Oscillations and sparsening of odor representations in the mushroom body

A neural network model for familiarity and context learning during honeybee foraging flights

How complex is the memory structure that honeybees use to navigate? Recently, an insect-inspired parsimonious spiking neural network model was proposed that enabled simulated ground-moving agents to follow learned routes. We adapted this model to flying insects and evaluate the route following performance in three different worlds with gradually decreasing object density. In addition, we propose an extension to the model to enable the model to associate sensory input with a behavioral context, such as foraging or homing. The spiking neural network model makes use of a sparse stimulus representation in the mushroom body and reward-based synaptic plasticity at its output synapses. In our experiments, simulated bees were able to navigate correctly even when panoramic cues were missing. The context extension we propose enabled agents to successfully discriminate partly overlapping routes. The structure of the visual environment, however, crucially determines the success rate. We find that the model fails more often in visually rich environments due to the overlap of features represented by the Kenyon cell layer. Reducing the landmark density improves the agents route following performance. In very sparse environments, we find that extended landmarks, such as roads or field edges, may help the agent stay on its route, but often act as strong distractors yielding poor route following performance. We conclude that the presented model is valid for simple route following tasks and may represent one component of insect navigation. Additional components might still be necessary for guidance and action selection while navigating along different memorized routes in complex natural environments.

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.

Elucidating the Neuronal Architecture of Olfactory Glomeruli in the Drosophila Antennal Lobe

Olfactory glomeruli are morphologically conserved spherical compartments of the olfactory system, distinguishable solely by their chemosensory repertoire, anatomical position, and volume. Little is known, however, about their numerical neuronal composition. We therefore characterized their neuronal architecture and correlated these anatomical features with their functional properties in Drosophila melanogaster. We quantitatively mapped all olfactory sensory neurons (OSNs) innervating each glomerulus, including sexually dimorphic distributions. Our data reveal the impact of OSN number on glomerular dimensions and demonstrate yet unknown sex-specific differences in several glomeruli. Moreover, we quantified uniglomerular projection neurons for each glomerulus, which unraveled a glomerulus-specific numerical innervation. Correlation between morphological features and functional specificity showed that glomeruli innervated by narrowly tuned OSNs seem to possess a larger number of projection neurons and are involved in less lateral processing than glomeruli targeted by broadly tuned OSNs. Our study demonstrates that the neuronal architecture of each glomerulus encoding crucial odors is unique.

Spiking and Excitatory/Inhibitory Input Dynamics of Barrel Cells in Response to Whisker Deflections of Varying Velocity and Angular Direction

The spiking of barrel regular-spiking (RS) cells is tuned for both whisker deflection direction and velocity. Velocity tuning arises due to thalamocortical (TC) synchrony (but not spike quantity) varying with deflection velocity, coupled with feedforward inhibition, while direction selectivity is not fully understood, though may be due partly to direction tuning of TC spiking. Data show that as deflection direction deviates from the preferred direction of an RS cell, excitatory input to the RS cell diminishes minimally, but temporally shifts to coincide with the time-lagged inhibitory input. This work constructs a realistic large-scale model of a barrel; model RS cells exhibit velocity and direction selectivity due to TC input dynamics, with the experimentally observed sharpening of direction tuning with decreasing velocity. The model puts forth the novel proposal that RS→RS synapses can naturally and simply account for the unexplained direction dependence of RS cell inputs - as deflection direction deviates from the preferred direction of an RS cell, and TC input declines, RS→RS synaptic transmission buffers the decline in total excitatory input and causes a shift in timing of the excitatory input peak from the peak in TC input to the delayed peak in RS input. The model also provides several experimentally testable predictions on the velocity dependence of RS cell inputs. This model is the first, to my knowledge, to study the interaction of direction and velocity and propose physiological mechanisms for the stimulus dependence in the timing and amplitude of RS cell inputs.

Increased complexity of mushroom body Kenyon cell subtypes in the brain is associated with behavioral evolution in hymenopteran insects

In insect brains, the mushroom bodies (MBs) are a higher-order center for sensory integration and memory. Honeybee (Apis mellifera L.) MBs comprise four Kenyon cell (KC) subtypes: class I large-, middle-, and small-type, and class II KCs, which are distinguished by the size and location of somata, and gene expression profiles. Although these subtypes have only been reported in the honeybee, the time of their acquisition during evolution remains unknown. Here we performed in situ hybridization of tachykinin-related peptide, which is differentially expressed among KC subtypes in the honeybee MBs, in four hymenopteran species to analyze whether the complexity of KC subtypes is associated with their behavioral traits. Three class I KC subtypes were detected in the MBs of the eusocial hornet Vespa mandarinia and the nidificating scoliid wasp Campsomeris prismatica, like in A. mellifera, whereas only two class I KC subtypes were detected in the parasitic wasp Ascogaster reticulata. In contrast, we were unable to detect class I KC subtype in the primitive and phytophagous sawfly Arge similis. Our findings suggest that the number of class I KC subtypes increased at least twice - first with the evolution of the parasitic lifestyle and then with the evolution of nidification.

An insect-like mushroom body in a crustacean brain

Mushroom bodies are the iconic learning and memory centers of insects. No previously described crustacean possesses a mushroom body as defined by strict morphological criteria although crustacean centers called hemiellipsoid bodies, which serve functions in sensory integration, have been viewed as evolutionarily convergent with mushroom bodies. Here, using key identifiers to characterize neural arrangements, we demonstrate insect-like mushroom bodies in stomatopod crustaceans (mantis shrimps). More than any other crustacean taxon, mantis shrimps display sophisticated behaviors relating to predation, spatial memory, and visual recognition comparable to those of insects. However, neuroanatomy-based cladistics suggesting close phylogenetic proximity of insects and stomatopod crustaceans conflicts with genomic evidence showing hexapods closely related to simple crustaceans called remipedes. We discuss whether corresponding anatomical phenotypes described here reflect the cerebral morphology of a common ancestor of Pancrustacea or an extraordinary example of convergent evolution.

With more than four million species, arthropods are the largest and most diverse group of animals on the planet and include, for example, crustaceans, insects and spiders. They are defined by their segmented bodies, hard outer skeletons and jointed limbs. All arthropods share a common ancestor that lived more than 550 million years ago. Exactly how this ancestral arthropod gave rise to the myriad species that exist today is unclear but we know that at some point the arthropod family tree split into branches, one of which went on to become the crustaceans. The crustacean branch then split again, giving rise to a line of descendants that would become the insects.

But although insects evolved from crustaceans, the brains of insects possess structures that those of crustaceans do not. Known as mushroom bodies, these structures help to form and store memories. Their absence in crustaceans has therefore been an enduring mystery. Wolff et al. now add a piece to the puzzle by showing that one group of modern-day crustaceans, the mantis shrimps, does in fact possess mushroom bodies.

By visualizing cells and pathways within the brains of mantis shrimps, and also a number of closely related species, Wolff et al. show that only these shrimps possess true mushroom bodies. However, some of the mantis shrimp’s close relatives possess a few attributes of these structures. This suggests that mushroom bodies are evolutionarily ancient structures that arose in a common ancestor of insects and crustaceans, before being lost or radically modified in most of the crustaceans.

So why did this happen? Mantis shrimps are top predators with excellent vision that hunt over considerable distances, requiring them to evaluate and memorize complex features of their environment. These cognitive demands, which might not be shared by other crustaceans, may have led to the mantis shrimps retaining their mushroom bodies. Further research into the brains and behavior of the mantis shrimp may provide insights into how mushroom bodies construct memories of a complex sensory world.

Spatiotemporal network coding of physiological mossy fiber inputs by the cerebellar granular layer

The granular layer, which mainly consists of granule and Golgi cells, is the first stage of the cerebellar cortex and processes spatiotemporal information transmitted by mossy fiber inputs with a wide variety of firing patterns. To study its dynamics at multiple time scales in response to inputs approximating real spatiotemporal patterns, we constructed a large-scale 3D network model of the granular layer. Patterned mossy fiber activity induces rhythmic Golgi cell activity that is synchronized by shared parallel fiber input and by gap junctions. This leads to long distance synchrony of Golgi cells along the transverse axis, powerfully regulating granule cell firing by imposing inhibition during a specific time window. The essential network mechanisms, including tunable Golgi cell oscillations, on-beam inhibition and NMDA receptors causing first winner keeps winning of granule cells, illustrate how fundamental properties of the granule layer operate in tandem to produce (1) well timed and spatially bound output, (2) a wide dynamic range of granule cell firing and (3) transient and coherent gating oscillations. These results substantially enrich our understanding of granule cell layer processing, which seems to promote spatial group selection of granule cell activity as a function of timing of mossy fiber input.

The cerebellum is an organ of peculiar geometrical properties, and has been attributed the function of applying spatiotemporal transforms to sensorimotor data since Eccles. In this work we have analyzed the spatiotemporal response properties of the first part of the cerebellar circuit, the granule layer. On the basis of a biophysically plausible and large-scale model of the cerebellum, constrained by a wealth of anatomical data, we study the network dynamics and firing properties of individual cell populations in response to 'realistic' input patterns. We make specific predictions about the spatiotemporal features of granule layer processing regarding the effects of the gap junction coupled network of Golgi cells on a spatially restricted input, in an effect we denominate first-takes-all. Furthermore, we calculate that the granule cell layer has a wide dynamic range, indicating that this is a system that can transmit large variations of input intensities.

Origins of Cell-Type-Specific Olfactory Processing in the Drosophila Mushroom Body Circuit

How cell-type-specific physiological properties shape neuronal functions in a circuit remains poorly understood. We addressed this issue in the Drosophila mushroom body (MB), a higher olfactory circuit, where neurons belonging to distinct glomeruli in the antennal lobe feed excitation to three types of intrinsic neurons, α/β, α'/β', and γ Kenyon cells (KCs). Two-photon optogenetics and intracellular recording revealed that whereas glomerular inputs add similarly in all KCs, spikes were generated most readily in α'/β' KCs. This cell type was also the most competent in recruiting GABAergic inhibition fed back by anterior paired lateral neuron, which responded to odors either locally within a lobe or globally across all lobes depending on the strength of stimuli. Notably, as predicted from these physiological properties, α'/β' KCs had the highest odor detection speed, sensitivity, and discriminability. This enhanced discrimination required proper GABAergic inhibition. These results link cell-type-specific mechanisms and functions in the MB circuit.

Calcium imaging revealed no modulatory effect on odor-evoked responses of the Drosophila antennal lobe by two populations of inhibitory local interneurons

Although we have considerable knowledge about how odors are represented in the antennal lobe (AL), the insects’ analogue to the olfactory bulb, we still do not fully understand how the different neurons in the AL network contribute to the olfactory code. In Drosophila melanogaster we can selectively manipulate specific neuronal populations to elucidate their function in odor processing. Here we silenced the synaptic transmission of two distinct subpopulations of multiglomerular GABAergic local interneurons (LN1 and LN2) using shibire (shi^ts) and analyzed their impact on odor-induced glomerular activity at the AL input and output level. We verified that the employed shi^ts construct effectively blocked synaptic transmission to the AL when expressed in olfactory sensory neurons. Notably, selective silencing of both LN populations did not significantly affect the odor-evoked activity patterns in the AL. Neither the glomerular input nor the glomerular output activity was modulated in comparison to the parental controls. We therefore conclude that these LN subpopulations, which cover one third of the total LN number, are not predominantly involved in odor identity coding per se. As suggested by their broad innervation patterns and contribution to long-term adaptation, they might contribute to AL–computation on a global and longer time scale.

Circuit Mechanisms of Sensorimotor Learning

The relationship between the brain and the environment is flexible, forming the foundation for our ability to learn. Here we review the current state of our understanding of the modifications in the sensorimotor pathway related to sensorimotor learning. We divide the process into three hierarchical levels with distinct goals: (1) sensory perceptual learning, (2) sensorimotor associative learning, and (3) motor skill learning. Perceptual learning optimizes the representations of important sensory stimuli. Associative learning and the initial phase of motor skill learning are ensured by feedback-based mechanisms that permit trial-and-error learning. The later phase of motor skill learning may primarily involve feedback-independent mechanisms operating under the classic Hebbian rule. With these changes under distinct constraints and mechanisms, sensorimotor learning establishes dedicated circuitry for the reproduction of stereotyped neural activity patterns and behavior.

A connectome of a learning and memory center in the adult Drosophila brain

Understanding memory formation, storage and retrieval requires knowledge of the underlying neuronal circuits. In Drosophila, the mushroom body (MB) is the major site of associative learning. We reconstructed the morphologies and synaptic connections of all 983 neurons within the three functional units, or compartments, that compose the adult MB’s α lobe, using a dataset of isotropic 8 nm voxels collected by focused ion-beam milling scanning electron microscopy. We found that Kenyon cells (KCs), whose sparse activity encodes sensory information, each make multiple en passant synapses to MB output neurons (MBONs) in each compartment. Some MBONs have inputs from all KCs, while others differentially sample sensory modalities. Only 6% of KC>MBON synapses receive a direct synapse from a dopaminergic neuron (DAN). We identified two unanticipated classes of synapses, KC>DAN and DAN>MBON. DAN activation produces a slow depolarization of the MBON in these DAN>MBON synapses and can weaken memory recall.

DOI:http://dx.doi.org/10.7554/eLife.26975.001

Regeneration of synapses in the olfactory pathway of locusts after antennal deafferentation

The olfactory pathway of the locust is capable of fast and precise regeneration on an anatomical level. Following deafferentation of the antenna either of young adult locusts, or of fifth instar nymphs, severed olfactory receptor neurons (ORNs) reinnervate the antennal lobe (AL) and arborize in AL microglomeruli. In the present study we tested whether these regenerated fibers establish functional synapses again. Intracellular recordings from AL projection neurons revealed that the first few odor stimulus evoked postsynaptic responses from regenerated ORNs from day 4-7 post crush on. On average, synaptic connections of regenerated afferents appeared faster in younger locusts operated as fifth instar nymphs than in adults. The proportions of response categories (excitatory vs. inhibitory) changed during regeneration, but were back to normal within 21 days. Odor-evoked oscillating extracellular local field potentials (LFP) were recorded in the mushroom body. These responses, absent after antennal nerve crush, reappeared, in a few animals as soon as 4 days post crush. Odor-induced oscillation patterns were restored within 7 days post crush. Both intra- and extracellular recordings indicate the capability of the locust olfactory system to re-establish synaptic contacts in the antennal lobe after antennal nerve lesion.

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.

What can tiny mushrooms in fruit flies tell us about learning and memory?

Nervous systems have evolved to translate external stimuli into appropriate behavioral responses. In an ever-changing environment, flexible adjustment of behavioral choice by experience-dependent learning is essential for the animal's survival. Associative learning is a simple form of learning that is widely observed from worms to humans. To understand the whole process of learning, we need to know how sensory information is represented and transformed in the brain, how it is changed by experience, and how the changes are reflected on motor output. To tackle these questions, studying numerically simple invertebrate nervous systems has a great advantage. In this review, I will feature the Pavlovian olfactory learning in the fruit fly, Drosophila melanogaster. The mushroom body is a key brain area for the olfactory learning in this organism. Recently, comprehensive anatomical information and the genetic tool sets were made available for the mushroom body circuit. This greatly accelerated the physiological understanding of the learning process. One of the key findings was dopamine-induced long-term synaptic plasticity that can alter the representations of stimulus valence. I will mostly focus on the new studies within these few years and discuss what we can possibly learn about the vertebrate systems from this model organism.

Interaction of compass sensing and object-motion detection in the locust central complex

Goal-directed behavior is often complicated by unpredictable events, such as the appearance of a predator during directed locomotion. This situation requires adaptive responses like evasive maneuvers followed by subsequent reorientation and course correction. Here we study the possible neural underpinnings of such a situation in an insect, the desert locust. As in other insects, its sense of spatial orientation strongly relies on the central complex, a group of midline brain neuropils. The central complex houses sky compass cells that signal the polarization plane of skylight and thus indicate the animal's steering direction relative to the sun. Most of these cells additionally respond to small moving objects that drive fast sensory-motor circuits for escape. Here we investigate how the presentation of a moving object influences activity of the neurons during compass signaling. Cells responded in one of two ways: in some neurons, responses to the moving object were simply added to the compass response that had adapted during continuous stimulation by stationary polarized light. By contrast, other neurons disadapted, i.e., regained their full compass response to polarized light, when a moving object was presented. We propose that the latter case could help to prepare for reorientation of the animal after escape. A neuronal network based on central-complex architecture can explain both responses by slight changes in the dynamics and amplitudes of adaptation to polarized light in CL columnar input neurons of the system.NEW & NOTEWORTHY Neurons of the central complex in several insects signal compass directions through sensitivity to the sky polarization pattern. In locusts, these neurons also respond to moving objects. We show here that during polarized-light presentation, responses to moving objects override their compass signaling or restore adapted inhibitory as well as excitatory compass responses. A network model is presented to explain the variations of these responses that likely serve to redirect flight or walking following evasive maneuvers.

Persistent irregular activity is a result of rebound and coincident detection mechanisms: A computational study

Persistent irregular activity is defined as elevated irregular neural discharges in the brain in such a way that while the average network activity displays high frequency oscillations, the participating neurons display irregular and low frequency oscillations. This type of activity is observed in many brain regions like prefrontal cortex that plays a role in working memory. Previous studies have shown that large networks with sparse connections, networks with strong noise and persistent inhibition and networks with structured synaptic connections display persistent-irregular activity. However, experimental studies show that, not all brain regions obey these assumptions. In this study we show that a small network of excitatory-inhibitory neurons with random synaptic connections can reproduce persistent-irregular activity. In particular, the model shows that less than perfect rebound pattern in excitatory cells, coincident-sensitive inhibitory cells and sparse synaptic inhibition can account for persistent-irregular activity in an excitatory-inhibitory neural network with randomly assigned synaptic connections.

Complementary codes for odor identity and intensity in olfactory cortex

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

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

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.

Normalized Neural Representations of Complex Odors

The olfactory system removes correlations in natural odors using a network of inhibitory neurons in the olfactory bulb. It has been proposed that this network integrates the response from all olfactory receptors and inhibits them equally. However, how such global inhibition influences the neural representations of odors is unclear. Here, we study a simple statistical model of the processing in the olfactory bulb, which leads to concentration-invariant, sparse representations of the odor composition. We show that the inhibition strength can be tuned to obtain sparse representations that are still useful to discriminate odors that vary in relative concentration, size, and composition. The model reveals two generic consequences of global inhibition: (i) odors with many molecular species are more difficult to discriminate and (ii) receptor arrays with heterogeneous sensitivities perform badly. Comparing these predictions to experiments will help us to understand the role of global inhibition in shaping normalized odor representations in the olfactory bulb.

The Olfactory Mosaic: Bringing an Olfactory Network Together for Odor Perception

Olfactory perception and its underlying neural mechanisms are not fixed, but rather vary over time, dependent on various parameters such as state, task, or learning experience. In olfaction, one of the primary sensory areas beyond the olfactory bulb is the piriform cortex. Due to an increasing number of functions attributed to the piriform cortex, it has been argued to be an associative cortex rather than a simple primary sensory cortex. In fact, the piriform cortex plays a key role in creating olfactory percepts, helping to form configural odor objects from the molecular features extracted in the nose. Moreover, its dynamic interactions with other olfactory and nonolfactory areas are also critical in shaping the olfactory percept and resulting behavioral responses. In this brief review, we will describe the key role of the piriform cortex in the larger olfactory perceptual network, some of the many actors of this network, and the importance of the dynamic interactions among the piriform-trans-thalamic and limbic pathways.

Complete identification of four giant interneurons supplying mushroom body calyces in the cockroach Periplaneta americana

Global inhibition is a fundamental physiological mechanism that has been proposed to shape odor representation in higher-order olfactory centers. A pair of mushroom bodies (MBs) in insect brains, an analog of the mammalian olfactory cortex, are implicated in multisensory integration and associative memory formation. With the use of single/multiple intracellular recording and staining in the cockroach Periplaneta americana, we succeeded in unambiguous identification of four tightly bundled GABA-immunoreactive giant interneurons that are presumably involved in global inhibitory control of the MB. These neurons, including three spiking neurons and one nonspiking neuron, possess dendrites in termination fields of MB output neurons and send axon terminals back to MB input sites, calyces, suggesting feedback roles onto the MB. The largest spiking neuron innervates almost exclusively the basal region of calyces, while the two smaller spiking neurons and the second-largest nonspiking neuron innervate more profusely the peripheral (lip) region of the calyces than the basal region. This subdivision corresponds well to the calycal zonation made by axon terminals of two populations of uniglomerular projection neurons with dendrites in distinct glomerular groups in the antennal lobe. The four giant neurons exhibited excitatory responses to every odor tested in a neuron-specific fashion, and two of the neurons also exhibited inhibitory responses in some recording sessions. Our results suggest that two parallel olfactory inputs to the MB undergo different forms of inhibitory control by the giant neurons, which may, in turn, be involved in different aspects of odor discrimination, plasticity, and state-dependent gain control. J. Comp. Neurol. 525:204-230, 2017. © 2016 Wiley Periodicals, Inc.

Simulating Cortical Feedback Modulation as Changes in Excitation and Inhibition in a Cortical Circuit Model

Cortical feedback pathways are hypothesized to distribute context-dependent signals during flexible behavior. Recent experimental work has attempted to understand the mechanisms by which cortical feedback inputs modulate their target regions. Within the mouse whisker sensorimotor system, cortical feedback stimulation modulates spontaneous activity and sensory responsiveness, leading to enhanced sensory representations. However, the cellular mechanisms underlying these effects are currently unknown. In this study we use a simplified neural circuit model, which includes two recurrent excitatory populations and global inhibition, to simulate cortical modulation. First, we demonstrate how changes in the strengths of excitation and inhibition alter the input-output processing responses of our model. Second, we compare these responses with experimental findings from cortical feedback stimulation. Our analyses predict that enhanced inhibition underlies the changes in spontaneous and sensory evoked activity observed experimentally. More generally, these analyses provide a framework for relating cellular and synaptic properties to emergent circuit function and dynamic modulation.

Adult Neurogenesis in the Songbird: Region-Specific Contributions of New Neurons to Behavioral Plasticity and Stability

Our understanding of the role of new neurons in learning and encoding new information has been largely based on studies of new neurons in the mammalian dentate gyrus and olfactory bulb - brain regions that may be specialized for learning. Thus the role of new neurons in regions that serve other functions has yet to be fully explored. The song system provides a model for studying new neuron function in brain regions that contribute differently to song learning, song auditory discrimination, and song motor production. These regions subserve learning as well as long-term storage of previously learned information. This review examines the differences between learning-based and activity-based retention of new neurons and explores the potential contributions of new neurons to behavioral stability in the song motor production pathway.

Dopaminergic neurons write and update memories with cell-type-specific rules

Associative learning is thought to involve parallel and distributed mechanisms of memory formation and storage. In Drosophila, the mushroom body (MB) is the major site of associative odor memory formation. Previously we described the anatomy of the adult MB and defined 20 types of dopaminergic neurons (DANs) that each innervate distinct MB compartments (Aso et al., 2014a, 2014b). Here we compare the properties of memories formed by optogenetic activation of individual DAN cell types. We found extensive differences in training requirements for memory formation, decay dynamics, storage capacity and flexibility to learn new associations. Even a single DAN cell type can either write or reduce an aversive memory, or write an appetitive memory, depending on when it is activated relative to odor delivery. Our results show that different learning rules are executed in seemingly parallel memory systems, providing multiple distinct circuit-based strategies to predict future events from past experiences.

A Fly-Inspired Mushroom Bodies Model for Sensory-Motor Control Through Sequence and Subsequence Learning

Classification and sequence learning are relevant capabilities used by living beings to extract complex information from the environment for behavioral control. The insect world is full of examples where the presentation time of specific stimuli shapes the behavioral response. On the basis of previously developed neural models, inspired by Drosophila melanogaster, a new architecture for classification and sequence learning is here presented under the perspective of the Neural Reuse theory. Classification of relevant input stimuli is performed through resonant neurons, activated by the complex dynamics generated in a lattice of recurrent spiking neurons modeling the insect Mushroom Bodies neuropile. The network devoted to context formation is able to reconstruct the learned sequence and also to trace the subsequences present in the provided input. A sensitivity analysis to parameter variation and noise is reported. Experiments on a roving robot are reported to show the capabilities of the architecture used as a neural controller.

Plasticity in the Olfactory System: Lessons for the Neurobiology of Memory

We are rapidly advancing toward an understanding of the molecular events underlying odor transduction, mechanisms of spatiotemporal central odor processing, and neural correlates of olfactory perception and cognition. A thread running through each of these broad components that define olfaction appears to be their dynamic nature. How odors are processed, at both the behavioral and neural level, is heavily dependent on past experience, current environmental context, and internal state. The neural plasticity that allows this dynamic processing is expressed nearly ubiquitously in the olfactory pathway, from olfactory receptor neurons to the higher-order cortex, and includes mechanisms ranging from changes in membrane excitability to changes in synaptic efficacy to neurogenesis and apoptosis. This review will describe recent findings regarding plasticity in the mammalian olfactory system that are believed to have general relevance for understanding the neurobiology of memory.

Neural mechanisms of insect navigation

We know more about the ethology of insect navigation than the neural substrates. Few studies have shown direct effects of brain manipulation on navigational behaviour; or measure brain responses that clearly relate to the animal's current location or spatial target, independently of specific sensory cues. This is partly due to the methodological problems of obtaining neural data in a naturally behaving animal. However, substantial indirect evidence, such as comparative anatomy and knowledge of the neural circuits that provide relevant sensory inputs provide converging arguments for the role of some specific brain areas: the mushroom bodies; and the central complex. Finally, modelling can help bridge the gap by relating the computational requirements of a given navigational task to the type of computation offered by different brain areas.

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

A Robust Feedforward Model of the Olfactory System

Most natural odors have sparse molecular composition. This makes the principles of compressed sensing potentially relevant to the structure of the olfactory code. Yet, the largely feedforward organization of the olfactory system precludes reconstruction using standard compressed sensing algorithms. To resolve this problem, recent theoretical work has shown that signal reconstruction could take place as a result of a low dimensional dynamical system converging to one of its attractor states. However, the dynamical aspects of optimization slowed down odor recognition and were also found to be susceptible to noise. Here we describe a feedforward model of the olfactory system that achieves both strong compression and fast reconstruction that is also robust to noise. A key feature of the proposed model is a specific relationship between how odors are represented at the glomeruli stage, which corresponds to a compression, and the connections from glomeruli to third-order neurons (neurons in the olfactory cortex of vertebrates or Kenyon cells in the mushroom body of insects), which in the model corresponds to reconstruction. We show that should this specific relationship hold true, the reconstruction will be both fast and robust to noise, and in particular to the false activation of glomeruli. The predicted connectivity rate from glomeruli to third-order neurons can be tested experimentally.

Heterosynaptic Plasticity Underlies Aversive Olfactory Learning in Drosophila

Although associative learning has been localized to specific brain areas in many animals, identifying the underlying synaptic processes in vivo has been difficult. Here, we provide the first demonstration of long-term synaptic plasticity at the output site of the Drosophila mushroom body. Pairing an odor with activation of specific dopamine neurons induces both learning and odor-specific synaptic depression. The plasticity induction strictly depends on the temporal order of the two stimuli, replicating the logical requirement for associative learning. Furthermore, we reveal that dopamine action is confined to and distinct across different anatomical compartments of the mushroom body lobes. Finally, we find that overlap between sparse representations of different odors defines both stimulus specificity of the plasticity and generalizability of associative memories across odors. Thus, the plasticity we find here not only manifests important features of associative learning but also provides general insights into how a sparse sensory code is read out.

Remembering Components of Food in Drosophila

Remembering features of past feeding experience can refine foraging and food choice. Insects can learn to associate sensory cues with components of food, such as sugars, amino acids, water, salt, alcohol, toxins and pathogens. In the fruit fly Drosophila some food components activate unique subsets of dopaminergic neurons (DANs) that innervate distinct functional zones on the mushroom bodies (MBs). This architecture suggests that the overall dopaminergic neuron population could provide a potential cellular substrate through which the fly might learn to value a variety of food components. In addition, such an arrangement predicts that individual component memories reside in unique locations. DANs are also critical for food memory consolidation and deprivation-state dependent motivational control of the expression of food-relevant memories. Here, we review our current knowledge of how nutrient-specific memories are formed, consolidated and specifically retrieved in insects, with a particular emphasis on Drosophila.

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.

A novel method for quantifying similarities between oscillatory neural responses in wavelet time-frequency power profiles

Quantifying similarities and differences between neural response patterns is an important step in understanding neural coding in sensory systems. It is difficult, however, to compare the degree of similarity among transient oscillatory responses. We developed a novel method of wavelet correlation analysis for quantifying similarity between transient oscillatory responses, and tested the method with olfactory cortical responses. In the anterior piriform cortex (aPC), the largest area of the primary olfactory cortex, odors induce inhibitory activities followed by transient oscillatory local field potentials (osci-LFPs). Qualitatively, the resulting time courses of osci-LFPs for identical odors were modestly different. We then compared several methods for quantifying the similarity between osci-LFPs for identical or different odors. Using fast Fourier transform band-pass filters, a conventional method demonstrated high correlations of the 0-2Hz components for both identical and different odors. None of the conventional methods tested demonstrated a clear correlation between osci-LFPs. However, wavelet correlation analysis resolved a stimulus dependency of 2-45Hz osci-LFPs in the aPC output layer, and produced experience-dependent high correlations in the input layer between some of the identical or different odors. These results suggest that redundancy in the neural representation of sensory information may change in the aPC. This wavelet correlation analysis may be useful for quantifying the similarities of transient oscillatory neural responses.

Using an Insect Mushroom Body Circuit to Encode Route Memory in Complex Natural Environments

Ants, like many other animals, use visual memory to follow extended routes through complex environments, but it is unknown how their small brains implement this capability. The mushroom body neuropils have been identified as a crucial memory circuit in the insect brain, but their function has mostly been explored for simple olfactory association tasks. We show that a spiking neural model of this circuit originally developed to describe fruitfly (Drosophila melanogaster) olfactory association, can also account for the ability of desert ants (Cataglyphis velox) to rapidly learn visual routes through complex natural environments. We further demonstrate that abstracting the key computational principles of this circuit, which include one-shot learning of sparse codes, enables the theoretical storage capacity of the ant mushroom body to be estimated at hundreds of independent images.

Studying small brains to understand the building blocks of cognition

Cognition encompasses a range of higher-order mental processes, such as attention, working memory, and model-based decision-making. These processes are thought to involve the dynamic interaction of multiple central brain regions. A mechanistic understanding of such computations requires not only monitoring and manipulating specific neural populations during behavior, but also knowing the connectivity of the underlying circuitry. These goals are experimentally challenging in mammals, but are feasible in numerically simpler insect brains. In Drosophila melanogaster in particular, genetic tools enable precisely targeted physiology and optogenetics in actively behaving animals. In this article we discuss how these advantages are increasingly being leveraged to study abstract neural representations and sensorimotor computations that may be relevant for cognition in both insects and mammals.

Parallel Olfactory Processing in the Honey Bee Brain: Odor Learning and Generalization under Selective Lesion of a Projection Neuron Tract

The function of parallel neural processing is a fundamental problem in Neuroscience, as it is found across sensory modalities and evolutionary lineages, from insects to humans. Recently, parallel processing has attracted increased attention in the olfactory domain, with the demonstration in both insects and mammals that different populations of second-order neurons encode and/or process odorant information differently. Among insects, Hymenoptera present a striking olfactory system with a clear neural dichotomy from the periphery to higher-order centers, based on two main tracts of second-order (projection) neurons: the medial and lateral antennal lobe tracts (m-ALT and l-ALT). To unravel the functional role of these two pathways, we combined specific lesions of the m-ALT tract with behavioral experiments, using the classical conditioning of the proboscis extension response (PER conditioning). Lesioned and intact bees had to learn to associate an odorant (1-nonanol) with sucrose. Then the bees were subjected to a generalization procedure with a range of odorants differing in terms of their carbon chain length or functional group. We show that m-ALT lesion strongly affects acquisition of an odor-sucrose association. However, lesioned bees that still learned the association showed a normal gradient of decreasing generalization responses to increasingly dissimilar odorants. Generalization responses could be predicted to some extent by in vivo calcium imaging recordings of l-ALT neurons. The m-ALT pathway therefore seems necessary for normal classical olfactory conditioning performance.

Spatiotemporal Coding of Individual Chemicals by the Gustatory System

Four of the five major sensory systems (vision, olfaction, somatosensation, and audition) are thought to use different but partially overlapping sets of neurons to form unique representations of vast numbers of stimuli. The only exception is gustation, which is thought to represent only small numbers of basic taste categories. However, using new methods for delivering tastant chemicals and making electrophysiological recordings from the tractable gustatory system of the moth Manduca sexta, we found chemical-specific information is as follows: (1) initially encoded in the population of gustatory receptor neurons as broadly distributed spatiotemporal patterns of activity; (2) dramatically integrated and temporally transformed as it propagates to monosynaptically connected second-order neurons; and (3) observed in tastant-specific behavior. Our results are consistent with an emerging view of the gustatory system: rather than constructing basic taste categories, it uses a spatiotemporal population code to generate unique neural representations of individual tastant chemicals.

SIGNIFICANCE STATEMENT

Our results provide a new view of taste processing. Using a new, relatively simple model system and a new set of techniques to deliver taste stimuli and to examine gustatory receptor neurons and their immediate followers, we found no evidence for labeled line connectivity, or basic taste categories such as sweet, salty, bitter, and sour. Rather, individual tastant chemicals are represented as patterns of spiking activity distributed across populations of receptor neurons. These representations are transformed substantially as multiple types of receptor neurons converge upon follower neurons, leading to a combinatorial coding format that uniquely, rapidly, and efficiently represents individual taste chemicals. Finally, we found that the information content of these neurons can drive tastant-specific behavior.

Supranormal orientation selectivity of visual neurons in orientation-restricted animals

Altered sensory experience in early life often leads to remarkable adaptations so that humans and animals can make the best use of the available information in a particular environment. By restricting visual input to a limited range of orientations in young animals, this investigation shows that stimulus selectivity, e.g., the sharpness of tuning of single neurons in the primary visual cortex, is modified to match a particular environment. Specifically, neurons tuned to an experienced orientation in orientation-restricted animals show sharper orientation tuning than neurons in normal animals, whereas the opposite was true for neurons tuned to non-experienced orientations. This sharpened tuning appears to be due to elongated receptive fields. Our results demonstrate that restricted sensory experiences can sculpt the supranormal functions of single neurons tailored for a particular environment. The above findings, in addition to the minimal population response to orientations close to the experienced one, agree with the predictions of a sparse coding hypothesis in which information is represented efficiently by a small number of activated neurons. This suggests that early brain areas adopt an efficient strategy for coding information even when animals are raised in a severely limited visual environment where sensory inputs have an unnatural statistical structure.

Electrosensory Midbrain Neurons Display Feature Invariant Responses to Natural Communication Stimuli

Neurons that respond selectively but in an invariant manner to a given feature of natural stimuli have been observed across species and systems. Such responses emerge in higher brain areas, thereby suggesting that they occur by integrating afferent input. However, the mechanisms by which such integration occurs are poorly understood. Here we show that midbrain electrosensory neurons can respond selectively and in an invariant manner to heterogeneity in behaviorally relevant stimulus waveforms. Such invariant responses were not seen in hindbrain electrosensory neurons providing afferent input to these midbrain neurons, suggesting that response invariance results from nonlinear integration of such input. To test this hypothesis, we built a model based on the Hodgkin-Huxley formalism that received realistic afferent input. We found that multiple combinations of parameter values could give rise to invariant responses matching those seen experimentally. Our model thus shows that there are multiple solutions towards achieving invariant responses and reveals how subthreshold membrane conductances help promote robust and invariant firing in response to heterogeneous stimulus waveforms associated with behaviorally relevant stimuli. We discuss the implications of our findings for the electrosensory and other systems.

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.

Efficient reinforcement learning of a reservoir network model of parametric working memory achieved with a cluster population winner-take-all readout mechanism

The brain often has to make decisions based on information stored in working memory, but the neural circuitry underlying working memory is not fully understood. Many theoretical efforts have been focused on modeling the persistent delay period activity in the prefrontal areas that is believed to represent working memory. Recent experiments reveal that the delay period activity in the prefrontal cortex is neither static nor homogeneous as previously assumed. Models based on reservoir networks have been proposed to model such a dynamical activity pattern. The connections between neurons within a reservoir are random and do not require explicit tuning. Information storage does not depend on the stable states of the network. However, it is not clear how the encoded information can be retrieved for decision making with a biologically realistic algorithm. We therefore built a reservoir-based neural network to model the neuronal responses of the prefrontal cortex in a somatosensory delayed discrimination task. We first illustrate that the neurons in the reservoir exhibit a heterogeneous and dynamical delay period activity observed in previous experiments. Then we show that a cluster population circuit decodes the information from the reservoir with a winner-take-all mechanism and contributes to the decision making. Finally, we show that the model achieves a good performance rapidly by shaping only the readout with reinforcement learning. Our model reproduces important features of previous behavior and neurophysiology data. We illustrate for the first time how task-specific information stored in a reservoir network can be retrieved with a biologically plausible reinforcement learning training scheme.

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.

Plasticity-driven individualization of olfactory coding in mushroom body output neurons

Although all sensory circuits ascend to higher brain areas where stimuli are represented in sparse, stimulus-specific activity patterns, relatively little is known about sensory coding on the descending side of neural circuits, as a network converges. In insects, mushroom bodies (MBs) have been an important model system for studying sparse coding in the olfactory system^1–3, where this format is important for accurate memory formation^4–6. In Drosophila, it has recently been shown that the 2000 Kenyon cells (KCs) of the MB converge onto a population of only 35 MB output neurons (MBONs), that fall into 22 anatomically distinct cell types^7,8. Here we provide the first comprehensive view of olfactory representations at the fourth layer of the circuit, where we find a clear transition in the principles of sensory coding. We show that MBON tuning curves are highly correlated with one another. This is in sharp contrast to the process of progressive decorrelation of tuning in the earlier layers of the circuit^2,9. Instead, at the population level, odor representations are reformatted so that positive and negative correlations arise between representations of different odors. At the single-cell level, we show that uniquely identifiable MBONs display profoundly different tuning across different animals, but tuning of the same neuron across the two hemispheres of an individual fly was nearly identical. Thus, individualized coordination of tuning arises at this level of the olfactory circuit. Furthermore, we find that this individualization is an active process that requires a learning-related gene, rutabaga. Ultimately, neural circuits have to flexibly map highly stimulus-specific information in sparse layers onto a limited number of different motor outputs. The reformatting of sensory representations we observe here may mark the beginning of this sensory-motor transition in the olfactory system.