A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo

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.

Do not waste your electrodes-principles of optimal electrode geometry for spike sorting

Objective. This study examines how the geometrical arrangement of electrodes influences spike sorting efficiency, and attempts to formalise principles for the design of electrode systems enabling optimal spike sorting performance.Approach. The clustering performance of KlustaKwik, a popular toolbox, was evaluated using semi-artificial multi-channel data, generated from a library of real spike waveforms recorded in the CA1 region of mouse Hippocampusin vivo.Main results. Based on spike sorting results under various channel configurations and signal levels, a simple model was established to describe the efficiency of different electrode geometries. Model parameters can be inferred from existing spike waveform recordings, which allowed quantifying both the cooperative effect between channels and the noise dependence of clustering performance.Significance. Based on the model, analytical and numerical results can be derived for the optimal spacing and arrangement of electrodes for one- and two-dimensional electrode systems, targeting specific brain areas.

Extracellular detection of neuronal coupling

We developed a method to non-invasively detect synaptic relationships among neurons from in vitro networks. Our method uses microelectrode arrays on which neurons are cultured and from which propagation of extracellular action potentials (eAPs) in single axons are recorded at multiple electrodes. Detecting eAP propagation bypasses ambiguity introduced by spike sorting. Our methods identify short latency spiking relationships between neurons with properties expected of synaptically coupled neurons, namely they were recapitulated by direct stimulation and were sensitive to changing the number of active synaptic sites. Our methods enabled us to assemble a functional subset of neuronal connectivity in our cultures.

HTsort: Enabling Fast and Accurate Spike Sorting on Multi-Electrode Arrays

Spike sorting is used to classify the spikes (action potentials acquired by physiological electrodes), aiming to identify their respective firing units. Now it has been developed to classify the spikes recorded by multi-electrode arrays (MEAs), with the improvement of micro-electrode technology. However, how to improve classification accuracy and maintain low time complexity simultaneously becomes a difficulty. A fast and accurate spike sorting approach named HTsort is proposed for high-density multi-electrode arrays in this paper. Several improvements have been introduced to the traditional pipeline that is composed of threshold detection and clustering method. First, the divide-and-conquer method is employed to utilize electrode spatial information to achieve pre-clustering. Second, the clustering method HDBSCAN (hierarchical density-based spatial clustering of applications with noise) is used to classify spikes and detect overlapping events (multiple spikes firing simultaneously). Third, the template merging method is used to merge redundant exported templates according to the template similarity and the spatial distribution of electrodes. Finally, the template matching method is used to resolve overlapping events. Our approach is validated on simulation data constructed by ourselves and publicly available data and compared to other state-of-the-art spike sorters. We found that the proposed HTsort has a more favorable trade-off between accuracy and time consumption. Compared with MountainSort and SpykingCircus, the time consumption is reduced by at least 40% when the number of electrodes is 64 and below. Compared with HerdingSpikes, the classification accuracy can typically improve by more than 10%. Meanwhile, HTsort exhibits stronger robustness against background noise than other sorters. Our more sophisticated spike sorter would facilitate neurophysiologists to complete spike sorting more quickly and accurately.

Resolution of Spike Overlapping by Biogeography-Based Optimization

There are many cases in which the separation of different sources from single channel recordings is important, for example, in fluorescence spectral overlap compensation, electrical impedance signaling, intramuscular electromyogram decomposition or in the case of spike sorting of neuron potentials from microelectrode arrays (MEA). Focusing on the latter, the problem can be faced by identifying spikes emerging from the background and clustering into different groups, indicating the activity of different neurons. Problems are found when more spikes are superimposed in overlapped waveforms. We discuss the application of Biogeography-Based Optimization (BBO) to resolve this specific problem. Our algorithm is compared with three spike-sorting methods (SpyKING Circus, Common Basis Pursuit and Klusta), showing statistically better performance (in terms of F1 score, True Positive Rate—TPR and Positive Predictive Value—PPV) in resolving overlaps in realistic, simulated data. Specifically, BBO showed median F1, TPR and PPV of 100%, 100% and about 75%, respectively, considering a simulated noise with the same spectral density as the experimental one and a similar power with highly statistically significant improvements of at least two performance indexes over each of the other three tested algorithms.

Functional ultrasound imaging of the spreading activity following optogenetic stimulation of the rat visual cortex

Optogenetics has revolutionized neurosciences by allowing fine control of neuronal activity. An important aspect for this control is assessing the activation and/or adjusting the stimulation, which requires imaging the entire volume of optogenetically-induced neuronal activity. An ideal technique for this aim is fUS imaging, which allows one to generate brain-wide activation maps with submesoscopic spatial resolution. However, optical stimulation of the brain with blue light might lead to non-specific activations at high irradiances. fUS imaging of optogenetic activations can be obtained at these wavelengths using lower light power (< 2mW) but it limits the depth of directly activatable neurons from the cortical surface. Our main goal was to report that we can detect specific optogenetic activations in V1 even in deep layers following stimulation at the cortical surface. Here, we show the possibility to detect deep optogenetic activations in anesthetized rats expressing the red-shifted opsin ChrimsonR in V1 using fUS imaging. We demonstrate the optogenetic specificity of these activations and their neuronal origin with electrophysiological recordings. Finally, we show that the optogenetic response initiated in V1 spreads to downstream (LGN) and upstream (V2) visual areas.

Duplicate Detection of Spike Events: A Relevant Problem in Human Single-Unit Recordings

Single-unit recordings in the brain of behaving human subjects provide a unique opportunity to advance our understanding of neural mechanisms of cognition. These recordings are exclusively performed in medical centers during diagnostic or therapeutic procedures. The presence of medical instruments along with other aspects of the hospital environment limit the control of electrical noise compared to animal laboratory environments. Here, we highlight the problem of an increased occurrence of simultaneous spike events on different recording channels in human single-unit recordings. Most of these simultaneous events were detected in clusters previously labeled as artifacts and showed similar waveforms. These events may result from common external noise sources or from different micro-electrodes recording activity from the same neuron. To address the problem of duplicate recorded events, we introduce an open-source algorithm to identify these artificial spike events based on their synchronicity and waveform similarity. Applying our method to a comprehensive dataset of human single-unit recordings, we demonstrate that our algorithm can substantially increase the data quality of these recordings. Given our findings, we argue that future studies of single-unit activity recorded under noisy conditions should employ algorithms of this kind to improve data quality.

Improving scalability in systems neuroscience

Emerging technologies to acquire data at increasingly greater scales promise to transform discovery in systems neuroscience. However, current exponential growth in the scale of data acquisition is a double-edged sword. Scaling up data acquisition can speed up the cycle of discovery but can also misinterpret the results or possibly slow down the cycle because of challenges presented by the curse of high-dimensional data. Active, adaptive, closed-loop experimental paradigms use hardware and algorithms optimized to enable time-critical computation to provide feedback that interprets the observations and tests hypotheses to actively update the stimulus or stimulation parameters. In this perspective, we review important concepts of active and adaptive experiments and discuss how selectively constraining the dimensionality and optimizing strategies at different stages of discovery loop can help mitigate the curse of high-dimensional data. Active and adaptive closed-loop experimental paradigms can speed up discovery despite an exponentially increasing data scale, offering a road map to timely and iterative hypothesis revision and discovery in an era of exponential growth in neuroscience.

Optogenetic fUSI for brain-wide mapping of neural activity mediating collicular-dependent behaviors

Neuronal cell types are arranged in brain-wide circuits that guide behavior. In mice, the superior colliculus innervates a set of targets that direct orienting and defensive actions. We combined functional ultrasound imaging (fUSI) with optogenetics to reveal the network of brain regions functionally activated by four collicular cell types. Stimulating each neuronal group triggered different behaviors and activated distinct sets of brain nuclei. This included regions not previously thought to mediate defensive behaviors, for example, the posterior paralaminar nuclei of the thalamus (PPnT), which we show to play a role in suppressing habituation. Neuronal recordings with Neuropixels probes show that (1) patterns of spiking activity and fUSI signals correlate well in space and (2) neurons in downstream nuclei preferentially respond to innately threatening visual stimuli. This work provides insight into the functional organization of the networks governing innate behaviors and demonstrates an experimental approach to explore the whole-brain neuronal activity downstream of targeted cell types.

ELVISort: encoding latent variables for instant sorting, an artificial intelligence-based end-to-end solution

Objective.The growing number of recording sites of silicon-based probes means that an increasing amount of neural cell activities can be recorded simultaneously, facilitating the investigation of underlying complex neural dynamics. In order to overcome the challenges generated by the increasing number of channels, highly automated signal processing tools are needed. Our goal was to build a spike sorting model that can perform as well as offline solutions while maintaining high efficiency, enabling high-performance online sorting.Approach.In this paper we present ELVISort, a deep learning method that combines the detection and clustering of different action potentials in an end-to-end fashion.Main results.The performance of ELVISort is comparable with other spike sorting methods that use manual or semi-manual techniques, while exceeding the methods which use an automatic approach: ELVISort has been tested on three independent datasets and yielded average F₁scores of 0.96, 0.82 and 0.81, which comparable with the results of state-of-the-art algorithms on the same data. We show that despite the good performance, ELVISort is capable to process data in real-time: the time it needs to execute the necessary computations for a sample of given length is only 1/15.71 of its actual duration (i.e. the sampling time multiplied by the number of the sampling points).Significance.ELVISort, because of its end-to-end nature, can exploit the massively parallel processing capabilities of GPUs via deep learning frameworks by processing multiple batches in parallel, with the potential to be used on other cutting-edge AI-specific hardware such as TPUs, enabling the development of integrated, portable and real-time spike sorting systems with similar performance to offline sorters.

Inhibitory neurons exhibit high controlling ability in the cortical microconnectome

The brain is a network system in which excitatory and inhibitory neurons keep activity balanced in the highly non-random connectivity pattern of the microconnectome. It is well known that the relative percentage of inhibitory neurons is much smaller than excitatory neurons in the cortex. So, in general, how inhibitory neurons can keep the balance with the surrounding excitatory neurons is an important question. There is much accumulated knowledge about this fundamental question. This study quantitatively evaluated the relatively higher functional contribution of inhibitory neurons in terms of not only properties of individual neurons, such as firing rate, but also in terms of topological mechanisms and controlling ability on other excitatory neurons. We combined simultaneous electrical recording (~2.5 hours) of ~1000 neurons in vitro, and quantitative evaluation of neuronal interactions including excitatory-inhibitory categorization. This study accurately defined recording brain anatomical targets, such as brain regions and cortical layers, by inter-referring MRI and immunostaining recordings. The interaction networks enabled us to quantify topological influence of individual neurons, in terms of controlling ability to other neurons. Especially, the result indicated that highly influential inhibitory neurons show higher controlling ability of other neurons than excitatory neurons, and are relatively often distributed in deeper layers of the cortex. Furthermore, the neurons having high controlling ability are more effectively limited in number than central nodes of k-cores, and these neurons also participate in more clustered motifs. In summary, this study suggested that the high controlling ability of inhibitory neurons is a key mechanism to keep balance with a large number of other excitatory neurons beyond simple higher firing rate. Application of the selection method of limited important neurons would be also applicable for the ability to effectively and selectively stimulate E/I imbalanced disease states.

How small numbers of inhibitory neurons functionally keep balance with large numbers of excitatory neurons in the brain by controlling each other is a fundamental question. Especially, this study quantitatively evaluated a topological mechanism of interaction networks in terms of controlling abilities of individual cortical neurons to other neurons. Combination of simultaneous electrical recording of ~1000 neurons and a quantitative evaluation method of neuronal interactions including excitatory-inhibitory categories, enabled us to evaluate the influence of individual neurons not only about firing rate but also about their relative positions in the networks and controllable ability of other neurons. Especially, the result showed that inhibitory neurons have more controlling ability than excitatory neurons, and such neurons were more often observed in deep layers. Because the limited number of neurons in terms controlling ability were much smaller than neurons based on centrality measure and, of course, more directly selected neurons based on their ability to control other neurons, the selection method of important neurons will help not only to produce realistic computational models but also will help to stimulate brain to effectively treat imbalanced disease states.

SHYBRID: A Graphical Tool for Generating Hybrid Ground-Truth Spiking Data for Evaluating Spike Sorting Performance

Spike sorting is the process of retrieving the spike times of individual neurons that are present in an extracellular neural recording. Over the last decades, many spike sorting algorithms have been published. In an effort to guide a user towards a specific spike sorting algorithm, given a specific recording setting (i.e., brain region and recording device), we provide an open-source graphical tool for the generation of hybrid ground-truth data in Python. Hybrid ground-truth data is a data-driven modelling paradigm in which spikes from a single unit are moved to a different location on the recording probe, thereby generating a virtual unit of which the spike times are known. The tool enables a user to efficiently generate hybrid ground-truth datasets and make informed decisions between spike sorting algorithms, fine-tune the algorithm parameters towards the used recording setting, or get a deeper understanding of those algorithms.

MEArec: A Fast and Customizable Testbench Simulator for Ground-truth Extracellular Spiking Activity

When recording neural activity from extracellular electrodes, both in vivo and in vitro, spike sorting is a required and very important processing step that allows for identification of single neurons’ activity. Spike sorting is a complex algorithmic procedure, and in recent years many groups have attempted to tackle this problem, resulting in numerous methods and software packages. However, validation of spike sorting techniques is complicated. It is an inherently unsupervised problem and it is hard to find universal metrics to evaluate performance. Simultaneous recordings that combine extracellular and patch-clamp or juxtacellular techniques can provide ground-truth data to evaluate spike sorting methods. However, their utility is limited by the fact that only a few cells can be measured at the same time. Simulated ground-truth recordings can provide a powerful alternative mean to rank the performance of spike sorters. We present here MEArec, a Python-based software which permits flexible and fast simulation of extracellular recordings. MEArec allows users to generate extracellular signals on various customizable electrode designs and can replicate various problematic aspects for spike sorting, such as bursting, spatio-temporal overlapping events, and drifts. We expect MEArec will provide a common testbench for spike sorting development and evaluation, in which spike sorting developers can rapidly generate and evaluate the performance of their algorithms.

Retinal Ganglion Cells Functional Changes in a Mouse Model of Alzheimer's Disease Are Linked with Neurotransmitter Alterations

BACKGROUND

Alzheimer's disease (AD) is the most prevalent form of dementia worldwide. This neurodegenerative syndrome affects cognition, memory, behavior, and the visual system, particularly the retina.

OBJECTIVE

This work aims to determine whether the 5xFAD mouse, a transgenic model of AD, displays changes in the function of retinal ganglion cells (RGCs) and if those alterations are correlated with changes in the expression of glutamate and gamma-aminobutyric acid (GABA) neurotransmitters.

METHODS

In young (2-3-month-old) and adult (6-7-month-old) 5xFAD and WT mice, we have studied the physiological response, firing rate, and burst of RGCs to various types of visual stimuli using a multielectrode array system.

RESULTS

The firing rate and burst response in 5xFAD RGCs showed hyperactivity at the early stage of AD in young mice, whereas hypoactivity was seen at the later stage of AD in adults. The physiological alterations observed in 5xFAD correlate well with an increase in the expression of glutamate in the ganglion cell layer in young and adults. GABA staining increased in the inner nuclear and plexiform layer, which was more pronounced in the adult than the young 5xFAD retina, altering the excitation/inhibition balance, which could explain the observed early hyperactivity and later hypoactivity in RGC physiology.

CONCLUSION

These findings indicate functional changes may be caused by neurochemical alterations of the retina starting at an early stage of the AD disease.

A facile and comprehensive algorithm for electrical response identification in mouse retinal ganglion cells

Retinal prostheses can restore the basic visual function of patients with retinal degeneration, which relies on effective electrical stimulation to evoke the physiological activities of retinal ganglion cells (RGCs). Current electrical stimulation strategies have defects such as unstable effects and insufficient stimulation positions, therefore, it is crucial to determine the optimal pulse parameters for precise and safe electrical stimulation. Biphasic voltages (cathode-first) with a pulse width of 25 ms and different amplitudes were used to ex vivo stimulate RGCs of three wild-type (WT) mice using a commercial microelectrode array (MEA) recording system. An algorithm is developed to automatically realize both spike-sorting and electrical response identification for the spike signals recorded. Measured from three WT mouse retinas, the total numbers of RGC units and responsive RGC units were 1193 and 151, respectively. In addition, the optimal pulse amplitude range for electrical stimulation was determined to be 0.43 V-1.3 V. The processing results of the automatic algorithm we proposed shows high consistency with those using traditional manual processing. We anticipate the new algorithm can not only speed up the elaborate electrophysiological data processing, but also optimize pulse parameters for the electrical stimulation strategy of neural prostheses.

Behavior needs neural variability

Human and non-human animal behavior is highly malleable and adapts successfully to internal and external demands. Such behavioral success stands in striking contrast to the apparent instability in neural activity (i.e., variability) from which it arises. Here, we summon the considerable evidence across scales, species, and imaging modalities that neural variability represents a key, undervalued dimension for understanding brain-behavior relationships at inter- and intra-individual levels. We believe that only by incorporating a specific focus on variability will the neural foundation of behavior be comprehensively understood.

Electrophysiological Analysis of Brain Organoids: Current Approaches and Advancements

Brain organoids, or cerebral organoids, have become widely used to study the human brain in vitro. As pluripotent stem cell-derived structures capable of self-organization and recapitulation of physiological cell types and architecture, brain organoids bridge the gap between relatively simple two-dimensional human cell cultures and non-human animal models. This allows for high complexity and physiological relevance in a controlled in vitro setting, opening the door for a variety of applications including development and disease modeling and high-throughput screening. While technologies such as single cell sequencing have led to significant advances in brain organoid characterization and understanding, improved functional analysis (especially electrophysiology) is needed to realize the full potential of brain organoids. In this review, we highlight key technologies for brain organoid development and characterization, then discuss current electrophysiological methods for brain organoid analysis. While electrophysiological approaches have improved rapidly for two-dimensional cultures, only in the past several years have advances been made to overcome limitations posed by the three-dimensionality of brain organoids. Here, we review major advances in electrophysiological technologies and analytical methods with a focus on advances with applicability for brain organoid analysis.

Network Dynamics in the Developing Piriform Cortex of Unanesthetized Rats

The time course of changes in functional cortical activity during early development has been extensively studied in the rodent visual system. A key period in this process is the time of eye opening, which marks the onset of patterned visual input and active vision. However, vision differs from other systems in that it receives limited patterned sensory input before eye opening, and it remains unclear how findings from vision relate to other systems. Here, we focus on the development of cortical network activity in the olfactory system-which is crucial for survival at birth-by recording field potential and spiking activity from piriform cortex of unanesthetized rat pups from birth (P0) to P21. Our results demonstrate that odors evoke stable 10-15 Hz oscillations in piriform cortex from birth to P15, after which cortical responses undergo rapid changes. This transition is coincident with the emergence of gamma oscillations and fast sniffing behavior and preceded by an increase in spontaneous activity. Neonatal network oscillations and their developmental dynamics exhibit striking similarities with those previously observed in the visual, auditory, and somatosensory systems, providing insight into the network-level mechanisms underlying the development of sensory cortex in general and olfactory processing in particular.

Predicting synchronous firing of large neural populations from sequential recordings

A major goal in neuroscience is to understand how populations of neurons code for stimuli or actions. While the number of neurons that can be recorded simultaneously is increasing at a fast pace, in most cases these recordings cannot access a complete population: some neurons that carry relevant information remain unrecorded. In particular, it is hard to simultaneously record all the neurons of the same type in a given area. Recent progress have made possible to profile each recorded neuron in a given area thanks to genetic and physiological tools, and to pool together recordings from neurons of the same type across different experimental sessions. However, it is unclear how to infer the activity of a full population of neurons of the same type from these sequential recordings. Neural networks exhibit collective behaviour, e.g. noise correlations and synchronous activity, that are not directly captured by a conditionally-independent model that would just put together the spike trains from sequential recordings. Here we show that we can infer the activity of a full population of retina ganglion cells from sequential recordings, using a novel method based on copula distributions and maximum entropy modeling. From just the spiking response of each ganglion cell to a repeated stimulus, and a few pairwise recordings, we could predict the noise correlations using copulas, and then the full activity of a large population of ganglion cells of the same type using maximum entropy modeling. Remarkably, we could generalize to predict the population responses to different stimuli with similar light conditions and even to different experiments. We could therefore use our method to construct a very large population merging cells' responses from different experiments. We predicted that synchronous activity in ganglion cell populations saturates only for patches larger than 1.5mm in radius, beyond what is today experimentally accessible.

Enhanced representation of natural sound sequences in the ventral auditory midbrain

The auditory midbrain (inferior colliculus, IC) plays an important role in sound processing, acting as hub for acoustic information extraction and for the implementation of fast audio-motor behaviors. IC neurons are topographically organized according to their sound frequency preference: dorsal IC regions encode low frequencies while ventral areas respond best to high frequencies, a type of sensory map defined as tonotopy. Tonotopic maps have been studied extensively using artificial stimuli (pure tones) but our knowledge of how these maps represent information about sequences of natural, spectro-temporally rich sounds is sparse. We studied this question by conducting simultaneous extracellular recordings across IC depths in awake bats (Carollia perspicillata) that listened to sequences of natural communication and echolocation sounds. The hypothesis was that information about these two types of sound streams is represented at different IC depths since they exhibit large differences in spectral composition, i.e., echolocation covers the high-frequency portion of the bat soundscape (> 45 kHz), while communication sounds are broadband and carry most power at low frequencies (20–25 kHz). Our results showed that mutual information between neuronal responses and acoustic stimuli, as well as response redundancy in pairs of neurons recorded simultaneously, increase exponentially with IC depth. The latter occurs regardless of the sound type presented to the bats (echolocation or communication). Taken together, our results indicate the existence of mutual information and redundancy maps at the midbrain level whose response cannot be predicted based on the frequency composition of natural sounds and classic neuronal tuning curves.

Differential Effect of Anesthesia on Visual Cortex Neurons with Diverse Population Coupling

Cortical neurons display diverse firing patterns and synchronization properties. How anesthesia alters the firing response of different neuron groups relevant for sensory information processing is unclear. Here we investigated the graded effect of anesthesia on spontaneous and visual flash-induced spike activity of different neuron groups classified based on their spike waveform, firing rate, and population coupling (the extent neurons conform to population spikes). Single-unit activity was measured from multichannel extracellular recordings in deep layers of primary visual cortex of freely moving rats in wakefulness and at three concentrations of desflurane. Anesthesia generally decreased firing rate and increased population coupling and burstiness of neurons. Population coupling and firing rate became more correlated and the pairwise correlation between neurons became more predictable by their population coupling in anesthesia. During wakefulness, visual stimulation increased firing rate; this effect was the largest and the most prolonged in neurons that exhibited high population coupling and high firing rate. During anesthesia, the early increase in firing rate (20-150 ms post-stimulus) of these neurons was suppressed, their spike timing was delayed and split into two peaks. The late response (200-400 ms post-stimulus) of all neurons was also suppressed. We conclude that anesthesia alters the visual response of primarily high-firing highly coupled neurons, which may interfere with visual sensory processing. The increased association of population coupling and firing rate during anesthesia suggests a decrease in sensory information content.

Thermodynamic Formalism in Neuronal Dynamics and Spike Train Statistics

The Thermodynamic Formalism provides a rigorous mathematical framework for studying quantitative and qualitative aspects of dynamical systems. At its core, there is a variational principle that corresponds, in its simplest form, to the Maximum Entropy principle. It is used as a statistical inference procedure to represent, by specific probability measures (Gibbs measures), the collective behaviour of complex systems. This framework has found applications in different domains of science. In particular, it has been fruitful and influential in neurosciences. In this article, we review how the Thermodynamic Formalism can be exploited in the field of theoretical neuroscience, as a conceptual and operational tool, in order to link the dynamics of interacting neurons and the statistics of action potentials from either experimental data or mathematical models. We comment on perspectives and open problems in theoretical neuroscience that could be addressed within this formalism.

SpikeInterface, a unified framework for spike sorting

Much development has been directed toward improving the performance and automation of spike sorting. This continuous development, while essential, has contributed to an over-saturation of new, incompatible tools that hinders rigorous benchmarking and complicates reproducible analysis. To address these limitations, we developed SpikeInterface, a Python framework designed to unify preexisting spike sorting technologies into a single codebase and to facilitate straightforward comparison and adoption of different approaches. With a few lines of code, researchers can reproducibly run, compare, and benchmark most modern spike sorting algorithms; pre-process, post-process, and visualize extracellular datasets; validate, curate, and export sorting outputs; and more. In this paper, we provide an overview of SpikeInterface and, with applications to real and simulated datasets, demonstrate how it can be utilized to reduce the burden of manual curation and to more comprehensively benchmark automated spike sorters.

Ethanol Alters Variability, But Not Rate, of Firing in Medial Prefrontal Cortex Neurons of Awake-Behaving Rats

BACKGROUND

The medial prefrontal cortex (mPFC) is a brain region involved in the evaluation and selection of motivationally relevant outcomes. Neural activity in mPFC is altered following acute ethanol (EtOH) use and, in rodent models, doses as low as 0.75 g/kg yield cognitive deficits. Deficits in decision making following acute EtOH are thought to be mediated, at least in part, by decreases in mPFC firing rates (FRs). However, the data leading to this conclusion have been generated exclusively in anesthetized rodents. The present study characterizes the effects of acute EtOH injections on mPFC neural activity in awake-behaving rodents.

METHODS

Awake-behaving and anesthetized in vivo electrophysiological recordings were performed. We utilized 3 groups: the first received 2 saline injections, the second received a saline injection followed by 1.0 g/kg EtOH, and the last received saline followed by 2 g/kg EtOH. One week later, an anesthetized recording occurred where a saline injection was followed by an injection of 1.0 g/kg EtOH.

RESULTS

The anesthetized condition showed robust decreases in neural activity and differences in up-down states (UDS) dynamics. In the awake-behaving condition, FRs were grouped according to behavioral state: moving, not-moving, and sleep. The differences in median FRs were found for each treatment and behavioral state combination. A FR decrease was only found in the 2.0 g/kg EtOH treatment during not-moving states. However, robust decreases in FR variability were found across behavioral state in both the 1.0 and 2.0 g/kg EtOH treatment. Sleep was separately analyzed. EtOH modulated the UDS during sleep producing decreases in FRs.

CONCLUSIONS

In conclusion, the changes in neural activity following EtOH administration in anesthetized animals are not conserved in awake-behaving animals. The most prominent difference following EtOH was a decrease in FR variability suggesting that acute EtOH may be affecting decision making via this mechanism.

State-Dependent Cortical Unit Activity Reflects Dynamic Brain State Transitions in Anesthesia

Understanding the effects of anesthesia on cortical neuronal spiking and information transfer could help illuminate the neuronal basis of the conscious state. Recent investigations suggest that the brain state identified by local field potential spectrum is not stationary but changes spontaneously at a fixed level of anesthetic concentration. How cortical unit activity changes with dynamically transitioning brain states under anesthesia is unclear. Extracellular unit activity was measured with 64-channel silicon microelectrode arrays in cortical layers 5/6 of the primary visual cortex of chronically instrumented, freely moving male rats (n = 7) during stepwise reduction of the anesthetic desflurane (6%, 4%, 2%, and 0%). Unsupervised machine learning applied to multiunit spike patterns revealed five distinct brain states. A novel desynchronized brain state with increased spike rate variability, sample entropy, and EMG activity occurred in 6% desflurane with 40.0% frequency. The other four brain states reflected graded levels of anesthesia. As anesthesia deepened the spike rate of neurons decreased regardless of their spike rate profile at baseline conscious state. Actively firing neurons with wide-spiking pattern showed increased bursting activity along with increased spike timing variability, unit-to-population correlation, and unit-to-unit transfer entropy, despite the overall decrease in transfer entropy. The narrow-spiking neurons showed similar changes but to a lesser degree. These results suggest that (1) anesthetic effect on spike rate is distinct from sleep, (2) synchronously fragmented spiking pattern is a signature of anesthetic-induced unconsciousness, and (3) the paradoxical, desynchronized brain state in deep anesthesia contends the generally presumed monotonic, dose-dependent anesthetic effect on the brain.SIGNIFICANCE STATEMENT Recent studies suggest that spontaneous changes in brain state occur under anesthesia. However, the spiking behavior of cortical neurons associated with such state changes has not been investigated. We found that local brain states defined by multiunit activity had a nonunitary relationship with the current anesthetic level. A paradoxical brain state displaying asynchronous firing pattern and high EMG activity was found unexpectedly in deep anesthesia. In contrast, the synchronous fragmentation of neuronal spiking appeared to be a robust signature of the state of anesthesia. The findings challenge the assumption of monotonic, anesthetic dose-dependent behavior of cortical neuron populations. They enhance the interpretation of neuroscientific data obtained under anesthesia and the understanding of the neuronal basis of anesthetic-induced state of unconsciousness.

Recent Advances in Electrical Neural Interface Engineering: Minimal Invasiveness, Longevity, and Scalability

Electrical neural interfaces serve as direct communication pathways that connect the nervous system with the external world. Technological advances in this domain are providing increasingly more powerful tools to study, restore, and augment neural functions. Yet, the complexities of the nervous system give rise to substantial challenges in the design, fabrication, and system-level integration of these functional devices. In this review, we present snapshots of the latest progresses in electrical neural interfaces, with an emphasis on advances that expand the spatiotemporal resolution and extent of mapping and manipulating brain circuits. We include discussions of large-scale, long-lasting neural recording; wireless, miniaturized implants; signal transmission, amplification, and processing; as well as the integration of interfaces with optical modalities. We outline the background and rationale of these developments and share insights into the future directions and new opportunities they enable.

Versatile live-cell activity analysis platform for characterization of neuronal dynamics at single-cell and network level

Chronic imaging of neuronal networks in vitro has provided fundamental insights into mechanisms underlying neuronal function. Current labeling and optical imaging methods, however, cannot be used for continuous and long-term recordings of the dynamics and evolution of neuronal networks, as fluorescent indicators can cause phototoxicity. Here, we introduce a versatile platform for label-free, comprehensive and detailed electrophysiological live-cell imaging of various neurogenic cells and tissues over extended time scales. We report on a dual-mode high-density microelectrode array, which can simultaneously record in (i) full-frame mode with 19,584 recording sites and (ii) high-signal-to-noise mode with 246 channels. We set out to demonstrate the capabilities of this platform with recordings from primary and iPSC-derived neuronal cultures and tissue preparations over several weeks, providing detailed morpho-electrical phenotypic parameters at subcellular, cellular and network level. Moreover, we develop reliable analysis tools, which drastically increase the throughput to infer axonal morphology and conduction speed.

Current methods of neuronal network imaging cannot be used for continuous, long-term functional recordings. Here, the authors present a dual-mode high-density microelectrode array, which can simultaneously record in full-frame and high-signal-to-noise modes for label-free electrophysiological measurements.

Layers II/III of Prefrontal Cortex in Df(h22q11)/+ Mouse Model of the 22q11.2 Deletion Display Loss of Parvalbumin Interneurons and Modulation of Neuronal Morphology and Excitability

The 22q11.2 deletion has been identified as a risk factor for multiple neurodevelopmental disorders. Behavioral and cognitive impairments are common among carriers of the 22q11.2 deletion. Parvalbumin expressing (PV⁺) interneurons provide perisomatic inhibition of excitatory neuronal circuits through GABA_A receptors, and a deficit of PV⁺ inhibitory circuits may underlie a multitude of the behavioral and functional deficits in the 22q11.2 deletion syndrome. We investigated putative deficits of PV⁺ inhibitory circuits and the associated molecular, morphological, and functional alterations in the prefrontal cortex (PFC) of the Df(h22q11)/+ mouse model of the 22q11.2 hemizygous deletion. We detected a significant decrease in the number of PV⁺ interneurons in layers II/III of PFC in Df(h22q11)/+ mice together with a reduction in the mRNA and protein levels of GABA_A (α3), a PV⁺ putative postsynaptic receptor subunit. Pyramidal neurons from the same layers further experienced morphological reorganizations of spines and dendrites. Accordingly, a decrease in the levels of the postsynaptic density protein 95 (PSD95) and a higher neuronal activity in response to the GABA_A antagonist bicuculline were measured in these layers in PFC of Df(h22q11)/+ mice compared with their wild-type littermates. Our study shows that a hemizygotic deletion of the 22q11.2 locus leads to deficit in the GABAergic control of network activity and involves molecular and morphological changes in both the inhibitory and excitatory synapses of parvalbumin interneurons and pyramidal neurons specifically in layers II/III PFC.

Towards optogenetic vision restoration with high resolution

In many cases of inherited retinal degenerations, ganglion cells are spared despite photoreceptor cell death, making it possible to stimulate them to restore visual function. Several studies have shown that it is possible to express an optogenetic protein in ganglion cells and make them light sensitive, a promising strategy to restore vision. However the spatial resolution of optogenetically-reactivated retinas has rarely been measured, especially in the primate. Since the optogenetic protein is also expressed in axons, it is unclear if these neurons will only be sensitive to the stimulation of a small region covering their somas and dendrites, or if they will also respond to any stimulation overlapping with their axon, dramatically impairing spatial resolution. Here we recorded responses of mouse and macaque retinas to random checkerboard patterns following an in vivo optogenetic therapy. We show that optogenetically activated ganglion cells are each sensitive to a small region of visual space. A simple model based on this small receptive field predicted accurately their responses to complex stimuli. From this model, we simulated how the entire population of light sensitive ganglion cells would respond to letters of different sizes. We then estimated the maximal acuity expected by a patient, assuming it could make an optimal use of the information delivered by this reactivated retina. The obtained acuity is above the limit of legal blindness. Our model also makes interesting predictions on how acuity might vary upon changing the therapeutic strategy, assuming an optimal use of the information present in the retinal activity. Optogenetic therapy could thus potentially lead to high resolution vision, under conditions that our model helps to determinine.

In many cases of blindness, ganglion cells, the retinal output, remain functional. A promising strategy to restore vision is to express optogenetic proteins in ganglion cells. However, it is not clear what is the resolution of this new light sensor. A major concern is that axons might become light sensitive, and a focal stimulation would activate a very broad area of the retina, dramatically impairing spatial resolution. Here we show that this is not the case. Ganglion cells are activated only by stimulations close to their soma. Using a combination of data analysis and modeling based on mouse and non-human primate retina recordings, we show that the acuity expected with this therapy could be above the level of legal blindness.

Recurrent circuitry is required to stabilize piriform cortex odor representations across brain states

Pattern completion, or the ability to retrieve stable neural activity patterns from noisy or partial cues, is a fundamental feature of memory. Theoretical studies indicate that recurrently connected auto-associative or discrete attractor networks can perform this process. Although pattern completion and attractor dynamics have been observed in various recurrent neural circuits, the role recurrent circuitry plays in implementing these processes remains unclear. In recordings from head-fixed mice, we found that odor responses in olfactory bulb degrade under ketamine/xylazine anesthesia while responses immediately downstream, in piriform cortex, remain robust. Recurrent connections are required to stabilize cortical odor representations across states. Moreover, piriform odor representations exhibit attractor dynamics, both within and across trials, and these are also abolished when recurrent circuitry is eliminated. Here, we present converging evidence that recurrently-connected piriform populations stabilize sensory representations in response to degraded inputs, consistent with an auto-associative function for piriform cortex supported by recurrent circuitry.

Desflurane Anesthesia Alters Cortical Layer-specific Hierarchical Interactions in Rat Cerebral Cortex

BACKGROUND

Neurocognitive investigations suggest that conscious sensory perception depends on recurrent neuronal interactions among sensory, parietal, and frontal cortical regions, which are suppressed by general anesthetics. The purpose of this work was to investigate if local interactions in sensory cortex are also altered by anesthetics. The authors hypothesized that desflurane would reduce recurrent neuronal interactions in cortical layer-specific manner consistent with the anatomical disposition of feedforward and feedback pathways.

METHODS

Single-unit neuronal activity was measured in freely moving adult male rats (268 units; 10 animals) using microelectrode arrays chronically implanted in primary and secondary visual cortex. Layer-specific directional interactions were estimated by mutual information and transfer entropy of multineuron spike patterns within and between cortical layers three and five. The effect of incrementally increasing and decreasing steady-state concentrations of desflurane (0 to 8% to 0%) was tested for statistically significant quadratic trend across the successive anesthetic states.

RESULTS

Desflurane produced robust, state-dependent reduction (P = 0.001) of neuronal interactions between primary and secondary visual areas and between layers three and five, as indicated by mutual information (37 and 41% decrease at 8% desflurane from wakeful baseline at [mean ± SD] 0.52 ± 0.51 and 0.53 ± 0.51 a.u., respectively) and transfer entropy (77 and 78% decrease at 8% desflurane from wakeful baseline at 1.86 ± 1.56 a.u. and 1.87 ± 1.67 a.u., respectively). In addition, a preferential suppression of feedback between secondary and primary visual cortex was suggested by the reduction of directional index of transfer entropy overall (P = 0.001; 89% decrease at 8% desflurane from 0.11 ± 0.18 a.u. at baseline) and specifically, in layer five (P = 0.001; 108% decrease at 8% desflurane from 0.12 ± 0.19 a.u. at baseline).

CONCLUSIONS

Desflurane anesthesia reduces neuronal interactions in visual cortex with a preferential effect on feedback. The findings suggest that neuronal disconnection occurs locally, among hierarchical sensory regions, which may contribute to global functional disconnection underlying anesthetic-induced unconsciousness.

SpikeForest, reproducible web-facing ground-truth validation of automated neural spike sorters

Spike sorting is a crucial step in electrophysiological studies of neuronal activity. While many spike sorting packages are available, there is little consensus about which are most accurate under different experimental conditions. SpikeForest is an open-source and reproducible software suite that benchmarks the performance of automated spike sorting algorithms across an extensive, curated database of ground-truth electrophysiological recordings, displaying results interactively on a continuously-updating website. With contributions from eleven laboratories, our database currently comprises 650 recordings (1.3 TB total size) with around 35,000 ground-truth units. These data include paired intracellular/extracellular recordings and state-of-the-art simulated recordings. Ten of the most popular spike sorting codes are wrapped in a Python package and evaluated on a compute cluster using an automated pipeline. SpikeForest documents community progress in automated spike sorting, and guides neuroscientists to an optimal choice of sorter and parameters for a wide range of probes and brain regions.

EZcalcium: Open-Source Toolbox for Analysis of Calcium Imaging Data

Fluorescence calcium imaging using a range of microscopy approaches, such as two-photon excitation or head-mounted “miniscopes,” is one of the preferred methods to record neuronal activity and glial signals in various experimental settings, including acute brain slices, brain organoids, and behaving animals. Because changes in the fluorescence intensity of genetically encoded or chemical calcium indicators correlate with action potential firing in neurons, data analysis is based on inferring such spiking from changes in pixel intensity values across time within different regions of interest. However, the algorithms necessary to extract biologically relevant information from these fluorescent signals are complex and require significant expertise in programming to develop robust analysis pipelines. For decades, the only way to perform these analyses was for individual laboratories to write their custom code. These routines were typically not well annotated and lacked intuitive graphical user interfaces (GUIs), which made it difficult for scientists in other laboratories to adopt them. Although the panorama is changing with recent tools like CaImAn, Suite2P, and others, there is still a barrier for many laboratories to adopt these packages, especially for potential users without sophisticated programming skills. As two-photon microscopes are becoming increasingly affordable, the bottleneck is no longer the hardware, but the software used to analyze the calcium data optimally and consistently across different groups. We addressed this unmet need by incorporating recent software solutions, namely NoRMCorre and CaImAn, for motion correction, segmentation, signal extraction, and deconvolution of calcium imaging data into an open-source, easy to use, GUI-based, intuitive and automated data analysis software package, which we named EZcalcium.

Recording local field potential and neuronal activity with tetrodes in epileptic patients

BACKGROUND

Recordings with tetrodes have proven to be more effective in isolating single neuron spiking activity than with single microwires. However, tetrodes have never been used in humans. We report on the characteristics, safety, compatibility with clinical intracranial recordings in epileptic patients, and performance, of a new type of hybrid electrode equipped with tetrodes.

NEW METHOD

240 standard clinical macroelectrodes and 102 hybrid electrodes were implanted in 28 patients. Hybrids (diameter 800 μm) are made of 6 or 9 macro-contacts and 2 or 3 tetrodes (diameter 70-80 μm).

RESULTS

No clinical complication or adverse event was associated with the hybrids. Impedance and noise of recordings were stable over time. The design enabled multiscale spatial analyses that revealed physiopathological events which were sometimes specific to one tetrode, but could not be recorded on the macro-contacts. After spike sorting, the single-unit yield was similar to other hybrid electrodes and was sometimes as high as >10 neurons per tetrode.

COMPARISON WITH EXISTING METHOD(S)

This new hybrid electrode has a smaller diameter than other available hybrid electrodes. It provides novel spatial information due to the configuration of the tetrodes. The single-unit yield appears promising.

CONCLUSIONS

This new hybrid electrode is safe, easy to use, and works satisfactorily for conducting multi-scale seizure and physiological analyses.

Median raphe controls acquisition of negative experience in the mouse

Adverse events need to be quickly evaluated and memorized, yet how these processes are coordinated is poorly understood. We discovered a large population of excitatory neurons in mouse median raphe region (MRR) expressing vesicular glutamate transporter 2 (vGluT2) that received inputs from several negative experience-related brain centers, projected to the main aversion centers, and activated the septohippocampal system pivotal for learning of adverse events. These neurons were selectively activated by aversive but not rewarding stimuli. Their stimulation induced place aversion, aggression, depression-related anhedonia, and suppression of reward-seeking behavior and memory acquisition-promoting hippocampal theta oscillations. By contrast, their suppression impaired both contextual and cued fear memory formation. These results suggest that MRR vGluT2 neurons are crucial for the acquisition of negative experiences and may play a central role in depression-related mood disorders.

Fronto-Temporal Coupling Dynamics During Spontaneous Activity and Auditory Processing in the Bat Carollia perspicillata

Most mammals rely on the extraction of acoustic information from the environment in order to survive. However, the mechanisms that support sound representation in auditory neural networks involving sensory and association brain areas remain underexplored. In this study, we address the functional connectivity between an auditory region in frontal cortex (the frontal auditory field, FAF) and the auditory cortex (AC) in the bat Carollia perspicillata. The AC is a classic sensory area central for the processing of acoustic information. On the other hand, the FAF belongs to the frontal lobe, a brain region involved in the integration of sensory inputs, modulation of cognitive states, and in the coordination of behavioral outputs. The FAF-AC network was examined in terms of oscillatory coherence (local-field potentials, LFPs), and within an information theoretical framework linking FAF and AC spiking activity. We show that in the absence of acoustic stimulation, simultaneously recorded LFPs from FAF and AC are coherent in low frequencies (1-12 Hz). This "default" coupling was strongest in deep AC layers and was unaltered by acoustic stimulation. However, presenting auditory stimuli did trigger the emergence of coherent auditory-evoked gamma-band activity (>25 Hz) between the FAF and AC. In terms of spiking, our results suggest that FAF and AC engage in distinct coding strategies for representing artificial and natural sounds. Taken together, our findings shed light onto the neuronal coding strategies and functional coupling mechanisms that enable sound representation at the network level in the mammalian brain.

Neural oscillations in the fronto-striatal network predict vocal output in bats

The ability to vocalize is ubiquitous in vertebrates, but neural networks underlying vocal control remain poorly understood. Here, we performed simultaneous neuronal recordings in the frontal cortex and dorsal striatum (caudate nucleus, CN) during the production of echolocation pulses and communication calls in bats. This approach allowed us to assess the general aspects underlying vocal production in mammals and the unique evolutionary adaptations of bat echolocation. Our data indicate that before vocalization, a distinctive change in high-gamma and beta oscillations (50–80 Hz and 12–30 Hz, respectively) takes place in the bat frontal cortex and dorsal striatum. Such precise fine-tuning of neural oscillations could allow animals to selectively activate motor programs required for the production of either echolocation or communication vocalizations. Moreover, the functional coupling between frontal and striatal areas, occurring in the theta oscillatory band (4–8 Hz), differs markedly at the millisecond level, depending on whether the animals are in a navigational mode (that is, emitting echolocation pulses) or in a social communication mode (emitting communication calls). Overall, this study indicates that fronto-striatal oscillations could provide a neural correlate for vocal control in bats.

In bats, rhythmic activity in frontal and striatal areas of the brain provide a neural correlate for vocal control, which can be used to predict whether the ensuing vocalizations are for echolocation or social communication.

A cerebellum-like circuit in the lateral line system of fish cancels mechanosensory input associated with its own movements

An animal's own movement exerts a profound impact on sensory input to its nervous system. Peripheral sensory receptors do not distinguish externally generated stimuli from stimuli generated by an animal's own behavior (reafference) - although the animal often must. One way that nervous systems can solve this problem is to provide movement-related signals (copies of motor commands and sensory feedback) to sensory systems, which can then be used to generate predictions that oppose or cancel out sensory responses to reafference. Here, we studied the use of movement-related signals to generate sensory predictions in the lateral line medial octavolateralis nucleus (MON) of the little skate. In the MON, mechanoreceptive afferents synapse on output neurons that also receive movement-related signals from central sources, via a granule cell parallel fiber system. This parallel fiber system organization is characteristic of a set of so-called cerebellum-like structures. Cerebellum-like structures have been shown to support predictive cancellation of reafference in the electrosensory systems of fish and the auditory system of mice. Here, we provide evidence that the parallel fiber system in the MON can generate predictions that are negative images of (and therefore cancel) sensory input associated with respiratory and fin movements. The MON, found in most aquatic vertebrates, is probably one of the most primitive cerebellum-like structures and a starting point for cerebellar evolution. The results of this study contribute to a growing body of work that uses an evolutionary perspective on the vertebrate cerebellum to understand its functional diversity in animal behavior.

Learning to Sort: Few-shot Spike Sorting with Adversarial Representation Learning

Spike sorting has long been used to obtain activities of single neurons from multi-unit recordings by extracting spikes from continuous data and assigning them to putative neurons. A large body of spike sorting algorithms have been developed that typically project spikes into a low-dimensional feature space and cluster them through iterative computations. However, there is no reached consensus on the optimal feature space or the best way of segmenting spikes into clusters, which often leads to the requirement of human intervention. It is hence desirable to effectively and efficiently utilize human knowledge in spike sorting while keeping a minimum level of manual intervention. Furthermore, the iterative computations that are commonly involved during clustering are inherently slow and hinder real-time processing of large-scale recordings. In this paper, we propose a novel few-shot spike sorting paradigm that employs a deep adversarial representation neural network to learn from a handful of annotated spikes and robustly classify unseen spikes sharing similar properties to the labeled ones. Once trained, the deep neural network can implement a parametric function that encodes analytically the categorical distribution of spike clusters, which can be significantly accelerated by GPUs and support processing hundreds of thousands of recording channels in real time. The paradigm also includes a clustering routine termed DidacticSortto aid users for labeling spikes that will be used to train the deep neural network. We have validated the performance of the proposed paradigm with both synthetic and in vitro datasets.

Deep Learning-Based Template Matching Spike Classification for Extracellular Recordings

We propose a deep learning-based spike sorting method for extracellular recordings. For analysis of extracellular single unit activity, the process of detecting and classifying action potentials called “spike sorting” has become essential. This is achieved through distinguishing the morphological differences of the spikes from each neuron, which arises from the differences of the surrounding environment and characteristics of the neurons. However, cases of high structural similarity and noise make the task difficult. And for manual spike sorting, it requires professional knowledge along with extensive time cost and suffers from human bias. We propose a deep learning-based spike sorting method on extracellular recordings from a single electrode that is efficient, robust to noise, and accurate. In circumstances where labelled data does not exist, we created pseudo-labels through principal component analysis and K-means clustering to be used for multi-layer perceptron training and built high performing spike classification model. When tested, our model outperformed conventional methods by 2.1% on simulation data of various noise levels, by 6.0% on simulation data of various clusters count, and by 1.7% on in-vivo data. As a result, we showed that the deep learning-based classification can classify spikes from extracellular recordings, even showing high classification accuracy on spikes that are difficult even for manual classification.

Discovering Precise Temporal Patterns in Large-Scale Neural Recordings through Robust and Interpretable Time Warping

Though the temporal precision of neural computation has been studied intensively, a data-driven determination of this precision remains a fundamental challenge. Reproducible spike patterns may be obscured on single trials by uncontrolled temporal variability in behavior and cognition and may not be time locked to measurable signatures in behavior or local field potentials (LFP). To overcome these challenges, we describe a general-purpose time warping framework that reveals precise spike-time patterns in an unsupervised manner, even when these patterns are decoupled from behavior or are temporally stretched across single trials. We demonstrate this method across diverse systems: cued reaching in nonhuman primates, motor sequence production in rats, and olfaction in mice. This approach flexibly uncovers diverse dynamical firing patterns, including pulsatile responses to behavioral events, LFP-aligned oscillatory spiking, and even unanticipated patterns, such as 7 Hz oscillations in rat motor cortex that are not time locked to measured behaviors or LFP.

Low-latency single channel real-time neural spike sorting system based on template matching

Recent technical advancements in neural engineering allow for precise recording and control of neural circuits simultaneously, opening up new opportunities for closed-loop neural control. In this work, a rapid spike sorting system was developed based on template matching to rapidly calculate instantaneous firing rates for each neuron in a multi-unit extracellular recording setting. Cluster templates were first generated by a desktop computer using a non-parameter spike sorting algorithm (Super-paramagnetic clustering) and then transferred to a field-programmable gate array digital circuit for rapid sorting through template matching. Two different matching techniques–Euclidean distance (ED) and correlational matching (CM)–were compared for the accuracy of sorting and the performance of calculating firing rates. The performance of the system was first verified using publicly available artificial data and was further confirmed with pre-recorded neural spikes from an anesthetized Mongolian gerbil. Real-time recording and sorting from an awake mouse were also conducted to confirm the system performance in a typical behavioral neuroscience experimental setting. Experimental results indicated that high sorting accuracies were achieved for both template-matching methods, but CM can better handle spikes with non-Gaussian spike distributions, making it more robust for in vivo recording. The technique was also compared to several other off-line spike sorting algorithms and the results indicated that the sorting accuracy is comparable but sorting time is significantly shorter than these other techniques. A low sorting latency of under 2 ms and a maximum spike sorting rate of 941 spikes/second have been achieved with our hybrid hardware/software system. The low sorting latency and fast sorting rate allow future system developments of neural circuit modulation through analyzing neural activities in real-time.

A projection specific logic to sampling visual inputs in mouse superior colliculus

Using sensory information to trigger different behaviors relies on circuits that pass through brain regions. The rules by which parallel inputs are routed to downstream targets are poorly understood. The superior colliculus mediates a set of innate behaviors, receiving input from >30 retinal ganglion cell types and projecting to behaviorally important targets including the pulvinar and parabigeminal nucleus. Combining transsynaptic circuit tracing with in vivo and ex vivo electrophysiological recordings, we observed a projection-specific logic where each collicular output pathway sampled a distinct set of retinal inputs. Neurons projecting to the pulvinar or the parabigeminal nucleus showed strongly biased sampling from four cell types each, while six others innervated both pathways. The visual response properties of retinal ganglion cells correlated well with those of their disynaptic targets. These findings open the possibility that projection-specific sampling of retinal inputs forms a basis for the selective triggering of behaviors by the superior colliculus.

Cluster tendency assessment in neuronal spike data

Sorting spikes from extracellular recording into clusters associated with distinct single units (putative neurons) is a fundamental step in analyzing neuronal populations. Such spike sorting is intrinsically unsupervised, as the number of neurons are not known a priori. Therefor, any spike sorting is an unsupervised learning problem that requires either of the two approaches: specification of a fixed value k for the number of clusters to seek, or generation of candidate partitions for several possible values of c, followed by selection of a best candidate based on various post-clustering validation criteria. In this paper, we investigate the first approach and evaluate the utility of several methods for providing lower dimensional visualization of the cluster structure and on subsequent spike clustering. We also introduce a visualization technique called improved visual assessment of cluster tendency (iVAT) to estimate possible cluster structures in data without the need for dimensionality reduction. Experimental results are conducted on two datasets with ground truth labels. In data with a relatively small number of clusters, iVAT is beneficial in estimating the number of clusters to inform the initialization of clustering algorithms. With larger numbers of clusters, iVAT gives a useful estimate of the coarse cluster structure but sometimes fails to indicate the presumptive number of clusters. We show that noise associated with recording extracellular neuronal potentials can disrupt computational clustering schemes, highlighting the benefit of probabilistic clustering models. Our results show that t-Distributed Stochastic Neighbor Embedding (t-SNE) provides representations of the data that yield more accurate visualization of potential cluster structure to inform the clustering stage. Moreover, The clusters obtained using t-SNE features were more reliable than the clusters obtained using the other methods, which indicates that t-SNE can potentially be used for both visualization and to extract features to be used by any clustering algorithm.

Whole-Brain Functional Ultrasound Imaging Reveals Brain Modules for Visuomotor Integration

Large numbers of brain regions are active during behaviors. A high-resolution, brain-wide activity map could identify brain regions involved in specific behaviors. We have developed functional ultrasound imaging to record whole-brain activity in behaving mice at a resolution of ∼100 μm. We detected 87 active brain regions during visual stimulation that evoked the optokinetic reflex, a visuomotor behavior that stabilizes the gaze both horizontally and vertically. Using a genetic mouse model of congenital nystagmus incapable of generating the horizontal reflex, we identified a subset of regions whose activity was reflex dependent. By blocking eye motion in control animals, we further separated regions whose activity depended on the reflex’s motor output. Remarkably, all reflex-dependent but eye motion-independent regions were located in the thalamus. Our work identifies functional modules of brain regions involved in sensorimotor integration and provides an experimental approach to monitor whole-brain activity of mice in normal and disease states.

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Functional ultrasound enables imaging whole-brain activity during mouse behavior

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Activity in 87 brain regions are modulated during the optokinetic reflex

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Reflex-related regions were identified by perturbing retinal direction selectivity

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A subset of these regions, all in the thalamus, are independent of eye motion

SpikeDeeptector: a deep-learning based method for detection of neural spiking activity

OBJECTIVE

In electrophysiology, microelectrodes are the primary source for recording neural data (single unit activity). These microelectrodes can be implanted individually or in the form of arrays containing dozens to hundreds of channels. Recordings of some channels contain neural activity, which are often contaminated with noise. Another fraction of channels does not record any neural data, but only noise. By noise, we mean physiological activities unrelated to spiking, including technical artifacts and neural activities of neurons that are too far away from the electrode to be usefully processed. For further analysis, an automatic identification and continuous tracking of channels containing neural data is of great significance for many applications, e.g. automated selection of neural channels during online and offline spike sorting. Automated spike detection and sorting is also critical for online decoding in brain-computer interface (BCI) applications, in which only simple threshold crossing events are often considered for feature extraction. To our knowledge, there is no method that can universally and automatically identify channels containing neural data. In this study, we aim to identify and track channels containing neural data from implanted electrodes, automatically and more importantly universally. By universally, we mean across different recording technologies, different subjects and different brain areas.

APPROACH

We propose a novel algorithm based on a new way of feature vector extraction and a deep learning method, which we call SpikeDeeptector. SpikeDeeptector considers a batch of waveforms to construct a single feature vector and enables contextual learning. The feature vectors are then fed to a deep learning method, which learns contextualized, temporal and spatial patterns, and classifies them as channels containing neural spike data or only noise.

MAIN RESULTS

We trained the model of SpikeDeeptector on data recorded from a single tetraplegic patient with two Utah arrays implanted in different areas of the brain. The trained model was then evaluated on data collected from six epileptic patients implanted with depth electrodes, unseen data from the tetraplegic patient and data from another tetraplegic patient implanted with two Utah arrays. The cumulative evaluation accuracy was 97.20% on 1.56 million hand labeled test inputs.

SIGNIFICANCE

The results demonstrate that SpikeDeeptector generalizes not only to the new data, but also to different brain areas, subjects, and electrode types not used for training.

CLINICAL TRIAL REGISTRATION NUMBER

The clinical trial registration number for patients implanted with the Utah array is NCT01849822. For the epilepsy patients, approval from the local ethics committee at the Ruhr-University Bochum, Germany, was obtained prior to implantation.

Single-cell micro- and nano-photonic technologies

Since the advent of optogenetics, the technology development has focused on new methods to optically interact with single nerve cells. This gave rise to the field of photonic neural interfaces, intended as the set of technologies that can modify light radiation in either a linear or non-linear fashion to control and/or monitor cellular functions. This set includes the use of plasmonic effects, up-conversion, electron transfer and integrated light steering, with some of them already implemented in vivo. This article will review available approaches in this framework, with a particular emphasis on methods operating at the single-unit level or having the potential to reach single-cell resolution.

Evaluation of Spike Sorting Algorithms: Application to Human Subthalamic Nucleus Recordings and Simulations

An important prerequisite for the analysis of spike synchrony in extracellular recordings is the extraction of single-unit activity from the multi-unit signal. To identify single units, potential spikes are separated with respect to their potential neuronal origins ('spike sorting'). However, different sorting algorithms yield inconsistent unit assignments, which seriously influences subsequent spike train analyses. We aim to identify the best sorting algorithm for subthalamic nucleus recordings of patients with Parkinson's disease (experimental data ED). Therefore, we apply various prevalent algorithms offered by the 'Plexon Offline Sorter' and evaluate the sorting results. Since this evaluation leaves us unsure about the best algorithm, we apply all methods again to artificial data (AD) with known ground truth. AD consists of pairs of single units with different shape similarity embedded in the background noise of the ED. The sorting evaluation depicts a significant influence of the respective methods on the single unit assignments. We find a high variability in the sortings obtained by different algorithms that increases with single units shape similarity. We also find significant differences in the resulting firing characteristics. We conclude that Valley-Seeking algorithms produce the most accurate result if the exclusion of artifacts as unsorted events is important. If the latter is less important ('clean' data) the K-Means algorithm is a better option. Our results strongly argue for the need of standardized validation procedures based on ground truth data. The recipe suggested here is simple enough to become a standard procedure.

Decoupling of timescales reveals sparse convergent CPG network in the adult spinal cord

During the generation of rhythmic movements, most spinal neurons receive an oscillatory synaptic drive. The neuronal architecture underlying this drive is unknown, and the corresponding network size and sparseness have not yet been addressed. If the input originates from a small central pattern generator (CPG) with dense divergent connectivity, it will induce correlated input to all receiving neurons, while sparse convergent wiring will induce a weak correlation, if any. Here, we use pairwise recordings of spinal neurons to measure synaptic correlations and thus infer the wiring architecture qualitatively. A strong correlation on a slow timescale implies functional relatedness and a common source, which will also cause correlation on fast timescale due to shared synaptic connections. However, we consistently find marginal coupling between slow and fast correlations regardless of neuronal identity. This suggests either sparse convergent connectivity or a CPG network with recurrent inhibition that actively decorrelates common input.

A data-driven regularization approach for template matching in spike sorting with high-density neural probes

Spike sorting is the process of assigning neural spikes in an extracellular brain recording to their putative neurons. Optimal pre-whitened template matching filters that are used in spike sorting typically suffer from ill-conditioning. In this paper, we investigate the origin of this ill-conditioning and the way in which it influences the resulting filters. Two data-driven subspace regularization approaches are proposed, and those are shown to outperform a regularization approach used in recent literature. The comparison of the methods is based on ground truth data that are recorded in-vivo.