues to innate olfactory behaviors. Here we show that despite stark differences in the patterning of plCoA and PCx inputs, odor-evoked neural ensembles in both areas are equally capable of discriminating odors, and exhibit similar odor tuning, reliability, and correlation structure. These results demonstrate that brain regions mediating odor-driven innate behaviors can, like brain areas involved in odor learning, represent odor objects using distributive population codes; these findings suggest both alternative mechanisms for the generation of innate odor-driven behaviors and additional roles for the plCoA in odor perception.

A Visual Guide to Sorting Electrophysiological Recordings Using 'SpikeSorter'

Few stand-alone software applications are available for sorting spikes from recordings made with multi-electrode arrays. Ideally, an application should be user friendly with a graphical user interface, able to read data files in a variety of formats, and provide users with a flexible set of tools giving them the ability to detect and sort extracellular voltage waveforms from different units with some degree of reliability. Previously published spike sorting methods are now available in a software program, SpikeSorter, intended to provide electrophysiologists with a complete set of tools for sorting, starting from raw recorded data file and ending with the export of sorted spikes times. Procedures are automated to the extent this is currently possible. The article explains and illustrates the use of the program. A representative data file is opened, extracellular traces are filtered, events are detected and then clustered. A number of problems that commonly occur during sorting are illustrated, including the artefactual over-splitting of units due to the tendency of some units to fire spikes in pairs where the second spike is significantly smaller than the first, and over-splitting caused by slow variation in spike height over time encountered in some units. The accuracy of SpikeSorter's performance has been tested with surrogate ground truth data and found to be comparable to that of other algorithms in current development.

State-of-the-art MEMS and microsystem tools for brain research

Mapping brain activity has received growing worldwide interest because it is expected to improve disease treatment and allow for the development of important neuromorphic computational methods. MEMS and microsystems are expected to continue to offer new and exciting solutions to meet the need for high-density, high-fidelity neural interfaces. Herein, the state-of-the-art in recording and stimulation tools for brain research is reviewed, and some of the most significant technology trends shaping the field of neurotechnology are discussed.

Movement Initiation Signals in Mouse Whisker Motor Cortex

Frontal cortex plays a central role in the control of voluntary movements, which are typically guided by sensory input. Here, we investigate the function of mouse whisker primary motor cortex (wM1), a frontal region defined by dense innervation from whisker primary somatosensory cortex (wS1). Optogenetic stimulation of wM1 evokes rhythmic whisker protraction (whisking), whereas optogenetic inactivation of wM1 suppresses initiation of whisking. Whole-cell membrane potential recordings and silicon probe recordings of action potentials reveal layer-specific neuronal activity in wM1 at movement initiation, and encoding of fast and slow parameters of movements during whisking. Interestingly, optogenetic inactivation of wS1 caused hyperpolarization and reduced firing in wM1, together with reduced whisking. Optogenetic stimulation of wS1 drove activity in wM1 with complex dynamics, as well as evoking long-latency, wM1-dependent whisking. Our results advance understanding of a well-defined frontal region and point to an important role for sensory input in controlling motor cortex.

Reliable Analysis of Single-Unit Recordings from the Human Brain under Noisy Conditions: Tracking Neurons over Hours

Recording extracellulary from neurons in the brains of animals in vivo is among the most established experimental techniques in neuroscience, and has recently become feasible in humans. Many interesting scientific questions can be addressed only when extracellular recordings last several hours, and when individual neurons are tracked throughout the entire recording. Such questions regard, for example, neuronal mechanisms of learning and memory consolidation, and the generation of epileptic seizures. Several difficulties have so far limited the use of extracellular multi-hour recordings in neuroscience: Datasets become huge, and data are necessarily noisy in clinical recording environments. No methods for spike sorting of such recordings have been available. Spike sorting refers to the process of identifying the contributions of several neurons to the signal recorded in one electrode. To overcome these difficulties, we developed Combinato: a complete data-analysis framework for spike sorting in noisy recordings lasting twelve hours or more. Our framework includes software for artifact rejection, automatic spike sorting, manual optimization, and efficient visualization of results. Our completely automatic framework excels at two tasks: It outperforms existing methods when tested on simulated and real data, and it enables researchers to analyze multi-hour recordings. We evaluated our methods on both short and multi-hour simulated datasets. To evaluate the performance of our methods in an actual neuroscientific experiment, we used data from from neurosurgical patients, recorded in order to identify visually responsive neurons in the medial temporal lobe. These neurons responded to the semantic content, rather than to visual features, of a given stimulus. To test our methods with multi-hour recordings, we made use of neurons in the human medial temporal lobe that respond selectively to the same stimulus in the evening and next morning.

Inhibitory control of correlated intrinsic variability in cortical networks

Cortical networks exhibit intrinsic dynamics that drive coordinated, large-scale fluctuations across neuronal populations and create noise correlations that impact sensory coding. To investigate the network-level mechanisms that underlie these dynamics, we developed novel computational techniques to fit a deterministic spiking network model directly to multi-neuron recordings from different rodent species, sensory modalities, and behavioral states. The model generated correlated variability without external noise and accurately reproduced the diverse activity patterns in our recordings. Analysis of the model parameters suggested that differences in noise correlations across recordings were due primarily to differences in the strength of feedback inhibition. Further analysis of our recordings confirmed that putative inhibitory neurons were indeed more active during desynchronized cortical states with weak noise correlations. Our results demonstrate that network models with intrinsically-generated variability can accurately reproduce the activity patterns observed in multi-neuron recordings and suggest that inhibition modulates the interactions between intrinsic dynamics and sensory inputs to control the strength of noise correlations.

DOI:http://dx.doi.org/10.7554/eLife.19695.001

Our brains contain billions of neurons, which are continually producing electrical signals to relay information around the brain. Yet most of our knowledge of how the brain works comes from studying the activity of one neuron at a time. Recently, studies of multiple neurons have shown that they tend to be active together in short bursts called “up” states, which are followed by periods in which they are less active called “down” states. When we are sleeping or under a general anesthetic, the neurons may be completely silent during down states, but when we are awake the difference in activity between the two states is usually less extreme. However, it is still not clear how the neurons generate these patterns of activity.

To address this question, Stringer et al. studied the activity of neurons in the brains of awake and anesthetized rats, mice and gerbils. The experiments recorded electrical activity from many neurons at the same time and found a wide range of different activity patterns. A computational model based on these data suggests that differences in the degree to which some neurons suppress the activity of other neurons may account for this variety. Increasing the strength of these inhibitory signals in the model decreased the fluctuations in electrical activity across entire areas of the brain. Further analysis of the experimental data supported the model’s predictions by showing that inhibitory neurons – which act to reduce electrical activity in other neurons – were more active when there were fewer fluctuations in activity across the brain.

The next step following on from this work would be to develop ways to build computer models that can mimic the activity of many more neurons at the same time. The models could then be used to interpret the electrical activity produced by many different kinds of neuron. This will enable researchers to test more sophisticated hypotheses about how the brain works.

DOI:http://dx.doi.org/10.7554/eLife.19695.002

Gamma frequency entrainment attenuates amyloid load and modifies microglia

Changes in gamma oscillations (20-50 Hz) have been observed in several neurological disorders. However, the relationship between gamma oscillations and cellular pathologies is unclear. Here we show reduced, behaviourally driven gamma oscillations before the onset of plaque formation or cognitive decline in a mouse model of Alzheimer's disease. Optogenetically driving fast-spiking parvalbumin-positive (FS-PV)-interneurons at gamma (40 Hz), but not other frequencies, reduces levels of amyloid-β (Aβ)1-40 and Aβ 1-42 isoforms. Gene expression profiling revealed induction of genes associated with morphological transformation of microglia, and histological analysis confirmed increased microglia co-localization with Aβ. Subsequently, we designed a non-invasive 40 Hz light-flickering regime that reduced Aβ1-40 and Aβ1-42 levels in the visual cortex of pre-depositing mice and mitigated plaque load in aged, depositing mice. Our findings uncover a previously unappreciated function of gamma rhythms in recruiting both neuronal and glial responses to attenuate Alzheimer's-disease-associated pathology.

Glutamatergic synaptic integration of locomotion speed via septoentorhinal projections

The medial septum and diagonal band of Broca (MSDB) send glutamatergic axons to medial entorhinal cortex (MEC). We found that this pathway provides speed-correlated input to several MEC cell-types in layer 2/3. The speed signal is integrated most effectively by pyramidal cells but also excites stellate cells and interneurons. Thus, the MSDB conveys speed information that can be used by MEC neurons for spatial representation of self-location.

Regular Cycles of Forward and Backward Signal Propagation in Prefrontal Cortex and in Consciousness

This paper addresses two fundamental questions: (1) Is it possible to develop mathematical neural network models which can explain and replicate the way in which higher-order capabilities like intelligence, consciousness, optimization, and prediction emerge from the process of learning (Werbos, 1994, 2016a; National Science Foundation, 2008)? and (2) How can we use and test such models in a practical way, to track, to analyze and to model high-frequency (≥ 500 hz) many-channel data from recording the brain, just as econometrics sometimes uses models grounded in the theory of efficient markets to track real-world time-series data (Werbos, 1990)? This paper first reviews some of the prior work addressing question (1), and then reports new work performed in MATLAB analyzing spike-sorted and burst-sorted data on the prefrontal cortex from the Buzsaki lab (Fujisawa et al., 2008, 2015) which is consistent with a regular clock cycle of about 153.4 ms and with regular alternation between a forward pass of network calculations and a backwards pass, as in the general form of the backpropagation algorithm which one of us first developed in the period 1968-1974 (Werbos, 1994, 2006; Anderson and Rosenfeld, 1998). In business and finance, it is well known that adjustments for cycles of the year are essential to accurate prediction of time-series data (Box and Jenkins, 1970); in a similar way, methods for identifying and using regular clock cycles offer large new opportunities in neural time-series analysis. This paper demonstrates a few initial footprints on the large "continent" of this type of neural time-series analysis, and discusses a few of the many further possibilities opened up by this new approach to "decoding" the neural code (Heller et al., 1995).

Recent progress in multi-electrode spike sorting methods

In recent years, arrays of extracellular electrodes have been developed and manufactured to record simultaneously from hundreds of electrodes packed with a high density. These recordings should allow neuroscientists to reconstruct the individual activity of the neurons spiking in the vicinity of these electrodes, with the help of signal processing algorithms. Algorithms need to solve a source separation problem, also known as spike sorting. However, these new devices challenge the classical way to do spike sorting. Here we review different methods that have been developed to sort spikes from these large-scale recordings. We describe the common properties of these algorithms, as well as their main differences. Finally, we outline the issues that remain to be solved by future spike sorting algorithms.

Layer 4 fast-spiking interneurons filter thalamocortical signals during active somatosensation

We rely on movement to explore the environment, for example, by palpating an object. In somatosensory cortex, activity related to movement of digits or whiskers is suppressed, which could facilitate detection of touch. Movement-related suppression is generally assumed to involve corollary discharges. Here we uncovered a thalamocortical mechanism in which cortical fast-spiking interneurons, driven by sensory input, suppress movement-related activity in layer 4 (L4) excitatory neurons. In mice locating objects with their whiskers, neurons in the ventral posteromedial nucleus (VPM) fired in response to touch and whisker movement. Cortical L4 fast-spiking interneurons inherited these responses from VPM. In contrast, L4 excitatory neurons responded mainly to touch. Optogenetic experiments revealed that fast-spiking interneurons reduced movement-related spiking in excitatory neurons, enhancing selectivity for touch-related information during active tactile sensation. These observations suggest a fundamental computation performed by the thalamocortical circuit to accentuate salient tactile information.

Contribution of synchronized GABAergic neurons to dopaminergic neuron firing and bursting

In the ventral tegmental area (VTA), interactions between dopamine (DA) and γ-aminobutyric acid (GABA) neurons are critical for regulating DA neuron activity and thus DA efflux. To provide a mechanistic explanation of how GABA neurons influence DA neuron firing, we developed a circuit model of the VTA. The model is based on feed-forward inhibition and recreates canonical features of the VTA neurons. Simulations revealed that γ-aminobutyric acid (GABA) receptor (GABAR) stimulation can differentially influence the firing pattern of the DA neuron, depending on the level of synchronization among GABA neurons. Asynchronous activity of GABA neurons provides a constant level of inhibition to the DA neuron and, when removed, produces a classical disinhibition burst. In contrast, when GABA neurons are synchronized by common synaptic input, their influence evokes additional spikes in the DA neuron, resulting in increased measures of firing and bursting. Distinct from previous mechanisms, the increases were not based on lowered firing rate of the GABA neurons or weaker hyperpolarization by the GABAR synaptic current. This phenomenon was induced by GABA-mediated hyperpolarization of the DA neuron that leads to decreases in intracellular calcium (Ca2+) concentration, thus reducing the Ca2+-dependent potassium (K+) current. In this way, the GABA-mediated hyperpolarization replaces Ca2+-dependent K+ current; however, this inhibition is pulsatile, which allows the DA neuron to fire during the rhythmic pauses in inhibition. Our results emphasize the importance of inhibition in the VTA, which has been discussed in many studies, and suggest a novel mechanism whereby computations can occur locally.

Artefactual origin of biphasic cortical spike-LFP correlation

Electrophysiological data acquisition systems introduce various distortions into the signals they record. While such distortions were discussed previously, their effects are often not appreciated. Here I show that the biphasic shape of cortical spike-triggered LFP average (stLFP), reported in multiple studies, is likely an artefact introduced by high-pass filter of the neural data acquisition system when the actual stLFP has a single trough around the zero lag.

Improving data quality in neuronal population recordings

Understanding how the brain operates requires understanding how large sets of neurons function together. Modern recording technology makes it possible to simultaneously record the activity of hundreds of neurons, and technological developments will soon allow recording of thousands or tens of thousands. As with all experimental techniques, these methods are subject to confounds that complicate the interpretation of such recordings, and could lead to erroneous scientific conclusions. Here we discuss methods for assessing and improving the quality of data from these techniques and outline likely future directions in this field.

Recording extracellular neural activity in the behaving monkey using a semichronic and high-density electrode system

We describe a technique to semichronically record the cortical extracellular neural activity in the behaving monkey employing commercial high-density electrodes. After the design and construction of low cost microdrives that allow varying the depth of the recording locations after the implantation surgery, we recorded the extracellular unit activity from pools of neurons at different depths in the presupplementary motor cortex (pre-SMA) of a rhesus monkey trained in a tapping task. The collected data were processed to classify cells as putative pyramidal cells or interneurons on the basis of their waveform features. We also demonstrate that short time cross-correlogram occasionally yields unit pairs with high short latency (<5 ms), narrow bin (<3 ms) peaks, indicative of monosynaptic spike transmission from pre- to postsynaptic neurons. These methods have been verified extensively in rodents. Finally, we observed that the pattern of population activity was repetitive over distinct trials of the tapping task. These results show that the semichronic technique is a viable option for the large-scale parallel recording of local circuit activity at different depths in the cortex of the macaque monkey and other large species.

Validating silicon polytrodes with paired juxtacellular recordings: method and dataset

Cross-validating new methods for recording neural activity is necessary to accurately interpret and compare the signals they measure. Here we describe a procedure for precisely aligning two probes for in vivo "paired-recordings" such that the spiking activity of a single neuron is monitored with both a dense extracellular silicon polytrode and a juxtacellular micropipette. Our new method allows for efficient, reliable, and automated guidance of both probes to the same neural structure with micrometer resolution. We also describe a new dataset of paired-recordings, which is available online. We propose that our novel targeting system, and ever expanding cross-validation dataset, will be vital to the development of new algorithms for automatically detecting/sorting single-units, characterizing new electrode materials/designs, and resolving nagging questions regarding the origin and nature of extracellular neural signals.

Processing and Analysis of Multichannel Extracellular Neuronal Signals: State-of-the-Art and Challenges

In recent years multichannel neuronal signal acquisition systems have allowed scientists to focus on research questions which were otherwise impossible. They act as a powerful means to study brain (dys)functions in in-vivo and in in-vitro animal models. Typically, each session of electrophysiological experiments with multichannel data acquisition systems generate large amount of raw data. For example, a 128 channel signal acquisition system with 16 bits A/D conversion and 20 kHz sampling rate will generate approximately 17 GB data per hour (uncompressed). This poses an important and challenging problem of inferring conclusions from the large amounts of acquired data. Thus, automated signal processing and analysis tools are becoming a key component in neuroscience research, facilitating extraction of relevant information from neuronal recordings in a reasonable time. The purpose of this review is to introduce the reader to the current state-of-the-art of open-source packages for (semi)automated processing and analysis of multichannel extracellular neuronal signals (i.e., neuronal spikes, local field potentials, electroencephalogram, etc.), and the existing Neuroinformatics infrastructure for tool and data sharing. The review is concluded by pinpointing some major challenges that are being faced, which include the development of novel benchmarking techniques, cloud-based distributed processing and analysis tools, as well as defining novel means to share and standardize data.

Network Homeostasis and State Dynamics of Neocortical Sleep

Sleep exerts many effects on mammalian forebrain networks, including homeostatic effects on both synaptic strengths and firing rates. We used large-scale recordings to examine the activity of neurons in the frontal cortex of rats and first observed that the distribution of pyramidal cell firing rates was wide and strongly skewed toward high firing rates. Moreover, neurons from different parts of that distribution were differentially modulated by sleep substates. Periods of nonREM sleep reduced the activity of high firing rate neurons and tended to upregulate firing of slow-firing neurons. By contrast, the effect of REM was to reduce firing rates across the entire rate spectrum. Microarousals, interspersed within nonREM epochs, increased firing rates of slow-firing neurons. The net result of sleep was to homogenize the firing rate distribution. These findings are at variance with current homeostatic models and provide a novel view of sleep in adjusting network excitability.

Long Term Recordings with Immobile Silicon Probes in the Mouse Cortex

A key experimental approach in neuroscience involves measuring neuronal activity in behaving animals with extracellular chronic recordings. Such chronic recordings were initially made with single electrodes and tetrodes, and are now increasingly performed with high-density, high-count silicon probes. A common way to achieve long-term chronic recording is to attach the probes to microdrives that progressively advance them into the brain. Here we report, however, that such microdrives are not strictly necessary. Indeed, we obtained high-quality recordings in both head-fixed and freely moving mice for several months following the implantation of immobile chronic probes. Probes implanted into the primary visual cortex yielded well-isolated single units whose spike waveform and orientation tuning were highly reproducible over time. Although electrode drift was not completely absent, stable waveforms occurred in at least 70% of the neurons tested across consecutive days. Thus, immobile silicon probes represent a straightforward and reliable technique to obtain stable, long-term population recordings in mice, and to follow the activity of populations of well-isolated neurons over multiple days.

Spike Detection for Large Neural Populations Using High Density Multielectrode Arrays

An emerging generation of high-density microelectrode arrays (MEAs) is now capable of recording spiking activity simultaneously from thousands of neurons with closely spaced electrodes. Reliable spike detection and analysis in such recordings is challenging due to the large amount of raw data and the dense sampling of spikes with closely spaced electrodes. Here, we present a highly efficient, online capable spike detection algorithm, and an offline method with improved detection rates, which enables estimation of spatial event locations at a resolution higher than that provided by the array by combining information from multiple electrodes. Data acquired with a 4096 channel MEA from neuronal cultures and the neonatal retina, as well as synthetic data, was used to test and validate these methods. We demonstrate that these algorithms outperform conventional methods due to a better noise estimate and an improved signal-to-noise ratio (SNR) through combining information from multiple electrodes. Finally, we present a new approach for analyzing population activity based on the characterization of the spatio-temporal event profile, which does not require the isolation of single units. Overall, we show how the improved spatial resolution provided by high density, large scale MEAs can be reliably exploited to characterize activity from large neural populations and brain circuits.

A Simple Method to Simultaneously Detect and Identify Spikes from Raw Extracellular Recordings

The ability to track when and which neurons fire in the vicinity of an electrode, in an efficient and reliable manner can revolutionize the neuroscience field. The current bottleneck lies in spike sorting algorithms; existing methods for detecting and discriminating the activity of multiple neurons rely on inefficient, multi-step processing of extracellular recordings. In this work, we show that a single-step processing of raw (unfiltered) extracellular signals is sufficient for both the detection and identification of active neurons, thus greatly simplifying and optimizing the spike sorting approach. The efficiency and reliability of our method is demonstrated in both real and simulated data.

Ramping single unit activity in the medial prefrontal cortex and ventral striatum reflects the onset of waiting but not imminent impulsive actions

The medial prefrontal cortex (mPFC) and ventral striatum (VS), including the nucleus accumbens, are key forebrain regions involved in regulating behaviour for future rewards. Dysfunction of these regions can result in impulsivity, characterized by actions that are mistimed and executed without due consideration of their consequences. Here we recorded the activity of single neurons in the mPFC and VS of 16 rats during performance on a five-choice serial reaction time task of sustained visual attention and impulsivity. Impulsive responses were assessed by the number of premature responses made before target stimuli were presented. We found that the majority of cells signalled trial outcome after an action was made (both rewarded and unrewarded). Positive and negative ramping activity was a feature of population activity in the mPFC and VS (49.5 and 50.4% of cells, respectively). This delay-related activity increased at the same rate and reached the same maximum (or minimum) for trials terminated by either correct or premature responses. However, on premature trials, the ramping activity started earlier and coincided with shorter latencies to begin waiting. For all trial types the pattern of ramping activity was unchanged when the pre-stimulus delay period was made variable. Thus, premature responses may result from a failure in the timing of the initiation of a waiting process, combined with a reduced reliance on external sensory cues, rather than a primary failure in delay activity. Our findings further show that the neural locus of this aberrant timing signal may emanate from structures outside the mPFC and VS.