Large Populations Of Neurons Research Articles

A Modular Implementation to Handle and Benchmark Drift Correction for High-Density Extracellular Recordings.

High-density neural devices are now offering the possibility to record from neuronal populations in vivo at unprecedented scale. However, the mechanical drifts often observed in these recordings are currently a major issue for "spike sorting," an essential analysis step to identify the activity of single neurons from extracellular signals. Although several strategies have been proposed to compensate for such drifts, the lack of proper benchmarks makes it hard to assess the quality and effectiveness of motion correction. In this paper, we present a benchmark study to precisely and quantitatively evaluate the performance of several state-of-the-art motion correction algorithms introduced in the literature. Using simulated recordings with induced drifts, we dissect the origins of the errors performed while applying a motion correction algorithm as a preprocessing step in the spike sorting pipeline. We show how important it is to properly estimate the positions of the neurons from extracellular traces in order to correctly estimate the probe motion, compare several interpolation procedures, and highlight what are the current limits for motion correction approaches.

Model-Based Explainable Deep Learning for Light-Field Microscopy Imaging.

In modern neuroscience, observing the dynamics of large populations of neurons is a critical step of understanding how networks of neurons process information. Light-field microscopy (LFM) has emerged as a type of scanless, high-speed, three-dimensional (3D) imaging tool, particularly attractive for this purpose. Imaging neuronal activity using LFM calls for the development of novel computational approaches that fully exploit domain knowledge embedded in physics and optics models, as well as enabling high interpretability and transparency. To this end, we propose a model-based explainable deep learning approach for LFM. Different from purely data-driven methods, the proposed approach integrates wave-optics theory, sparse representation and non-linear optimization with the artificial neural network. In particular, the architecture of the proposed neural network is designed following precise signal and optimization models. Moreover, the network's parameters are learned from a training dataset using a novel training strategy that integrates layer-wise training with tailored knowledge distillation. Such design allows the network to take advantage of domain knowledge and learned new features. It combines the benefit of both model-based and learning-based methods, thereby contributing to superior interpretability, transparency and performance. By evaluating on both structural and functional LFM data obtained from scattering mammalian brain tissues, we demonstrate the capabilities of the proposed approach to achieve fast, robust 3D localization of neuron sources and accurate neural activity identification.

Dendritic filtering determines the frequency-dependent spike train correlations

Temporal correlation between neural discharge sequences induced by correlated synaptic inputs distributes widely in large population of neurons. However, how synaptic input frequency affects the spike train correlation remains largely unclear. To address this question, we use a pair of unconnected ball-and-stick (BS) model neurons of integrate-and-fire (IF) type to simulate the correlated spike trains when receiving the shared synaptic inputs with specific frequency at the distal dendrite. We derive the relevant simplified point (SP) neuron model analytically to well reproduce the BS model dynamics with suprathreshold inputs. We show that the output correlation is negatively correlated to input frequency, and this frequency-dependent correlation is modulated by dendritic morphologies and synaptic positions. Our results highlight the dependence of spike train correlation on input frequency and suggest that the dendritic filtering is a primary factor in shaping this correlation-frequency relationship using SP models and morphologically realistic model neurons. These predictions should be considered when understanding the neural population correlations. Data availabilityNo data was used for the research described in the article.

All-optical interrogation of millimeter-scale networks and application to developing ferret cortex

BackgroundPerception and behavior require coordinated activity of thousands of neurons operating in networks that span millimeters of brain area. In vivo calcium imaging approaches have proven exceptionally powerful for examining the structure of these networks at large scales, and optogenetics can allow for causal manipulations of large populations of neurons. However, realizing the full potential of these techniques requires the ability to simultaneously measure and manipulate distinct circuit elements on the scale of millimeters. New methodWe describe an opto-macroscope, an artifact-free, all-optical system capable of delivering patterned optogenetic stimulation with high spatial and temporal resolution across millimeters of brain while simultaneously imaging functional neural activity. ResultsWe find that this approach provides direct manipulation of cortical regions ranging from hundreds of microns to several millimeters in area, allowing for the perturbation of individual brain areas or networks of functional domains. Using this system we find that spatially complex endogenous networks in the developing ferret visual cortex can be readily reactivated by precisely designed patterned optogenetic stimuli. Comparison with existing methodsOur opto-macroscope extends current all-optical optogenetic approaches which operate on a cellular scale with multiphoton stimulation, and are poorly suited to investigate the millimeter-scale of many functional networks. It also builds upon other mesoscopic optogenetic techniques that lack simultaneous optical readouts of neural activity. ConclusionsThe large-scale all-optical capabilities of our system make it a powerful new tool for investigating the contribution of cortical domains and brain areas to the functional neural networks that underlie perception and behavior.

A paradigm for ethanol consumption in head-fixed mice during prefrontal cortical two-photon calcium imaging

The prefrontal cortex (PFC) is a hub for cognitive behaviors and is a key target for neuroadaptations in alcohol use disorders. Recent advances in genetically encoded sensors and functional microscopy allow multimodal in vivo PFC activity recordings at subcellular and cellular scales. While these methods could enable a deeper understanding of the relationship between alcohol and PFC function/dysfunction, they typically require animals to be head-fixed. Here, we present a method in mice for binge-like ethanol consumption during head-fixation. Male and female mice were first acclimated to ethanol by providing home cage access to 20% ethanol (v/v) for 4 or 8 days. After home cage drinking, mice consumed ethanol from a lick spout during head-fixation. We used two-photon calcium imaging during the head-fixed drinking paradigm to record from a large population of PFC neurons (>1000) to explore how acute ethanol affects their activity. Drinking exerted temporally heterogeneous effects on PFC activity at single neuron and population levels. Intoxication modulated the tonic activity of some neurons while others showed phasic responses around ethanol receipt. Population level activity did not show tonic or phasic modulation but tracked ethanol consumption over the minute-timescale. Network level interactions assessed through between-neuron pairwise correlations were largely resilient to intoxication at the population level while neurons with increased tonic activity showed higher synchrony by the end of the drinking period. By establishing a method for binge-like drinking in head-fixed mice, we lay the groundwork for leveraging advanced microscopy technologies to study alcohol-induced neuroadaptations in PFC and other brain circuits.This article is part of the Special Issue on "PFC circuit function in psychiatric disease and relevant models".

Activity-dependent ectopic action potentials in regular-spiking neurons of the neocortex.

Action potentials usually travel orthodromically along a neuron's axon, from the axon initial segment (AIS) toward the presynaptic terminals. Under some circumstances action potentials also travel in the opposite direction, antidromically, after being initiated at a distal location. Given their initiation at an atypical site, we refer to these events as "ectopic action potentials." Ectopic action potentials (EAPs) were initially observed in pathological conditions including seizures and nerve injury. Several studies have described regular-spiking (RS) pyramidal neurons firing EAPs in seizure models. Under nonpathological conditions, EAPs were reported in a few populations of neurons, and our group has found that EAPs can be induced in a large proportion of parvalbumin-expressing interneurons in the neocortex. Nevertheless, to our knowledge there have been no prior reports of ectopic firing in the largest population of neurons in the neocortex, pyramidal neurons, under nonpathological conditions. We performed in vitro recordings utilizing the whole-cell patch clamp technique. To elicit EAPs, we triggered orthodromic action potentialswith either long, progressively increasing current steps, or with trains of brief pulses at 30, 60, or 100 Hz delivered in 3 different ways, varying in stimulus and resting period duration. We found that a large proportion (72.7%) of neocortical RS cells from mice can fire EAPs after a specific stimulus in vitro, and that most RS cells (56.1%) are capable of firing EAPs across a broad range of stimulus conditions. Of the 37 RS neurons in which we were able to elicit EAPs, it took an average of 863.8 orthodromic action potentials delivered over the course of an average of ~81.4 s before the first EAP was seen. We observed that some cells responded to specific stimulus frequencies while less selective, suggesting frequency tuning in a subset of the cells. Our findings suggest that pyramidal cells can integrate information over long time-scales before briefly entering a mode of self-generated firing that originates in distal axons. The surprising ubiquity of EAP generation in RS cells raises interesting questions about the potential roles of ectopic spiking in information processing, cortical oscillations, and seizure susceptibility.

Vagal sensory neurons mediate the Bezold–Jarisch reflex and induce syncope

Visceral sensory pathways mediate homeostatic reflexes, the dysfunction of which leads to many neurological disorders1. The Bezold–Jarisch reflex (BJR), first described2,3 in 1867, is a cardioinhibitory reflex that is speculated to be mediated by vagal sensory neurons (VSNs) that also triggers syncope. However, the molecular identity, anatomical organization, physiological characteristics and behavioural influence of cardiac VSNs remain mostly unknown. Here we leveraged single-cell RNA-sequencing data and HYBRiD tissue clearing4 to show that VSNs that express neuropeptide Y receptor Y2 (NPY2R) predominately connect the heart ventricular wall to the area postrema. Optogenetic activation of NPY2R VSNs elicits the classic triad of BJR responses—hypotension, bradycardia and suppressed respiration—and causes an animal to faint. Photostimulation during high-resolution echocardiography and laser Doppler flowmetry with behavioural observation revealed a range of phenotypes reflected in clinical syncope, including reduced cardiac output, cerebral hypoperfusion, pupil dilation and eye-roll. Large-scale Neuropixels brain recordings and machine-learning-based modelling showed that this manipulation causes the suppression of activity across a large distributed neuronal population that is not explained by changes in spontaneous behavioural movements. Additionally, bidirectional manipulation of the periventricular zone had a push–pull effect, with inhibition leading to longer syncope periods and activation inducing arousal. Finally, ablating NPY2R VSNs specifically abolished the BJR. Combined, these results demonstrate a genetically defined cardiac reflex that recapitulates characteristics of human syncope at physiological, behavioural and neural network levels.

Non-consummatory behavior signals predict aversion-resistant alcohol drinking in head-fixed mice

A key facet of alcohol use disorder is continuing to drink alcohol despite negative consequences (so called “aversion-resistant drinking”). In this study, we sought to assess the degree to which head-fixed mice exhibit aversion-resistant drinking and to leverage behavioral analysis techniques available in head-fixture to relate non-consummatory behaviors to aversion-resistant drinking. We assessed aversion-resistant drinking in head-fixed female and male C57BL/6 J mice. We adulterated 20% (v/v) alcohol with varying concentrations of the bitter tastant quinine to measure the degree to which mice would continue to drink despite this aversive stimulus. We recorded high-resolution video of the mice during head-fixed drinking, tracked body parts with machine vision tools, and analyzed body movements in relation to consumption. Female and male head-fixed mice exhibited heterogenous levels of aversion-resistant drinking. Additionally, non-consummatory behaviors, such as paw movement and snout movement, were related to the intensity of aversion-resistant drinking. These studies demonstrate that head-fixed mice exhibit aversion-resistant drinking and that non-consummatory behaviors can be used to assess perceived aversiveness in this paradigm. Furthermore, these studies lay the groundwork for future experiments that will utilize advanced electrophysiological techniques to record from large populations of neurons during aversion-resistant drinking to understand the neurocomputational processes that drive this clinically relevant behavior.This article is part of the Special Issue on “PFC circuit function in psychiatric disease and relevant models”.

The Co-Expression Pattern of Calcium-Binding Proteins with γ-Aminobutyric Acid and Glutamate Transporters in the Amygdala of the Guinea Pig: Evidence for Glutamatergic Subpopulations.

The amygdala has large populations of neurons utilizing specific calcium-binding proteins such as parvalbumin (PV), calbindin (CB), or calretinin (CR). They are considered specialized subsets of γ-aminobutyric acid (GABA) interneurons; however, many of these cells are devoid of GABA or glutamate decarboxylase. The neurotransmitters used by GABA-immunonegative cells are still unknown, but it is suggested that a part may use glutamate. Thus, this study investigates in the amygdala of the guinea pig relationships between PV, CB, or CR-containing cells and GABA transporter (VGAT) or glutamate transporter type 2 (VGLUT2), markers of GABAergic and glutamatergic neurons, respectively. The results show that although most neurons using PV, CB, and CR co-expressed VGAT, each of these populations also had a fraction of VGLUT2 co-expressing cells. For almost all neurons using PV (~90%) co-expressed VGAT, while ~1.5% of them had VGLUT2. The proportion of neurons using CB and VGAT was smaller than that for PV (~80%), while the percentage of cells with VGLUT2 was larger (~4.5%). Finally, only half of the neurons using CR (~53%) co-expressed VGAT, while ~3.5% of them had VGLUT2. In conclusion, the populations of neurons co-expressing PV, CB, and CR are in the amygdala, primarily GABAergic. However, at least a fraction of neurons in each of them co-express VGLUT2, suggesting that these cells may use glutamate. Moreover, the number of PV-, CB-, and CR-containing neurons that may use glutamate is probably larger as they can utilize VGLUT1 or VGLUT3, which are also present in the amygdala.

FIOLA: an accelerated pipeline for fluorescence imaging online analysis.

Optical microscopy methods such as calcium and voltage imaging enable fast activity readout of large neuronal populations using light. However, the lack of corresponding advances in online algorithms has slowed progress in retrieving information about neural activity during or shortly after an experiment. This gap not only prevents the execution of real-time closed-loop experiments, but also hampers fast experiment-analysis-theory turnover for high-throughput imaging modalities. Reliable extraction of neural activity from fluorescence imaging frames at speeds compatible with indicator dynamics and imaging modalities poses a challenge. We therefore developed FIOLA, a framework for fluorescence imaging online analysis that extracts neuronal activity from calcium and voltage imaging movies at speeds one order of magnitude faster than state-of-the-art methods. FIOLA exploits algorithms optimized for parallel processing on GPUs and CPUs. We demonstrate reliable and scalable performance of FIOLA on both simulated and real calcium and voltage imaging datasets. Finally, we present an online experimental scenario to provide guidance in setting FIOLA parameters and to highlight the trade-offs of our approach.

Small-correlation expansion to quantify information in noisy sensory systems.

Neural networks encode information through their collective spiking activity in response to external stimuli. This population response is noisy and strongly correlated, with a complex interplay between correlations induced by the stimulus, and correlations caused by shared noise. Understanding how these correlations affect information transmission has so far been limited to pairs or small groups of neurons, because the curse of dimensionality impedes the evaluation of mutual information in larger populations. Here, we develop a small-correlation expansion to compute the stimulus information carried by a large population of neurons, yielding interpretable analytical expressions in terms of the neurons' firing rates and pairwise correlations. We validate the approximation on synthetic data and demonstrate its applicability to electrophysiological recordings in the vertebrate retina, allowing us to quantify the effects of noise correlations between neurons and of memory in single neurons.

Evolution of surface electromyography: From muscle electrophysiology towards neural recording and interfacing.

Surface electromyography (EMG) comprises a recording of electrical activity from the body surface generated by muscle fibres during muscle contractions. Its characteristics depend on the fibre membrane potentials and the neural activation signal sent from the motor neurons to the muscles. EMG has been classically used as the primary investigation tool in kinesiology studies in a variety of applications. More recently, surface EMG techniques have evolved from single-channel methods to high-density systems with hundreds of electrodes. High-density EMG recordings can be deconvolved to estimate the discharge times of spinal motor neurons innervating the recorded muscles, with algorithms that have been developed and validated in the last two decades. Within limits and with some variability across muscles, these techniques provide a non-invasive method to study relatively large populations of motor neurons in humans. Surface EMG is thus evolving from a peripheral measure of muscle electrical activity towards a neural recording and neural interfacing signal. These advances in technology have had a major impact on our fundamental understanding of the neural control of movement and have exposed new perspectives in neurotechnologies. Here we provide an overview and perspective of modern EMG technology, as derived from past achievements, and its impact in neurophysiology and neural engineering.

Developmental emergence of cortical neurogliaform cell diversity.

GABAergic interneurons are key regulators of cortical circuit function. Among the dozens of reported transcriptionally distinct subtypes of cortical interneurons, neurogliaform cells (NGC) are unique: recruited by long-range excitatory inputs, source of slow cortical inhibition and able to modulate the activity of large neuronal populations. Despite their functional relevance, the developmental emergence and diversity of NGCs remains unclear. Here, by combining single-cell transcriptomics, genetic fate-mapping, electrophysiological and morphological characterization, we reveal that discrete molecular subtypes of NGCs, with distinctive anatomical and molecular profiles populate the neocortex. Furthermore, we show that NGC subtypes emerge gradually through development, as incipient discriminant molecular signatures are apparent in Preoptic Area (POA) born NGC precursors. By identifying NGC developmentally-conserved transcriptional programs, we report that the transcription factor Tox2 constitutes an identity hallmark across NGC subtypes. Using CRISPR-Cas9-mediated genetic loss-of-function, we show that Tox2 is critical for NGC development: POA-born cells lacking Tox2 fail to differentiate into NGCs. Together, these results reveal that NGCs are born from a spatially restricted pool of Tox2+ POA precursors, after which intra-type diverging molecular programs are gradually acquired post-mitotically and result in functionally and molecularly discrete NGC cortical subtypes.

Time and experience differentially affect distinct aspects of hippocampal representational drift

Hippocampal activity is critical for spatial memory. Within a fixed, familiar environment, hippocampal codes gradually change over timescales of days to weeks-a phenomenon known as representational drift. The passage of time and the amount of experience are two factors that profoundly affect memory. However, thus far, it has remained unclear to what extent these factors drive hippocampal representational drift. Here, we longitudinally recorded large populations of hippocampal neurons in mice while they repeatedly explored two different familiar environments that they visited at different time intervals over weeks. We foundthat time and experience differentially affected distinct aspects of representational drift: the passage of time drove changes in neuronal activity rates, whereas experience drove changes in the cells' spatial tuning.Changes in spatial tuning were context specific and largely independent of changes in activity rates. Thus, our results suggest that representational drift is a multi-faceted process governed by distinct neuronal mechanisms.

A CMOS-based highly scalable flexible neural electrode interface.

Perception, thoughts, and actions are encoded by the coordinated activity of large neuronal populations spread over large areas. However, existing electrophysiological devices are limited by their scalability in capturing this cortex-wide activity. Here, we developed an electrode connector based on an ultra-conformable thin-film electrode array that self-assembles onto silicon microelectrode arrays enabling multithousand channel counts at a millimeter scale. The interconnects are formed using microfabricated electrode pads suspended by thin support arms, termed Flex2Chip. Capillary-assisted assembly drives the pads to deform toward the chip surface, and van der Waals forces maintain this deformation, establishing Ohmic contact. Flex2Chip arrays successfully measured extracellular action potentials ex vivo and resolved micrometer scale seizure propagation trajectories in epileptic mice. We find that seizure dynamics in absence epilepsy in the Scn8a+/- model do not have constant propagation trajectories.

Can the Clocks Tick Together Despite the Noise? Stochastic Simulations and Analysis

The suprachiasmatic nucleus (SCN), also known as the circadian master clock, consists of a large population of oscillator neurons. Together, these neurons produce a coherent signal that drives the body’s circadian rhythms. What properties of the cell-to-cell communication allow the synchronization of these neurons, despite a wide range of environmental challenges such as fluctuations in photoperiods? To answer that question, we present a mean-field description of globally coupled neurons modeled as Goodwin oscillators with standard Gaussian noise. Provided that the initial conditions of all neurons are independent and identically distributed, any finite number of neurons becomes independent and has the same probability distribution in the mean-field limit, a phenomenon called propagation of chaos. This probability distribution is a solution to a Vlasov–Fokker–Planck type equation, which can be obtained from the stochastic particle model. We study, using the macroscopic description, how the interaction between external noise and intercellular coupling affects the dynamics of the collective rhythm, and we provide a numerical description of the bifurcations resulting from the noise-induced transitions. Our numerical simulations show a noise-induced rhythm generation at low noise intensities, while the SCN clock is arrhythmic in the high noise setting. Notably, coupling induces resonance-like behavior at low noise intensities, and varying coupling strength can cause period locking and variance dissipation even in the presence of noise.

Inference of Electrical Stimulation Sensitivity from Recorded Activity of Primate Retinal Ganglion Cells.

High-fidelity electronic implants can in principle restore the function of neural circuits by precisely activating neurons via extracellular stimulation. However, direct characterization of the individual electrical sensitivity of a large population of target neurons, to precisely control their activity, can be difficult or impossible. A potential solution is to leverage biophysical principles to infer sensitivity to electrical stimulation from features of spontaneous electrical activity, which can be recorded relatively easily. Here, this approach is developed and its potential value for vision restoration is tested quantitatively using large-scale multielectrode stimulation and recording from retinal ganglion cells (RGCs) of male and female macaque monkeys ex vivo Electrodes recording larger spikes from a given cell exhibited lower stimulation thresholds across cell types, retinas, and eccentricities, with systematic and distinct trends for somas and axons. Thresholds for somatic stimulation increased with distance from the axon initial segment. The dependence of spike probability on injected current was inversely related to threshold, and was substantially steeper for axonal than somatic compartments, which could be identified by their recorded electrical signatures. Dendritic stimulation was largely ineffective for eliciting spikes. These trends were quantitatively reproduced with biophysical simulations. Results from human RGCs were broadly similar. The inference of stimulation sensitivity from recorded electrical features was tested in a data-driven simulation of visual reconstruction, revealing that the approach could significantly improve the function of future high-fidelity retinal implants.SIGNIFICANCE STATEMENT This study demonstrates that individual in situ primate retinal ganglion cells of different types respond to artificially generated, external electrical fields in a systematic manner, in accordance with theoretical predictions, that allows for prediction of electrical stimulus sensitivity from recorded spontaneous activity. It also provides evidence that such an approach could be immensely helpful in the calibration of clinical retinal implants.

Constructive role of shot noise in the collective dynamics of neural networks.

Finite-size effects may significantly influence the collective dynamics of large populations of neurons. Recently, we have shown that in globally coupled networks these effects can be interpreted as additional common noise term, the so-called shot noise, to the macroscopic dynamics unfolding in the thermodynamic limit. Here, we continue to explore the role of the shot noise in the collective dynamics of globally coupled neural networks. Namely, we study the noise-induced switching between different macroscopic regimes. We show that shot noise can turn attractors of the infinitely large network into metastable states whose lifetimes smoothly depend on the system parameters. A surprising effect is that the shot noise modifies the region where a certain macroscopic regime exists compared to the thermodynamic limit. This may be interpreted as a constructive role of the shot noise since a certain macroscopic state appears in a parameter region where it does not exist in an infinite network.

Anesthesia and the Merry-go-round of Information in the Brain.

Anesthesia and the Merry-go-round of Information in the Brain.

Cortex-wide neural dynamics predict behavioral states and provide a neural basis for resting-state dynamic functional connectivity

Although resting-state functional magnetic resonance imaging (fMRI) studies have observed dynamically changing brain-wide networks of correlated activity, fMRI's dependence on hemodynamic signals makes results challenging to interpret. Meanwhile, emerging techniques for real-time recording of large populations of neurons have revealed compelling fluctuations in neuronal activity across the brain that are obscured by traditional trial averaging. To reconcile these observations, we use wide-field optical mapping to simultaneously record pan-cortical neuronal and hemodynamic activity in awake, spontaneously behaving mice. Some components of observed neuronal activity clearly represent sensory and motor function. However, particularly during quiet rest, strongly fluctuating patterns of activity across diverse brain regions contribute greatly to interregional correlations. Dynamic changes in these correlations coincide with changes in arousal state. Simultaneously acquired hemodynamics depict similar brain-state-dependent correlation shifts. These results support a neural basis for dynamic resting-state fMRI, while highlighting the importance of brain-wide neuronal fluctuations in the study of brain state.