Intensity versus identity coding in an olfactory system

Adaptation of olfactory receptor abundances for efficient coding

Olfactory receptor usage is highly heterogeneous, with some receptor types being orders of magnitude more abundant than others. We propose an explanation for this striking fact: the receptor distribution is tuned to maximally represent information about the olfactory environment in a regime of efficient coding that is sensitive to the global context of correlated sensor responses. This model predicts that in mammals, where olfactory sensory neurons are replaced regularly, receptor abundances should continuously adapt to odor statistics. Experimentally, increased exposure to odorants leads variously, but reproducibly, to increased, decreased, or unchanged abundances of different activated receptors. We demonstrate that this diversity of effects is required for efficient coding when sensors are broadly correlated, and provide an algorithm for predicting which olfactory receptors should increase or decrease in abundance following specific environmental changes. Finally, we give simple dynamical rules for neural birth and death processes that might underlie this adaptation.

Odorant mixtures elicit less variable and faster responses than pure odorants

In natural environments, odors are typically mixtures of several different chemical compounds. However, the implications of mixtures for odor processing have not been fully investigated. We have extended a standard olfactory receptor model to mixtures and found through its mathematical analysis that odorant-evoked activity patterns are more stable across concentrations and first-spike latencies of receptor neurons are shorter for mixtures than for pure odorants. Shorter first-spike latencies arise from the nonlinear dependence of binding rate on odorant concentration, commonly described by the Hill coefficient, while the more stable activity patterns result from the competition between different ligands for receptor sites. These results are consistent with observations from numerical simulations and physiological recordings in the olfactory system of insects. Our results suggest that mixtures allow faster and more reliable olfactory coding, which could be one of the reasons why animals often use mixtures in chemical signaling.

Cortical population activity within a preserved neural manifold underlies multiple motor behaviors

Populations of cortical neurons flexibly perform different functions; for the primary motor cortex (M1) this means a rich repertoire of motor behaviors. We investigate the flexibility of M1 movement control by analyzing neural population activity during a variety of skilled wrist and reach-to-grasp tasks. We compare across tasks the neural modes that capture dominant neural covariance patterns during each task. While each task requires different patterns of muscle and single unit activity, we find unexpected similarities at the neural population level: the structure and activity of the neural modes is largely preserved across tasks. Furthermore, we find two sets of neural modes with task-independent activity that capture, respectively, generic temporal features of the set of tasks and a task-independent mapping onto muscle activity. This system of flexibly combined, well-preserved neural modes may underlie the ability of M1 to learn and generate a wide-ranging behavioral repertoire.

Neuronal Response Latencies Encode First Odor Identity Information across Subjects

Odorants are coded in the primary olfactory processing centers by spatially and temporally distributed patterns of glomerular activity. Whereas the spatial distribution of odorant-induced responses is known to be conserved across individuals, the universality of its temporal structure is still debated. Via fast two-photon calcium imaging, we analyzed the early phase of neuronal responses in the form of the activity onset latencies in the antennal lobe projection neurons of honeybee foragers. We show that each odorant evokes a stimulus-specific response latency pattern across the glomerular coding space. Moreover, we investigate these early response features for the first time across animals, revealing that the order of glomerular firing onsets is conserved across individuals and allows them to reliably predict odorant identity, but not concentration. These results suggest that the neuronal response latencies provide the first available code for fast odor identification.SIGNIFICANCE STATEMENT Here, we studied early temporal coding in the primary olfactory processing centers of the honeybee brain by fast imaging of glomerular responses to different odorants across glomeruli and across individuals. Regarding the elusive role of rapid response dynamics in olfactory coding, we were able to clarify the following aspects: (1) the rank of glomerular activation is conserved across individuals, (2) its stimulus prediction accuracy is equal to that of the response amplitude code, and (3) it contains complementary information. Our findings suggest a substantial role of response latencies in odor identification, anticipating the static response amplitude code.

Dynamic contrast enhancement and flexible odor codes

Sensory stimuli evoke spiking activities patterned across neurons and time that are hypothesized to encode information about their identity. Since the same stimulus can be encountered in a multitude of ways, how stable or flexible are these stimulus-evoked responses? Here we examine this issue in the locust olfactory system. In the antennal lobe, we find that both spatial and temporal features of odor-evoked responses vary in a stimulus-history dependent manner. The response variations are not random, but allow the antennal lobe circuit to enhance the uniqueness of the current stimulus. Nevertheless, information about the odorant identity is conf ounded due to this contrast enhancement computation. Notably, predictions from a linear logical classifier (OR-of-ANDs) that can decode information distributed in flexible subsets of neurons match results from behavioral experiments. In sum, our results suggest that a trade-off between stability and flexibility in sensory coding can be achieved using a simple computational logic.

Sensory stimuli are encountered in multiple ways necessitating a flexible and adaptive neural population code for identification. Here, the authors show that the dynamics of odor coding in the locust antennal lobe varies with stimulus context so as to enhance the target stimulus representation.

Different population dynamics in the supplementary motor area and motor cortex during reaching

Neural populations perform computations through their collective activity. Different computations likely require different population-level dynamics. We leverage this assumption to examine neural responses recorded from the supplementary motor area (SMA) and motor cortex. During visually guided reaching, the respective roles of these areas remain unclear; neurons in both areas exhibit preparation-related activity and complex patterns of movement-related activity. To explore population dynamics, we employ a novel "hypothesis-guided" dimensionality reduction approach. This approach reveals commonalities but also stark differences: linear population dynamics, dominated by rotations, are prominent in motor cortex but largely absent in SMA. In motor cortex, the observed dynamics produce patterns resembling muscle activity. Conversely, the non-rotational patterns in SMA co-vary with cues regarding when movement should be initiated. Thus, while SMA and motor cortex display superficially similar single-neuron responses during visually guided reaching, their different population dynamics indicate they are likely performing quite different computations.

Unsupervised clustering of temporal patterns in high-dimensional neuronal ensembles using a novel dissimilarity measure

Temporally ordered multi-neuron patterns likely encode information in the brain. We introduce an unsupervised method, SPOTDisClust (Spike Pattern Optimal Transport Dissimilarity Clustering), for their detection from high-dimensional neural ensembles. SPOTDisClust measures similarity between two ensemble spike patterns by determining the minimum transport cost of transforming their corresponding normalized cross-correlation matrices into each other (SPOTDis). Then, it performs density-based clustering based on the resulting inter-pattern dissimilarity matrix. SPOTDisClust does not require binning and can detect complex patterns (beyond sequential activation) even when high levels of out-of-pattern "noise" spiking are present. Our method handles efficiently the additional information from increasingly large neuronal ensembles and can detect a number of patterns that far exceeds the number of recorded neurons. In an application to neural ensemble data from macaque monkey V1 cortex, SPOTDisClust can identify different moving stimulus directions on the sole basis of temporal spiking patterns.

Calcium in Kenyon Cell Somata as a Substrate for an Olfactory Sensory Memory in Drosophila

Animals can form associations between temporally separated stimuli. To do so, the nervous system has to retain a neural representation of the first stimulus until the second stimulus appears. The neural substrate of such sensory stimulus memories is unknown. Here, we search for a sensory odor memory in the insect olfactory system and characterize odorant-evoked Ca2+ activity at three consecutive layers of the olfactory system in Drosophila: in olfactory receptor neurons (ORNs) and projection neurons (PNs) in the antennal lobe, and in Kenyon cells (KCs) in the mushroom body. We show that the post-stimulus responses in ORN axons, PN dendrites, PN somata, and KC dendrites are odor-specific, but they are not predictive of the chemical identity of past olfactory stimuli. However, the post-stimulus responses in KC somata carry information about the identity of previous olfactory stimuli. These findings show that the Ca2+ dynamics in KC somata could encode a sensory memory of odorant identity and thus might serve as a basis for associations between temporally separated stimuli.

The Synaptic Properties of Cells Define the Hallmarks of Interval Timing in a Recurrent Neural Network

Extensive research has described two key features of interval timing. The bias property is associated with accuracy and implies that time is overestimated for short intervals and underestimated for long intervals. The scalar property is linked to precision and states that the variability of interval estimates increases as a function of interval duration. The neural mechanisms behind these properties are not well understood. Here we implemented a recurrent neural network that mimics a cortical ensemble and includes cells that show paired-pulse facilitation and slow inhibitory synaptic currents. The network produces interval selective responses and reproduces both bias and scalar properties when a Bayesian decoder reads its activity. Notably, the interval-selectivity, timing accuracy, and precision of the network showed complex changes as a function of the decay time constants of the modeled synaptic properties and the level of background activity of the cells. These findings suggest that physiological values of the time constants for paired-pulse facilitation and GABAb, as well as the internal state of the network, determine the bias and scalar properties of interval timing.SIGNIFICANCE STATEMENT Timing is a fundamental element of complex behavior, including music and language. Temporal processing in a wide variety of contexts shows two primary features: time estimates exhibit a shift toward the mean (the bias property) and are more variable for longer intervals (the scalar property). We implemented a recurrent neural network that includes long-lasting synaptic currents, which cannot only produce interval-selective responses but also follow the bias and scalar properties. Interestingly, only physiological values of the time constants for paired-pulse facilitation and GABAb, as well as intermediate background activity within the network can reproduce the two key features of interval timing.

Differential effects of adaptation on odor discrimination

Adaptation of neural responses is ubiquitous in sensory systems and can potentially facilitate many important computational functions. Here we examined this issue with a well-constrained computational model of the early olfactory circuits. In the insect olfactory system, the responses of olfactory receptor neurons (ORNs) on the antennae adapt over time. We found that strong adaptation of sensory input is important for rapidly detecting a fresher stimulus encountered in the presence of other background cues and for faithfully representing its identity. However, when the overlapping odorants were chemically similar, we found that adaptation could alter the representation of these odorants to emphasize only distinguishing features. This work demonstrates novel roles for peripheral neurons during olfactory processing in complex environments. NEW & NOTEWORTHY Olfactory systems face the problem of distinguishing salient information from a complex olfactory environment. The neural representations of specific odor sources should be consistent regardless of the background. How are olfactory representations robust to varying environmental interference? We show that in locusts the extraction of salient information begins in the periphery. Olfactory receptor neurons adapt in response to odorants. Adaptation can provide a computational mechanism allowing novel odorant components to be highlighted during complex stimuli.

Independent processing of increments and decrements in odorant concentration by ON and OFF olfactory receptor neurons

A salient feature of the insect olfactory system is its ability to detect and interpret simultaneously the identity and concentration of an odorant signal along with the temporal stimulus cues that are essential for accurate odorant tracking. The olfactory system of the cockroach utilizes two parallel pathways for encoding of odorant identity and the moment-to-moment succession of odorant concentrations as well as the rate at which concentration changes. This separation originates at the peripheral level of the ORNs (olfactory receptor neurons) which are localized in basiconic and trichoid sensilla. The graded activity of ORNs in the basiconic sensilla provides the variable for the combinatorial representation of odorant identity. The antagonistically responding ON and OFF ORNs in the trichoid sensilla transmit information about concentration increments and decrements with excitatory signals. Each ON and OFF ORN adjusts its gain for odorant concentration and its rate of change to the temporal dynamics of the odorant signal: as the rate of change diminishes, both ORNs improve their sensitivity for the rate of change at the expense of the sensitivity for the instantaneous concentration. This suggests that the ON and OFF ORNs are optimized to detect minute fluctuations or even creeping changes in odorant concentration.

Dimensionality reduction in neuroscience

The nervous system extracts information from its environment and distributes and processes that information to inform and drive behaviour. In this task, the nervous system faces a type of data analysis problem, for, while a visual scene may be overflowing with information, reaching for the television remote before us requires extraction of only a relatively small fraction of that information. We could care about an almost infinite number of visual stimulus patterns, but we don't: we distinguish two actors' faces with ease but two different images of television static with significant difficulty. Equally, we could respond with an almost infinite number of movements, but we don't: the motions executed to pick up the remote are highly stereotyped and related to many other grasping motions. If we were to look at what was going on inside the brain during this task, we would find populations of neurons whose electrical activity was highly structured and correlated with the images on the screen and the action of localizing and picking up the remote.

Mechanosensory-Based Phase Coding of Odor Identity in the Olfactory Bulb

Mitral and tufted (M/T) cells in the olfactory bulb produce rich temporal patterns of activity in response to different odors. However, it remains unknown how these temporal patterns are generated and how they are utilized in olfaction. Here we show that temporal patterning effectively discriminates between the two sensory modalities detected by olfactory sensory neurons (OSNs): odor and airflow-driven mechanical signals. Sniff-induced mechanosensation generates glomerulus-specific oscillatory activity in M/T cells, whose phase was invariant across airflow speed. In contrast, odor stimulation caused phase shifts (phase coding). We also found that odor-evoked phase shifts are concentration invariant and stable across multiple sniff cycles, contrary to the labile nature of rate coding. The loss of oscillatory mechanosensation impaired the precision and stability of phase coding, demonstrating its role in olfaction. We propose that phase, not rate, coding is a robust encoding strategy of odor identity and is ensured by airflow-induced mechanosensation in OSNs.

EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review

The present review discusses a well-established method for characterizing resting-state activity of the human brain using multichannel electroencephalography (EEG). This method involves the examination of electrical microstates in the brain, which are defined as successive short time periods during which the configuration of the scalp potential field remains semi-stable, suggesting quasi-simultaneity of activity among the nodes of large-scale networks. A few prototypic microstates, which occur in a repetitive sequence across time, can be reliably identified across participants. Researchers have proposed that these microstates represent the basic building blocks of the chain of spontaneous conscious mental processes, and that their occurrence and temporal dynamics determine the quality of mentation. Several studies have further demonstrated that disturbances of mental processes associated with neurological and psychiatric conditions manifest as changes in the temporal dynamics of specific microstates. Combined EEG-fMRI studies and EEG source imaging studies have indicated that EEG microstates are closely associated with resting-state networks as identified using fMRI. The scale-free properties of the time series of EEG microstates explain why similar networks can be observed at such different time scales. The present review will provide an overview of these EEG microstates, available methods for analysis, the functional interpretations of findings regarding these microstates, and their behavioral and clinical correlates.

Rate, not selectivity, determines neuronal population coding accuracy in auditory cortex

The notion that neurons with higher selectivity carry more information about external sensory inputs is widely accepted in neuroscience. High-selectivity neurons respond to a narrow range of sensory inputs, and thus would be considered highly informative by rejecting a large proportion of possible inputs. In auditory cortex, neuronal responses are less selective immediately after the onset of a sound and then become highly selective in the following sustained response epoch. These 2 temporal response epochs have thus been interpreted to encode first the presence and then the content of a sound input. Contrary to predictions from that prevailing theory, however, we found that the neural population conveys similar information about sound input across the 2 epochs in spite of the neuronal selectivity differences. The amount of information encoded turns out to be almost completely dependent upon the total number of population spikes in the read-out window for this system. Moreover, inhomogeneous Poisson spiking behavior is sufficient to account for this property. These results imply a novel principle of sensory encoding that is potentially shared widely among multiple sensory systems.

Structure in neural population recordings: an expected byproduct of simpler phenomena?

Neuroscientists increasingly analyze the joint activity of multi-neuron recordings to identify population-level structure that is believed to be significant and scientifically novel. Claims of significant population structure support hypotheses in many brain areas. However, these claims require first investigating the possibility that the population structure in question is an expected byproduct of simpler features known to exist in data. Classically, this critical examination can be either intuited or addressed with conventional controls. However, these approaches fail when considering population data, raising concerns about the scientific merit of population-level studies. Here we develop a framework to test the novelty of population-level findings against simpler features such as correlations across times, neurons and conditions. We apply this framework to test two recent population findings in prefrontal and motor cortices, providing essential context to those studies. More broadly, the methodologies we introduce provide a general neural population control for many population-level hypotheses.

Neural Manifolds for the Control of Movement

The analysis of neural dynamics in several brain cortices has consistently uncovered low-dimensional manifolds that capture a significant fraction of neural variability. These neural manifolds are spanned by specific patterns of correlated neural activity, the "neural modes." We discuss a model for neural control of movement in which the time-dependent activation of these neural modes is the generator of motor behavior. This manifold-based view of motor cortex may lead to a better understanding of how the brain controls movement.

Measuring the olfactory bulb input-output transformation reveals a contribution to the perception of odorant concentration invariance

Humans and other animals can recognize an odorant as the same over a range of odorant concentrations. It remains unclear whether the olfactory bulb, the brain structure that mediates the first stage of olfactory information processing, participates in generating this perceptual concentration invariance. Olfactory bulb glomeruli are regions of neuropil that contain input and output processes: olfactory receptor neuron nerve terminals (input) and mitral/tufted cell apical dendrites (output). Differences between the input and output of a brain region define the function(s) carried out by that region. Here we compare the activity signals from the input and output across a range of odorant concentrations. The output maps maintain a relatively stable representation of odor identity over the tested concentration range, even though the input maps and signals change markedly. These results provide direct evidence that the mammalian olfactory bulb likely participates in generating the perception of concentration invariance of odor quality.

Humans and animals recognize an odorant across a range of odorant concentrations, but where in the olfactory processing pathway this invariance is generated is unclear. By measuring and comparing olfactory bulb outputs to inputs, the authors show that the olfactory bulb participates in generating the perception of odorant concentration invariance.

Representation of Multidimensional Stimuli: Quantifying the Most Informative Stimulus Dimension from Neural Responses

A common way to assess the function of sensory neurons is to measure the number of spikes produced by individual neurons while systematically varying a given dimension of the stimulus. Such measured tuning curves can then be used to quantify the accuracy of the neural representation of the stimulus dimension under study, which can in turn be related to behavioral performance. However, tuning curves often change shape when other dimensions of the stimulus are varied, reflecting the simultaneous sensitivity of neurons to multiple stimulus features. Here we illustrate how one-dimensional information analyses are misleading in this context, and propose a framework derived from Fisher information that allows the quantification of information carried by neurons in multidimensional stimulus spaces. We use this method to probe the representation of sound localization in auditory neurons of chinchillas and guinea pigs of both sexes, and show how heterogeneous tuning properties contribute to a representation of sound source position that is robust to changes in sound level.SIGNIFICANCE STATEMENT Sensory neurons' responses are typically modulated simultaneously by numerous stimulus properties, which can result in an overestimation of neural acuity with existing one-dimensional neural information transmission measures. To overcome this limitation, we develop new, compact expressions of Fisher information-derived measures that bound the robust encoding of separate stimulus dimensions in the context of multidimensional stimuli. We apply this method to the problem of the representation of sound source location in the face of changes in sound source level by neurons of the auditory midbrain.

New Methods to Study Gustatory Coding

The sense of taste allows animals to detect chemicals in the environment, giving rise to behaviors critical for survival. When Gustatory Receptor Neurons (GRNs) detect tastant molecules, they encode information about the identity and concentration of the tastant as patterns of electrical activity that then propagate to follower neurons in the brain. These patterns constitute internal representations of the tastant, which then allow the animal to select actions and form memories. The use of relatively simple animal models has been a powerful tool to study basic principles in sensory coding. Here, we propose three new methods to study gustatory coding using the moth Manduca sexta. First, we present a dissection procedure for exposing the maxillary nerves and the subesophageal zone (SEZ), allowing recording of the activity of GRNs from their axons. Second, we describe the use of extracellular electrodes to record the activity of multiple GRNs by placing tetrode wires directly into the maxillary nerve. Third, we present a new system for delivering and monitoring, with high temporal precision, pulses of different tastants. These methods allow the characterization of neuronal responses in vivo directly from GRNs before, during and after tastants are delivered. We provide examples of voltage traces recorded from multiple GRNs, and present an example of how a spike sorting technique can be applied to the data to identify the responses of individual neurons. Finally, to validate our recording approach, we compare extracellular recordings obtained from GRNs with tetrodes to intracellular recordings obtained with sharp glass electrodes.

Models of Acetylcholine and Dopamine Signals Differentially Improve Neural Representations

Biological and artificial neural networks (ANNs) represent input signals as patterns of neural activity. In biology, neuromodulators can trigger important reorganizations of these neural representations. For instance, pairing a stimulus with the release of either acetylcholine (ACh) or dopamine (DA) evokes long lasting increases in the responses of neurons to the paired stimulus. The functional roles of ACh and DA in rearranging representations remain largely unknown. Here, we address this question using a Hebbian-learning neural network model. Our aim is both to gain a functional understanding of ACh and DA transmission in shaping biological representations and to explore neuromodulator-inspired learning rules for ANNs. We model the effects of ACh and DA on synaptic plasticity and confirm that stimuli coinciding with greater neuromodulator activation are over represented in the network. We then simulate the physiological release schedules of ACh and DA. We measure the impact of neuromodulator release on the network's representation and on its performance on a classification task. We find that ACh and DA trigger distinct changes in neural representations that both improve performance. The putative ACh signal redistributes neural preferences so that more neurons encode stimulus classes that are challenging for the network. The putative DA signal adapts synaptic weights so that they better match the classes of the task at hand. Our model thus offers a functional explanation for the effects of ACh and DA on cortical representations. Additionally, our learning algorithm yields performances comparable to those of state-of-the-art optimisation methods in multi-layer perceptrons while requiring weaker supervision signals and interacting with synaptically-local weight updates.

An integrated calcium imaging processing toolbox for the analysis of neuronal population dynamics

The development of new imaging and optogenetics techniques to study the dynamics of large neuronal circuits is generating datasets of unprecedented volume and complexity, demanding the development of appropriate analysis tools. We present a comprehensive computational workflow for the analysis of neuronal population calcium dynamics. The toolbox includes newly developed algorithms and interactive tools for image pre-processing and segmentation, estimation of significant single-neuron single-trial signals, mapping event-related neuronal responses, detection of activity-correlated neuronal clusters, exploration of population dynamics, and analysis of clusters' features against surrogate control datasets. The modules are integrated in a modular and versatile processing pipeline, adaptable to different needs. The clustering module is capable of detecting flexible, dynamically activated neuronal assemblies, consistent with the distributed population coding of the brain. We demonstrate the suitability of the toolbox for a variety of calcium imaging datasets. The toolbox open-source code, a step-by-step tutorial and a case study dataset are available at https://github.com/zebrain-lab/Toolbox-Romano-et-al.

Engaging and disengaging recurrent inhibition coincides with sensing and unsensing of a sensory stimulus

Even simple sensory stimuli evoke neural responses that are dynamic and complex. Are the temporally patterned neural activities important for controlling the behavioral output? Here, we investigated this issue. Our results reveal that in the insect antennal lobe, due to circuit interactions, distinct neural ensembles are activated during and immediately following the termination of every odorant. Such non-overlapping response patterns are not observed even when the stimulus intensity or identities were changed. In addition, we find that ON and OFF ensemble neural activities differ in their ability to recruit recurrent inhibition, entrain field-potential oscillations and more importantly in their relevance to behaviour (initiate versus reset conditioned responses). Notably, we find that a strikingly similar strategy is also used for encoding sound onsets and offsets in the marmoset auditory cortex. In sum, our results suggest a general approach where recurrent inhibition is associated with stimulus ‘recognition' and ‘derecognition'.

Sensory stimuli evoke temporally dynamic responses. Here the authors report that responses to odour onset and offset are orthogonally represented in the locust antennal lobe, differentially entrain oscillations, and propose a model in which they are necessary for initiation and termination of behaviour.

Odor identity coding by distributed ensembles of neurons in the mouse olfactory cortex

Olfactory perception and behaviors critically depend on the ability to identify an odor across a wide range of concentrations. Here, we use calcium imaging to determine how odor identity is encoded in olfactory cortex. We find that, despite considerable trial-to-trial variability, odor identity can accurately be decoded from ensembles of co-active neurons that are distributed across piriform cortex without any apparent spatial organization. However, piriform response patterns change substantially over a 100-fold change in odor concentration, apparently degrading the population representation of odor identity. We show that this problem can be resolved by decoding odor identity from a subpopulation of concentration-invariant piriform neurons. These concentration-invariant neurons are overrepresented in piriform cortex but not in olfactory bulb mitral and tufted cells. We therefore propose that distinct perceptual features of odors are encoded in independent subnetworks of neurons in the olfactory cortex.

Non-invasive aerosol delivery and transport of gold nanoparticles to the brain

Targeted delivery of nanoscale carriers containing packaged payloads to the central nervous system has potential use in many diagnostic and therapeutic applications. Moreover, understanding of the bio-interactions of the engineered nanoparticles used for tissue-specific delivery by non-invasive delivery approaches are also of paramount interest. Here, we have examined this issue systematically in a relatively simple invertebrate model using insects. We synthesized 5 nm, positively charged gold nanoparticles (AuNPs) and targeted their delivery using the electrospray aerosol generator. Our results revealed that after the exposure of synthesized aerosol to the insect antenna, AuNPs reached the brain within an hour. Nanoparticle accumulation in the brain increased linearly with the exposure time. Notably, electrophysiological recordings from neurons in the insect brain several hours after exposure did not show any significant alterations in their spontaneous and odor-evoked spiking properties. Taken together, our findings reveal that aerosolized delivery of nanoparticles can be an effective non-invasive approach for delivering nanoparticles to the brain, and also presents an approach to monitor the short-term nano-biointeractions.

Revealing unobserved factors underlying cortical activity with a rectified latent variable model applied to neural population recordings

The activity of sensory cortical neurons is not only driven by external stimuli but also shaped by other sources of input to the cortex. Unlike external stimuli, these other sources of input are challenging to experimentally control, or even observe, and as a result contribute to variability of neural responses to sensory stimuli. However, such sources of input are likely not "noise" and may play an integral role in sensory cortex function. Here we introduce the rectified latent variable model (RLVM) in order to identify these sources of input using simultaneously recorded cortical neuron populations. The RLVM is novel in that it employs nonnegative (rectified) latent variables and is much less restrictive in the mathematical constraints on solutions because of the use of an autoencoder neural network to initialize model parameters. We show that the RLVM outperforms principal component analysis, factor analysis, and independent component analysis, using simulated data across a range of conditions. We then apply this model to two-photon imaging of hundreds of simultaneously recorded neurons in mouse primary somatosensory cortex during a tactile discrimination task. Across many experiments, the RLVM identifies latent variables related to both the tactile stimulation as well as nonstimulus aspects of the behavioral task, with a majority of activity explained by the latter. These results suggest that properly identifying such latent variables is necessary for a full understanding of sensory cortical function and demonstrate novel methods for leveraging large population recordings to this end.NEW & NOTEWORTHY The rapid development of neural recording technologies presents new opportunities for understanding patterns of activity across neural populations. Here we show how a latent variable model with appropriate nonlinear form can be used to identify sources of input to a neural population and infer their time courses. Furthermore, we demonstrate how these sources are related to behavioral contexts outside of direct experimental control.

Context- and Output Layer-Dependent Long-Term Ensemble Plasticity in a Sensory Circuit

Sensory information is translated into ensemble representations by various populations of projection neurons in brain circuits. The dynamics of ensemble representations formed by distinct channels of output neurons in diverse behavioral contexts remains largely unknown. We studied the two output neuron layers in the olfactory bulb (OB), mitral and tufted cells, using chronic two-photon calcium imaging in awake mice. Both output populations displayed similar odor response profiles. During passive sensory experience, both populations showed reorganization of ensemble odor representations yet stable pattern separation across days. Intriguingly, during active odor discrimination learning, mitral but not tufted cells exhibited improved pattern separation, although both populations showed reorganization of ensemble representations. An olfactory circuitry model suggests that cortical feedback on OB interneurons can trigger both forms of plasticity. In conclusion, we show that different OB output layers display unique context-dependent long-term ensemble plasticity, allowing parallel transfer of non-redundant sensory information to downstream centers.

Video Abstract

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Mitral and tufted cells in the olfactory bulb show similar odor-evoked responses

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Passive odor experience reorganizes ensemble odor representations in both cell types

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Associative odor learning specifically improves pattern separation in mitral cells

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Cortical feedback can trigger both forms of plasticity in a network model

Oscillatory integration windows in neurons

Oscillatory synchrony among neurons occurs in many species and brain areas, and has been proposed to help neural circuits process information. One hypothesis states that oscillatory input creates cyclic integration windows: specific times in each oscillatory cycle when postsynaptic neurons become especially responsive to inputs. With paired local field potential (LFP) and intracellular recordings and controlled stimulus manipulations we directly test this idea in the locust olfactory system. We find that inputs arriving in Kenyon cells (KCs) sum most effectively in a preferred window of the oscillation cycle. With a computational model, we show that the non-uniform structure of noise in the membrane potential helps mediate this process. Further experiments performed in vivo demonstrate that integration windows can form in the absence of inhibition and at a broad range of oscillation frequencies. Our results reveal how a fundamental coincidence-detection mechanism in a neural circuit functions to decode temporally organized spiking.

Dense encoding of natural odorants by ensembles of sparsely activated neurons in the olfactory bulb

Sensory information undergoes substantial transformation along sensory pathways, usually encompassing sparsening of activity. In the olfactory bulb, though natural odorants evoke dense glomerular input maps, mitral and tufted (M/T) cells tuning is considered to be sparse because of highly odor-specific firing rate change. However, experiments used to draw this conclusion were either based on recordings performed in anesthetized preparations or used monomolecular odorants presented at arbitrary concentrations. In this study, we evaluated the lifetime and population sparseness evoked by natural odorants by capturing spike temporal patterning of neuronal assemblies instead of individual M/T tonic activity. Using functional imaging and tetrode recordings in awake mice, we show that natural odorants at their native concentrations are encoded by broad assemblies of M/T cells. While reducing odorant concentrations, we observed a reduced number of activated glomeruli representations and consequently a narrowing of M/T tuning curves. We conclude that natural odorants at their native concentrations recruit M/T cells with phasic rather than tonic activity. When encoding odorants in assemblies, M/T cells carry information about a vast number of odorants (lifetime sparseness). In addition, each natural odorant activates a broad M/T cell assembly (population sparseness).

Spatially resolved time-frequency analysis of odour coding in the insect antennal lobe

Antennal lobes constitute the first neurophils in the insect brain involved in coding and processing of olfactory information. With their stereotyped functional and anatomical organization, they provide an accessible model with which to investigate information processing of an external stimulus in a neural network in vivo. Here, by combining functional calcium imaging with time-frequency analysis, we have been able to monitor the oscillatory components of neural activity upon olfactory stimulation. The aim of this study is to investigate the presence of stimulus-induced oscillatory patterns in the honeybee antennal lobe, and to analyse the distribution of those patterns across the antennal lobe glomeruli. Fast two-photon calcium imaging reveals the presence of low-frequency oscillations, the intensity of which is perturbed by an incoming stimulus. Moreover, analysis of the spatial arrangement of this activity indicates that it is not homogeneous throughout the antennal lobe. On the contrary, each glomerulus displays an odorant-specific time-frequency profile, and acts as a functional unit of the oscillatory activity. The presented approach allows simultaneous recording of complex activity patterns across several nodes of the antennal lobe, providing the means to better understand the network dynamics regulating olfactory coding and leading to perception.

Imprinting and recalling cortical ensembles

Neuronal ensembles are coactive groups of neurons that may represent building blocks of cortical circuits. These ensembles could be formed by Hebbian plasticity, whereby synapses between coactive neurons are strengthened. Here we report that repetitive activation with two-photon optogenetics of neuronal populations from ensembles in the visual cortex of awake mice builds neuronal ensembles that recur spontaneously after being imprinted and do not disrupt preexisting ones. Moreover, imprinted ensembles can be recalled by single- cell stimulation and remain coactive on consecutive days. Our results demonstrate the persistent reconfiguration of cortical circuits by two-photon optogenetics into neuronal ensembles that can perform pattern completion.

Clustering of neural code words revealed by a first-order phase transition

A network of neurons in the central nervous system collectively represents information by its spiking activity states. Typically observed states, i.e., code words, occupy only a limited portion of the state space due to constraints imposed by network interactions. Geometrical organization of code words in the state space, critical for neural information processing, is poorly understood due to its high dimensionality. Here, we explore the organization of neural code words using retinal data by computing the entropy of code words as a function of Hamming distance from a particular reference codeword. Specifically, we report that the retinal code words in the state space are divided into multiple distinct clusters separated by entropy-gaps, and that this structure is shared with well-known associative memory networks in a recallable phase. Our analysis also elucidates a special nature of the all-silent state. The all-silent state is surrounded by the densest cluster of code words and located within a reachable distance from most code words. This code-word space structure quantitatively predicts typical deviation of a state-trajectory from its initial state. Altogether, our findings reveal a non-trivial heterogeneous structure of the code-word space that shapes information representation in a biological network.

Learning about natural variation of odor mixtures enhances categorization in early olfactory processing

Natural odors are typically mixtures of several chemical components. Mixtures vary in composition among odor objects that have the same meaning. Therefore a central 'categorization' problem for an animal as it makes decisions about odors in natural contexts is to correctly identify odor variants that have the same meaning and avoid variants that have a different meaning. We propose that identified mechanisms of associative and non-associative plasticity in early sensory processing in the insect antennal lobe and mammalian olfactory bulb are central to solving this problem. Accordingly, this plasticity should work to improve categorization of odors that have the opposite meanings in relation to important events. Using synthetic mixtures designed to mimic natural odor variation among flowers, we studied how honey bees learn about and generalize among floral odors associated with food. We behaviorally conditioned honey bees on a difficult odor discrimination problem using synthetic mixtures that mimic natural variation among snapdragon flowers. We then used calcium imaging to measure responses of projection neurons of the antennal lobe, which is the first synaptic relay of olfactory sensory information in the brain, to study how ensembles of projection neurons change as a result of behavioral conditioning. We show how these ensembles become 'tuned' through plasticity to improve categorization of odors that have the different meanings. We argue that this tuning allows more efficient use of the immense coding space of the antennal lobe and olfactory bulb to solve the categorization problem. Our data point to the need for a better understanding of the 'statistics' of the odor space.

Neural correlations enable invariant coding and perception of natural stimuli in weakly electric fish

Neural representations of behaviorally relevant stimulus features displaying invariance with respect to different contexts are essential for perception. However, the mechanisms mediating their emergence and subsequent refinement remain poorly understood in general. Here, we demonstrate that correlated neural activity allows for the emergence of an invariant representation of natural communication stimuli that is further refined across successive stages of processing in the weakly electric fish Apteronotus leptorhynchus. Importantly, different patterns of input resulting from the same natural communication stimulus occurring in different contexts all gave rise to similar behavioral responses. Our results thus reveal how a generic neural circuit performs an elegant computation that mediates the emergence and refinement of an invariant neural representation of natural stimuli that most likely constitutes a neural correlate of perception.

DOI:http://dx.doi.org/10.7554/eLife.12993.001

We can effortlessly recognize an object – a car, for example – in many different contexts such as when seen from behind, under different lighting levels or even from different viewpoints. This phenomenon is known as perceptual invariance: objects are correctly recognized, despite variations in exactly what is seen (or otherwise sensed). However, it is still not clear how the brain processes perceptual information to recognize the same object under a wide variety of contexts.

Some fish, such as the brown ghost knifefish, produce a weak electric signal that they can alter to communicate with other members of their species. A communication call may be produced in a variety of contexts that alter which aspects of the signal nearby fish detect. Despite this, fish tend to respond to a given communication call in the same way regardless of its context; this suggests that these fish also have perceptual invariance.

The communication calls of weakly electric fish can be easily mimicked in a laboratory and produce reliable behavioral responses, which makes these fish a good model for understanding how perceptual invariance might be coded in the brain. Therefore, Metzen et al. recorded the activity of the receptor neurons that first respond to communication calls in weakly electric fish. The results revealed that a given communication signal made the firing patterns of all receptor neurons in the fish’s brain more similar to each other, regardless of the signal’s context. This occurs despite the changes in context causing single receptor neurons to respond in different ways. At each stage of the process by which information is transmitted from the receptor neurons to neurons deeper in the brain, the similarity in the neurons’ firing patterns is refined, thereby giving rise to perceptual invariance.

While perceptual invariance to a given object in different contexts is desirable, it is also important to be able to distinguish between different objects. This implies that neurons should respond similarly to stimuli associated with the same object and differently to stimuli associated with different objects. Further studies are now needed to confirm whether this is the case.

DOI:http://dx.doi.org/10.7554/eLife.12993.002

Robust neuronal dynamics in premotor cortex during motor planning

Neural activity maintains representations that bridge past and future events, often over many seconds. Network models can produce persistent and ramping activity, but the positive feedback that is critical for these slow dynamics can cause sensitivity to perturbations. Here we use electrophysiology and optogenetic perturbations in the mouse premotor cortex to probe the robustness of persistent neural representations during motor planning. We show that preparatory activity is remarkably robust to large-scale unilateral silencing: detailed neural dynamics that drive specific future movements were quickly and selectively restored by the network. Selectivity did not recover after bilateral silencing of the premotor cortex. Perturbations to one hemisphere are thus corrected by information from the other hemisphere. Corpus callosum bisections demonstrated that premotor cortex hemispheres can maintain preparatory activity independently. Redundancy across selectively coupled modules, as we observed in the premotor cortex, is a hallmark of robust control systems. Network models incorporating these principles show robustness that is consistent with data.

Classification of odorants across layers in locust olfactory pathway

Olfactory processing takes place across multiple layers of neurons from the transduction of odorants in the periphery, to odor quality processing, learning, and decision making in higher olfactory structures. In insects, projection neurons (PNs) in the antennal lobe send odor information to the Kenyon cells (KCs) of the mushroom bodies and lateral horn neurons (LHNs). To examine the odor information content in different structures of the insect brain, antennal lobe, mushroom bodies and lateral horn, we designed a model of the olfactory network based on electrophysiological recordings made in vivo in the locust. We found that populations of all types (PNs, LHNs, and KCs) had lower odor classification error rates than individual cells of any given type. This improvement was quantitatively different from that observed using uniform populations of identical neurons compared with spatially structured population of neurons tuned to different odor features. This result, therefore, reflects an emergent network property. Odor classification improved with increasing stimulus duration: for similar odorants, KC and LHN ensembles reached optimal discrimination within the first 300-500 ms of the odor response. Performance improvement with time was much greater for a population of cells than for individual neurons. We conclude that, for PNs, LHNs, and KCs, ensemble responses are always much more informative than single-cell responses, despite the accumulation of noise along with odor information.

Properties and physiological function of Ca2+-dependent K+ currents in uniglomerular olfactory projection neurons

Ca(2+)-activated potassium currents [IK(Ca)] are an important link between the intracellular signaling system and the membrane potential, which shapes intrinsic electrophysiological properties. To better understand the ionic mechanisms that mediate intrinsic firing properties of olfactory uniglomerular projection neurons (uPNs), we used whole cell patch-clamp recordings in an intact adult brain preparation of the male cockroach Periplaneta americana to analyze IK(Ca) In the insect brain, uPNs form the principal pathway from the antennal lobe to the protocerebrum, where centers for multimodal sensory processing and learning are located. In uPNs the activation of IK(Ca) was clearly voltage and Ca(2+) dependent. Thus under physiological conditions IK(Ca) is strongly dependent on Ca(2+) influx kinetics and on the membrane potential. The biophysical characterization suggests that IK(Ca) is generated by big-conductance (BK) channels. A small-conductance (SK) channel-generated current could not be detected. IK(Ca) was sensitive to charybdotoxin (CTX) and iberiotoxin (IbTX) but not to apamin. The functional role of IK(Ca) was analyzed in occlusion experiments under current clamp, in which portions of IK(Ca) were blocked by CTX or IbTX. Blockade of IK(Ca) showed that IK(Ca) contributes significantly to intrinsic electrophysiological properties such as the action potential waveform and membrane excitability.

Space Takes Time: Concentration Dependent Output Codes from Primary Olfactory Networks Rapidly Provide Additional Information at Defined Discrimination Thresholds

As odor concentration increases, primary olfactory network representations expand in spatial distribution, temporal complexity and duration. However, the direct relationship between concentration dependent odor representations and the psychophysical thresholds of detection and discrimination is poorly understood. This relationship is absolutely critical as thresholds signify transition points whereby representations become meaningful to the organism. Here, we matched stimulus protocols for psychophysical assays and intracellular recordings of antennal lobe (AL) projection neurons (PNs) in the moth Manduca sexta to directly compare psychophysical thresholds and the output representations they elicit. We first behaviorally identified odor detection and discrimination thresholds across an odor dilution series for a panel of structurally similar odors. We then characterized spatiotemporal spiking patterns across a population of individually filled and identified AL PNs in response to those odors at concentrations below, at, and above identified thresholds. Using spatial and spatiotemporal based analyses we observed that each stimulus produced unique representations, even at sub-threshold concentrations. Mean response latency did not decrease and the percent glomerular activation did not increase with concentration until undiluted odor. Furthermore, correlations between spatial patterns for odor decreased, but only significantly with undiluted odor. Using time-integrated Euclidean distance (ED) measures, we determined that added spatiotemporal information was present at the discrimination but not detection threshold. This added information was evidenced by an increase in integrated distance between the sub-detection and discrimination threshold concentrations (of the same odor) that was not present in comparison of the sub-detection and detection threshold. After consideration of delays for information to reach the AL we find that it takes ~120-140 ms for the AL to output identity information. Overall, these results demonstrate that as odor concentration increases, added information about odor identity is embedded in the spatiotemporal representation at the discrimination threshold.

Cortical Correlates of Low-Level Perception: From Neural Circuits to Percepts

Low-level perception results from neural-based computations, which build a multimodal skeleton of unconscious or self-generated inferences on our environment. This review identifies bottleneck issues concerning the role of early primary sensory cortical areas, mostly in rodent and higher mammals (cats and non-human primates), where perception substrates can be searched at multiple scales of neural integration. We discuss the limitation of purely bottom-up approaches for providing realistic models of early sensory processing and the need for identification of fast adaptive processes, operating within the time of a percept. Future progresses will depend on the careful use of comparative neuroscience (guiding the choices of experimental models and species adapted to the questions under study), on the definition of agreed-upon benchmarks for sensory stimulation, on the simultaneous acquisition of neural data at multiple spatio-temporal scales, and on the in vivo identification of key generic integration and plasticity algorithms validated experimentally and in simulations.

Neural Coding of Perceived Odor Intensity

Stimulus intensity is a fundamental perceptual feature in all sensory systems. In olfaction, perceived odor intensity depends on at least two variables: odor concentration; and duration of the odor exposure or adaptation. To examine how neural activity at early stages of the olfactory system represents features relevant to intensity perception, we studied the responses of mitral/tufted cells (MTCs) while manipulating odor concentration and exposure duration. Temporal profiles of MTC responses to odors changed both as a function of concentration and with adaptation. However, despite the complexity of these responses, adaptation and concentration dependencies behaved similarly. These similarities were visualized by principal component analysis of average population responses and were quantified by discriminant analysis in a trial-by-trial manner. The qualitative functional dependencies of neuronal responses paralleled psychophysics results in humans. We suggest that temporal patterns of MTC responses in the olfactory bulb contribute to an internal perceptual variable: odor intensity.

Imaging the neural basis of locomotion

To investigate the fundamental question of how nervous systems encode, organize, and sequence behaviors, Kato et al. imaged neural activity with cellular resolution across the brain of the worm Caenorhabditis elegans. Locomotion behavior seems to be continuously represented by cyclical patterns of distributed neural activity that are present even in immobilized animals.

Low-dimensional attractor for neural activity from local field potentials in optogenetic mice

We used optogenetic mice to investigate possible nonlinear responses of the medial prefrontal cortex (mPFC) local network to light stimuli delivered by a 473 nm laser through a fiber optics. Every 2 s, a brief 10 ms light pulse was applied and the local field potentials (LFPs) were recorded with a 10 kHz sampling rate. The experiment was repeated 100 times and we only retained and analyzed data from six animals that showed stable and repeatable response to optical stimulations. The presence of nonlinearity in our data was checked using the null hypothesis that the data were linearly correlated in the temporal domain, but were random otherwise. For each trail, 100 surrogate data sets were generated and both time reversal asymmetry and false nearest neighbor (FNN) were used as discriminating statistics for the null hypothesis. We found that nonlinearity is present in all LFP data. The first 0.5 s of each 2 s LFP recording were dominated by the transient response of the networks. For each trial, we used the last 1.5 s of steady activity to measure the phase resetting induced by the brief 10 ms light stimulus. After correcting the LFPs for the effect of phase resetting, additional preprocessing was carried out using dendrograms to identify “similar” groups among LFP trials. We found that the steady dynamics of mPFC in response to light stimuli could be reconstructed in a three-dimensional phase space with topologically similar “8”-shaped attractors across different animals. Our results also open the possibility of designing a low-dimensional model for optical stimulation of the mPFC local network.

Feed-Forward versus Feedback Inhibition in a Basic Olfactory Circuit

Inhibitory interneurons play critical roles in shaping the firing patterns of principal neurons in many brain systems. Despite difference in the anatomy or functions of neuronal circuits containing inhibition, two basic motifs repeatedly emerge: feed-forward and feedback. In the locust, it was proposed that a subset of lateral horn interneurons (LHNs), provide feed-forward inhibition onto Kenyon cells (KCs) to maintain their sparse firing—a property critical for olfactory learning and memory. But recently it was established that a single inhibitory cell, the giant GABAergic neuron (GGN), is the main and perhaps sole source of inhibition in the mushroom body, and that inhibition from this cell is mediated by a feedback (FB) loop including KCs and the GGN. To clarify basic differences in the effects of feedback vs. feed-forward inhibition in circuit dynamics we here use a model of the locust olfactory system. We found both inhibitory motifs were able to maintain sparse KCs responses and provide optimal odor discrimination. However, we further found that only FB inhibition could create a phase response consistent with data recorded in vivo. These findings describe general rules for feed-forward versus feedback inhibition and suggest GGN is potentially capable of providing the primary source of inhibition to the KCs. A better understanding of how inhibitory motifs impact post-synaptic neuronal activity could be used to reveal unknown inhibitory structures within biological networks.

Understanding how inhibitory neurons interact with excitatory neurons is critical for understanding the behaviors of neuronal networks. Here we address this question with simple but biologically relevant models based on the anatomy of the locust olfactory pathway. Two ubiquitous and basic inhibitory motifs were tested: feed-forward and feedback. Feed-forward inhibition typically occurs between different brain areas when excitatory neurons excite inhibitory cells, which then inhibit a group of postsynaptic excitatory neurons outside of the initializing excitatory neurons’ area. On the other hand, the feedback inhibitory motif requires a population of excitatory neurons to drive the inhibitory cells, which in turn inhibit the same population of excitatory cells. We found the type of the inhibitory motif determined the timing with which each group of cells fired action potentials in comparison to one another (relative timing). It also affected the range of inhibitory neurons’ activity, with the inhibitory neurons having a wider range in the feedback circuit than that in the feed-forward one. These results will allow predicting the type of the connectivity structure within unexplored biological circuits given only electrophysiological recordings.

Modelling Odor Decoding in the Antennal Lobe by Combining Sequential Firing Rate Models with Bayesian Inference

The olfactory information that is received by the insect brain is encoded in the form of spatiotemporal patterns in the projection neurons of the antennal lobe. These dense and overlapping patterns are transformed into a sparse code in Kenyon cells in the mushroom body. Although it is clear that this sparse code is the basis for rapid categorization of odors, it is yet unclear how the sparse code in Kenyon cells is computed and what information it represents. Here we show that this computation can be modeled by sequential firing rate patterns using Lotka-Volterra equations and Bayesian online inference. This new model can be understood as an ‘intelligent coincidence detector’, which robustly and dynamically encodes the presence of specific odor features. We found that the model is able to qualitatively reproduce experimentally observed activity in both the projection neurons and the Kenyon cells. In particular, the model explains mechanistically how sparse activity in the Kenyon cells arises from the dense code in the projection neurons. The odor classification performance of the model proved to be robust against noise and time jitter in the observed input sequences. As in recent experimental results, we found that recognition of an odor happened very early during stimulus presentation in the model. Critically, by using the model, we found surprising but simple computational explanations for several experimental phenomena.

Odor recognition in the insect brain is amazingly fast but still not fully understood. It is known that recognition is performed in three stages. In the first stage, the sensors respond to an odor by displaying a reproducible neuronal pattern. This code is turned, in the second and third stages, into a sparse code, that is, only relatively few neurons activate over hundreds of milliseconds. It is generally assumed that the insect brain uses this temporal code to recognize an odor. We propose a new model of how this temporal code emerges using sequential activation of groups of neurons. We show that these sequential activations underlie a fast and accurate recognition which is highly robust against neuronal or sensory noise. This model replicates several key experimental findings and explains how the insect brain achieves both speed and robustness of odor recognition as observed in experiments.

Neural Encoding of Odors during Active Sampling and in Turbulent Plumes

Sensory inputs are often fluctuating and intermittent, yet animals reliably utilize them to direct behavior. Here we ask how natural stimulus fluctuations influence the dynamic neural encoding of odors. Using the locust olfactory system, we isolated two main causes of odor intermittency: chaotic odor plumes and active sampling behaviors. Despite their irregularity, chaotic odor plumes still drove dynamic neural response features including the synchronization, temporal patterning, and short-term plasticity of spiking in projection neurons, enabling classifier-based stimulus identification and activating downstream decoders (Kenyon cells). Locusts can also impose odor intermittency through active sampling movements with their unrestrained antennae. Odors triggered immediate, spatially targeted antennal scanning that, paradoxically, weakened individual neural responses. However, these frequent but weaker responses were highly informative about stimulus location. Thus, not only are odor-elicited dynamic neural responses compatible with natural stimulus fluctuations and important for stimulus identification, but locusts actively increase intermittency, possibly to improve stimulus localization.

Dynamic trajectory of multiple single-unit activity during working memory task in rats

Working memory plays an important role in complex cognitive tasks. A popular theoretical view is that transient properties of neuronal dynamics underlie cognitive processing. The question raised here as to how the transient dynamics evolve in working memory. To address this issue, we investigated the multiple single-unit activity dynamics in rat medial prefrontal cortex (mPFC) during a Y-maze working memory task. The approach worked by reconstructing state space from delays of the original single-unit firing rate variables, which were further analyzed using kernel principal component analysis (KPCA). Then the neural trajectories were obtained to visualize the multiple single-unit activity. Furthermore, the maximal Lyapunov exponent (MLE) was calculated to quantitatively evaluate the neural trajectories during the working memory task. The results showed that the neuronal activity produced stable and reproducible neural trajectories in the correct trials while showed irregular trajectories in the incorrect trials, which may establish a link between the neurocognitive process and behavioral performance in working memory. The MLEs significantly increased during working memory in the correctly performed trials, indicating an increased divergence of the neural trajectories. In the incorrect trials, the MLEs were nearly zero and remained unchanged during the task. Taken together, the trial-specific neural trajectory provides an effective way to track the instantaneous state of the neuronal population during the working memory task and offers valuable insights into working memory function. The MLE describes the changes of neural dynamics in working memory and may reflect different neuronal population states in working memory.

An Interglomerular Circuit Gates Glomerular Output and Implements Gain Control in the Mouse Olfactory Bulb

Odors elicit distributed activation of glomeruli in the olfactory bulb (OB). Crosstalk between co-active glomeruli has been proposed to perform a variety of computations, facilitating efficient extraction of sensory information by the cortex. Dopaminergic/GABAergic cells in the OB, which can be identified by their expression of the dopamine transporter (DAT), provide the earliest opportunity for such crosstalk. Here we show in mice that DAT+ cells carry concentration-dependent odor signals and broadcast focal glomerular inputs throughout the OB to cause suppression of mitral/tufted (M/T) cell firing, an effect that is mediated by the external tufted (ET) cells coupled to DAT+ cells via chemical and electrical synapses. We find that DAT+ cells implement gain control and decorrelate odor representations in the M/T cell population. Our results further indicate that ET cells are gatekeepers of glomerular output and prime determinants of M/T responsiveness.