Large Populations Of Neurons Research Articles

Estimating average single-neuron visual receptive field sizes by fMRI

The noninvasive estimation of neuronal receptive field (RF) properties in vivo allows a detailed understanding of brain organization as well as its plasticity by longitudinal following of potential changes. Visual RFs measured invasively by electrophysiology in animal models have traditionally provided a great extent of our current knowledge about the visual brain and its disorders. Voxel-based estimates of population RF (pRF) by functional magnetic resonance imaging (fMRI) in humans revolutionized the field and have been used extensively in numerous studies. However, current methods cannot estimate single-neuron RF sizes as they reflect large populations of neurons with individual RF scatter. Here, we introduce an approach to estimate RF size using spatial frequency selectivity to checkerboard patterns. This method allowed us to obtain noninvasive, average single-neuron RF estimates over a large portion of human early visual cortex. These estimates were significantly smaller compared with prior pRF methods. Furthermore, fMRI and electrophysiology experiments in nonhuman primates demonstrated an exceptionally good match, validating the approach.

You are as fast as your motor neurons: speed of recruitment and maximal discharge of motor neurons determine the maximal rate of force development in humans.

We propose and validate a method for accurately identifying the activity of populations of motor neurons during contractions at maximal rate of force development in humans. The behaviour of the motor neuron pool during rapid voluntary contractions in humans is presented. We show with this approach that the motor neuron recruitment speed and maximal motor unit discharge rate largely explains the individual ability in generating rapid force contractions. The results also indicate that the synaptic inputs received by the motor neurons before force is generated dictate human potential to generate force rapidly. This is the first characterization of the discharge behaviour of a representative sample of human motor neurons during rapid contractions. During rapid contractions, motor neurons are recruited in a short burst and begin to discharge at high frequencies (up to >200Hz). In the present study, we investigated the behaviour of relatively large populations of motor neurons during rapid (explosive) contractions in humans, applying a new approach to accurately identify motor neuron activity simultaneous to measuring the rate of force development. The activity of spinal motor neurons was assessed by high-density electromyographic decomposition from the tibialis anterior muscle of 20 men during isometric explosive contractions. The speed of motor neuron recruitment and the instantaneous motor unit discharge rate were analysed as a function of the impulse (the time-force integral) and the maximal rate of force development. The peak of motor unit discharge rate occurred before force generation and discharge rates decreased thereafter. The maximal motor unit discharge rate was associated with the explosive force variables, at the whole population level (r2 =0.71±0.12; P<0.001). Moreover, the peak motor unit discharge and maximal rate of force variables were correlated with an estimate of the supraspinal drive, which was measured as the speed of motor unit recruitment before the generation of afferent feedback (P<0.05). We show for the first time the full association between the effective neural drive to the muscle and human maximal rate of force development. The results obtained in the present study indicate that the variability in the maximal contractile explosive force of the human tibialis anterior muscle is determined by the neural activation preceding force generation.

Inverted Encoding Models Reconstruct an Arbitrary Model Response, Not the Stimulus.

Probing how large populations of neurons represent stimuli is key to understanding sensory representations as many stimulus characteristics can only be discerned from population activity and not from individual single-units. Recently, inverted encoding models have been used to produce channel response functions from large spatial-scale measurements of human brain activity that are reminiscent of single-unit tuning functions and have been proposed to assay “population-level stimulus representations” (Sprague et al., 2018a). However, these channel response functions do not assay population tuning. We show by derivation that the channel response function is only determined up to an invertible linear transform. Thus, these channel response functions are arbitrary, one of an infinite family and therefore not a unique description of population representation. Indeed, simulations demonstrate that bimodal, even random, channel basis functions can account perfectly well for population responses without any underlying neural response units that are so tuned. However, the approach can be salvaged by extending it to reconstruct the stimulus, not the assumed model. We show that when this is done, even using bimodal and random channel basis functions, a unimodal function peaking at the appropriate value of the stimulus is recovered which can be interpreted as a measure of population selectivity. More precisely, the recovered function signifies how likely any value of the stimulus is, given the observed population response. Whether an analysis is recovering the hypothetical responses of an arbitrary model rather than assessing the selectivity of population representations is not an issue unique to the inverted encoding model and human neuroscience, but a general problem that must be confronted as more complex analyses intervene between measurement of population activity and presentation of data.

Sweet and bitter taste stimuli activate VTA projection neurons in the parabrachial nucleus

This study investigated neural projections from the parabrachial nucleus (PBN), a gustatory and visceral processing area in the brainstem, to the ventral tegmental area (VTA) in the midbrain. The VTA contains a large population of dopaminergic neurons that have been shown to play a role in reward processing. Anterograde neural tracing methods were first used to confirm that a robust projection from the caudal PBN terminates in the dorsal VTA; this projection was larger on the contralateral side. In the next experiment, we combined dual retrograde tracing from the VTA and the gustatory ventral posteromedial thalamus (VPMpc) with taste-evoked Fos protein expression, which labels activated neurons. Mice were stimulated through an intraoral cannula with sucrose, quinine, or water, and PBN sections were processed for immunofluorescent detection of Fos and retrograde tracers. The distribution of tracer-labeled PBN neurons demonstrated that the populations of cells projecting to the VTA or VPMpc are largely independent. Quantification of cells double labeled for Fos and either tracer demonstrated that sucrose and quinine were effective in activating both pathways. These results indicate that information about both appetitive and aversive tastes is delivered to a key midbrain reward interface via direct projections from the PBN.

Regional complexity in enteric neuron wiring reflects diversity of motility patterns in the mouse large intestine.

The enteric nervous system controls a variety of gastrointestinal functions including intestinal motility. The minimal neuronal circuit necessary to direct peristalsis is well-characterized but several intestinal regions display also other motility patterns for which the underlying circuits and connectivity schemes that coordinate the transition between those patterns are poorly understood. We investigated whether in regions with a richer palette of motility patterns, the underlying nerve circuits reflect this complexity. Using Ca2+ imaging, we determined the location and response fingerprint of large populations of enteric neurons upon focal network stimulation. Complemented by neuronal tracing and volumetric reconstructions of synaptic contacts, this shows that the multifunctional proximal colon requires specific additional circuit components as compared to the distal colon, where peristalsis is the predominant motility pattern. Our study reveals that motility control is hard-wired in the enteric neural networks and that circuit complexity matches the motor pattern portfolio of specific intestinal regions.

Fast nonconvex deconvolution of calcium imaging data.

Calcium imaging data promises to transform the field of neuroscience by making it possible to record from large populations of neurons simultaneously. However, determining the exact moment in time at which a neuron spikes, from a calcium imaging data set, amounts to a non-trivial deconvolution problem which is of critical importance for downstream analyses. While a number of formulations have been proposed for this task in the recent literature, in this article, we focus on a formulation recently proposed in Jewell and Witten (2018. Exact spike train inference via $\ell_{0} $ optimization. The Annals of Applied Statistics12(4), 2457-2482) that can accurately estimate not just the spike rate, but also the specific times at which the neuron spikes. We develop a much faster algorithm that can be used to deconvolve a fluorescence trace of 100 000 timesteps in less than a second. Furthermore, we present a modification to this algorithm that precludes the possibility of a "negative spike". We demonstrate the performance of this algorithm for spike deconvolution on calcium imaging datasets that were recently released as part of the $\texttt{spikefinder}$ challenge (http://spikefinder.codeneuro.org/). The algorithm presented in this article was used in the Allen Institute for Brain Science's "platform paper" to decode neural activity from the Allen Brain Observatory; this is the main scientific paper in which their data resource is presented. Our $\texttt{C++}$ implementation, along with $\texttt{R}$ and $\texttt{python}$ wrappers, is publicly available. $\texttt{R}$ code is available on $\texttt{CRAN}$ and $\texttt{Github}$, and $\texttt{python}$ wrappers are available on $\texttt{Github}$; see https://github.com/jewellsean/FastLZeroSpikeInference.

Local homogeneity of tonotopic organization in the primary auditory cortex of marmosets

Marmoset has emerged as a useful nonhuman primate species for studying brain structure and function. Previous studies on the mouse primary auditory cortex (A1) showed that neurons with preferential frequency-tuning responses are mixed within local cortical regions, despite a large-scale tonotopic organization. Here we found that frequency-tuning properties of marmoset A1 neurons are highly uniform within local cortical regions. We first defined the tonotopic map of A1 using intrinsic optical imaging and then used in vivo two-photon calcium imaging of large neuronal populations to examine the tonotopic preference at the single-cell level. We found that tuning preferences of layer 2/3 neurons were highly homogeneous over hundreds of micrometers in both horizontal and vertical directions. Thus, marmoset A1 neurons are distributed in a tonotopic manner at both macro- and microscopic levels. Such organization is likely to be important for the organization of auditory circuits in the primate brain.

Multispectral tracing in densely labeled mouse brain with nTracer.

SummaryThis note describes nTracer, an ImageJ plug-in for user-guided, semi-automated tracing of multispectral fluorescent tissue samples. This approach allows for rapid and accurate reconstruction of whole cell morphology of large neuronal populations in densely labeled brains.Availability and implementationnTracer was written as a plug-in for the open source image processing software ImageJ. The software, instructional documentation, tutorial videos, sample image and sample tracing results are available at https://www.cai-lab.org/ntracer-tutorial.Supplementary information Supplementary data are available at Bioinformatics online.

Wave Turbulence and Energy Cascade in the Hippocampus.

Mesoscale cortical activity can be defined as the organization of activity of large neuron populations into collective action, forming time-dependent patterns such as traveling waves. Although collective action may play an important role in the cross-scale integration of brain activity and in the emergence of cognitive behavior, a comprehensive formulation of the laws governing its dynamics is still lacking. Because collective action processes are macroscopic with respect to neuronal activity, these processes cannot be described directly with methods and models developed for the microscale (individual neurons).To identify the characteristic features of mesoscopic dynamics, and to lay the foundations for a theoretical description of mesoscopic activity in the hippocampus, we conduct a comprehensive examination of observational data of hippocampal local field potential (LFP) recordings. We use the strong correlation between rat running-speed and the LFP power to parameterize the energy input into the hippocampus, and show that both the power and non-linearity of collective action (e.g., theta and gamma rhythms) increase with increased speed. Our results show that collective-action dynamics are stochastic (the precise state of a single neuron is irrelevant), weakly non-linear, and weakly dissipative. These are the principles of the theory of weak turbulence. Therefore, we propose weak turbulence a theoretical framework for the description of mesoscopic activity in the hippocampus. The weak turbulence framework provides a complete description of the cross-scale energy exchange (the energy cascade). It uncovers the mechanism governing major features of LFP spectra and bispectra, such as the physical meaning of the exponent α of power-law LFP spectra (e.g., f−α, where f is the frequency), the strengthening of theta-gamma coupling with energy input into the hippocampus, as well as specific phase lags associated with their interaction. Remarkably, the weak turbulence framework is consistent with the theory of self organized criticality, which provides a simple explanation for the existence of the power-law background spectrum. Together with self-organized criticality, weak turbulence could provide a unifying approach to modeling the dynamics of mesoscopic activity.

Circuit interrogation in freely moving animals.

Developments in imaging tools are making it possible to record activity from both large neuronal populations and subcellular components in freely moving animals. Although these developments are enabling relationships between brain activity and complex behaviors to be explored, many challenges need to be overcome before the potential of the freely moving animal can be fully utilized.

Silent Synapse Unsilencing in Hippocampal CA1 Neurons for Associative Fear Memory Storage.

Clarifying learning-induced synaptic plasticity in hippocampal circuits is critical for understanding hippocampal mechanisms of memory acquisition and storage. Many in vitro studies have demonstrated learning-associated plasticity at hippocampal synapses. However, as a neural basis of memory encoding, the nature of synaptic plasticity underlying hippocampal neuronal responses to memorized stimulation remains elusive. Using in vivo whole-cell recording in anaesthetized adult rats and mice, we investigated synaptic activity of hippocampal CA1 pyramidal cells (PCs) in response to a flash of visual stimulation as the conditioned stimulus (CS) in associative fear conditioning. We found that shortly (<3 days) after conditioning, excitatory synaptic responses and spiking responses to the flash CS emerged in a large number (~70%) of CA1 PCs, a neuronal population previously unresponsive to the flash before conditioning. The learning-induced CA1 excitatory responsiveness was further indicated to result from postsynaptic unsilencing at flash-associated silent synapses, with NMDA receptor-gated responses we recently reported in naive animals. Our findings suggest that associative fear learning can induce excitatory responsiveness to the memorized CS in a large population of CA1 neurons, via a process of postsynaptic unsilencing at CA1 silent synapses, which may be critical for hippocampal acquisition and storage of associative memory.

SCALPEL: EXTRACTING NEURONS FROM CALCIUM IMAGING DATA.

In the past few years, new technologies in the field of neuroscience have made it possible to simultaneously image activity in large populations of neurons at cellular resolution in behaving animals. In mid-2016, a huge repository of this so-called "calcium imaging" data was made publicly available. The availability of this large-scale data resource opens the door to a host of scientific questions for which new statistical methods must be developed. In this paper we consider the first step in the analysis of calcium imaging data-namely, identifying the neurons in a calcium imaging video. We propose a dictionary learning approach for this task. First, we perform image segmentation to develop a dictionary containing a huge number of candidate neurons. Next, we refine the dictionary using clustering. Finally, we apply the dictionary to select neurons and estimate their corresponding activity over time, using a sparse group lasso optimization problem. We assess performance on simulated calcium imaging data and apply our proposal to three calcium imaging data sets. Our proposed approach is implemented in the R package scalpel, which is available on CRAN.

Brain-wide Organization of Neuronal Activity and Convergent Sensorimotor Transformations in Larval Zebrafish

Simultaneous recordings of large populations of neurons in behaving animals allow detailed observation of high-dimensional, complex brain activity. However, experimental approaches often focus on singular behavioral paradigms or brain areas. Here, we recorded whole-brain neuronal activity of larval zebrafish presented with a battery of visual stimuli while recording fictive motor output. We identified neurons tuned to each stimulus type and motor output and discovered groups of neurons in the anterior hindbrain that respond to different stimuli eliciting similar behavioral responses. These convergent sensorimotor representations were only weakly correlated to instantaneous motor activity, suggesting that they critically inform, but do not directly generate, behavioral choices. To catalog brain-wide activity beyond explicit sensorimotor processing, we developed an unsupervised clustering technique that organizes neurons into functional groups. These analyses enabled a broad overview of the functional organization of the brain and revealed numerous brain nuclei whose neurons exhibit concerted activity patterns.

Bilingual aphasia: Explanations in population encoding

Abstract The study of bilingual aphasia has particular power to determine the neural basis of language function and can advance the treatment of aphasia. No theory of bilingual language has provided a coherent and comprehensive account for research results. I begin with a precis of a population encoding model of language function that assumes that representations are based upon the patterns of activity of large populations of neurons. I then consider how regularities in specific domains of language function (e.g. in semantics and phonologic sequence knowledge), frequency effects, and age of acquisition effects (strongest in lexical-semantics) might account for observed behavior in polyglots with aphasia. Finally, I review the literature on bilingual aphasia and its treatment to determine how well these hypotheses account for observed behavior. This analysis reveals that the population encoding theory can provide a coherent and granular account for this behavior.

Multimodal sensory information is represented by a combinatorial code in a sensorimotor system.

A ubiquitous feature of the nervous system is the processing of simultaneously arriving sensory inputs from different modalities. Yet, because of the difficulties of monitoring large populations of neurons with the single resolution required to determine their sensory responses, the cellular mechanisms of how populations of neurons encode different sensory modalities often remain enigmatic. We studied multimodal information encoding in a small sensorimotor system of the crustacean stomatogastric nervous system that drives rhythmic motor activity for the processing of food. This system is experimentally advantageous, as it produces a fictive behavioral output in vitro, and distinct sensory modalities can be selectively activated. It has the additional advantage that all sensory information is routed through a hub ganglion, the commissural ganglion, a structure with fewer than 220 neurons. Using optical imaging of a population of commissural neurons to track each individual neuron's response across sensory modalities, we provide evidence that multimodal information is encoded via a combinatorial code of recruited neurons. By selectively stimulating chemosensory and mechanosensory inputs that are functionally important for processing of food, we find that these two modalities were processed in a distributed network comprising the majority of commissural neurons imaged. In a total of 12 commissural ganglia, we show that 98% of all imaged neurons were involved in sensory processing, with the two modalities being processed by a highly overlapping set of neurons. Of these, 80% were multimodal, 18% were unimodal, and only 2% of the neurons did not respond to either modality. Differences between modalities were represented by the identities of the neurons participating in each sensory condition and by differences in response sign (excitation versus inhibition), with 46% changing their responses in the other modality. Consistent with the hypothesis that the commissural network encodes different sensory conditions in the combination of activated neurons, a new combination of excitation and inhibition was found when both pathways were activated simultaneously. The responses to this bimodal condition were distinct from either unimodal condition, and for 30% of the neurons, they were not predictive from the individual unimodal responses. Thus, in a sensorimotor network, different sensory modalities are encoded using a combinatorial code of neurons that are activated or inhibited. This provides motor networks with the ability to differentially respond to categorically different sensory conditions and may serve as a model to understand higher-level processing of multimodal information.

Rich spatio-temporal stimulus dynamics unveil sensory specialization in cortical area S2

Tactile perception in rodents depends on simultaneous, multi-whisker contacts with objects. Although it is known that neurons in secondary somatosensory cortex (wS2) respond to individual deflections of many whiskers, wS2′s precise function remains unknown. The convergence of information from multiple whiskers into wS2 neurons suggests that they are good candidates for integrating multi-whisker information. Here, we apply stimulation patterns with rich dynamics simultaneously to 24 macro-vibrissae of rats while recording large populations of single neurons. Varying inter-whisker correlations without changing single whisker statistics, we observe pronounced supra-linear multi-whisker integration. Using novel analysis methods, we show that continuous multi-whisker movements contribute to the firing of wS2 neurons over long temporal windows, facilitating spatio-temporal integration. In contrast, primary cortex (wS1) neurons encode fine features of whisker movements on precise temporal scales. These results provide the first description of wS2′s representation during multi-whisker stimulation and outline its specialized role in parallel to wS1 tactile processing.

PySight: plug and play photon counting for fast continuous volumetric intravital microscopy

Imaging increasingly large neuronal populations at high rates pushed multi-photon microscopy into the photon-deprived regime. We present PySight, an add-on hardware and software solution tailored for photon-deprived imaging conditions. PySight more than triples the measured median amplitude of neuronal calcium transients in awake mice and facilitates single-trial intravital voltage imaging in fruit flies. Its unique data streaming architecture allowed us to image a fruit fly’s brain olfactory response over 234 μm×600 μm×330 μm at 73 volumes per second, while retaining over 200 times lower data rates than those of a conventional data acquisition system with comparable voxel sizes (1.2 μm×1.2 μm×2.2 μm). PySight requires no electronics expertise or custom synchronization boards, and its open-source software is extensible to any imaging method based on single-pixel (bucket) detectors. PySight offers an optimal data acquisition scheme for ever increasing imaging volumes of turbid living tissue.

Lateralized rhythmic acoustic stimulation during daytime NREM sleep enhances slow waves.

Slow wave sleep (SWS) is characterized by the predominance of delta waves and slow oscillations, reflecting the synchronized activity of large cortical neuronal populations. Amongst other functions, SWS plays a crucial role in the restorative capacity of sleep. Rhythmic acoustic stimulation (RAS) during SWS has been shown a cost-effective method to enhance slow wave activity. Slow wave activity can be expressed in a region-specific manner as a function of previous waking activity. However, it is unclear whether slow waves can be enhanced in a region-specific manner using RAS. We investigated the effects of unilaterally presented rhythmic acoustic sound patterns on sleep electroencephalographic (EEG) oscillations. Thirty-five participants received during SWS 12-second long rhythmic bursts of pink noise (at a rate of 1 Hz) that alternated with non-stimulated, silent periods, unilaterally delivered into one of the ears of the participants. As expected, RAS enhanced delta power, especially in its low-frequency components between 0.75 and 2.25 Hz. However, increased slow oscillatory activity was apparent in both hemispheres regardless of the side of the stimulation. The most robust increases in slow oscillatory activity appeared during the first 3-4 seconds of the stimulation period. Furthermore, a short-lasting increase in theta and sigma power was evidenced immediately after the first pulse of the stimulation sequences. Our findings indicate that lateralized RAS has a strong potential to globally enhance slow waves during daytime naps. The lack of localized effects suggests that slow waves are triggered by the ascending reticular system and not directly by specific auditory pathways.

Calcium Transient Dynamics of Neural Ensembles in the Primary Motor Cortex of Naturally Behaving Monkeys

To understand brain circuits of cognitive behaviors under natural conditions, we developed techniques for imaging neuronal activities from large neuronal populations in the deep layer cortex of the naturally behaving common marmoset. Animals retrieved food pellets or climbed ladders as a miniature fluorescence microscope monitored hundreds of calcium indicator-expressing cortical neurons in the right primary motor cortex. This technique, which can be adapted to other brain regions, can deepen our understanding of brain circuits by facilitating longitudinal population analyses of neuronal representation associated with cognitive naturalistic behaviors and their pathophysiological processes.

Cognition as a Window into Neuronal Population Space.

Understanding how cognitive processes affect the responses of sensory neurons may clarify the relationship between neuronal population activity and behavior. However, tools for analyzing neuronal activity have not kept up with technological advances in recording from large neuronal populations. Here, we describe prevalent hypotheses of how cognitive processes affect sensory neurons, driven largely by a model based on the activity of single neurons or pools of neurons as the units of computation. We then use simple simulations to expand this model to a new conceptual framework that focuses on subspaces of population activity as the relevant units of computation, uses comparisons between brain areas or to behavior to guide analyses of these subspaces, and suggests that population activity is optimized to decode the large variety of stimuli and tasks that animals encounter in natural behavior. This framework provides new ways of understanding the ever-growing quantity of recorded population activity data.