Testing odor response stereotypy in the Drosophila mushroom body

A Critical Role for the Fascin Family of Actin Bundling Proteins in Axon Development, Brain Wiring and Function

Actin-based cell motility drives many neurodevelopmental events including guided axonal growth. Fascin is a major family of F-actin bundling proteins, but its role in axon development in vivo and brain wiring remains unclear. Here, we report that fascin is required for axon development, brain wiring and function. We show that fascin is enriched in the motile filopodia of axonal growth cones and its inhibition impairs axonal extension and branching of hippocampal neurons in culture. We next provide evidence that fascin is essential for axon development and brain wiring in vivo using Drosophila melanogaster as a model. Drosophila expresses a single ortholog of mammalian fascin called Singed (SN), which is highly expressed in the mushroom body (MB) of the central nervous system. We observe that loss of SN results in drastic MB disruption, highlighted by α- and β-lobe defects that are consistent with altered axonal guidance. SN-null flies also exhibit defective sensorimotor behaviors as assessed by the negative geotaxis assay. MB- specific expression of SN in SN-null flies rescues MB structure and sensorimotor deficits, indicating that SN functions autonomously in MB neurons. Together, our data from primary neuronal culture and in vivo models highlight a critical role for fascin in brain development and function.

Highlights

Fascin regulates axonal growth and branching of hippocampal neurons in culture.Singed, Drosophila fascin, is enriched specifically in mushroom body (MB) axons.Singed loss causes axon guidance defects and sensorimotor issues in flies.MB-specific Singed re-expression rescues MB structure and behavior in flies.

Neuronal wiring diagram of an adult brain

Connections between neurons can be mapped by acquiring and analysing electron microscopic brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative1-6, but nevertheless inadequate for understanding brain function more globally. Here we present a neuronal wiring diagram of a whole brain containing 5 × 107 chemical synapses7 between 139,255 neurons reconstructed from an adult female Drosophila melanogaster8,9. The resource also incorporates annotations of cell classes and types, nerves, hemilineages and predictions of neurotransmitter identities10-12. Data products are available for download, programmatic access and interactive browsing and have been made interoperable with other fly data resources. We derive a projectome-a map of projections between regions-from the connectome and report on tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine and descending neurons) across both hemispheres and between the central brain and the optic lobes. Tracing from a subset of photoreceptors to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviours. The technologies and open ecosystem reported here set the stage for future large-scale connectome projects in other species.

Spatial constraints and cell surface molecule depletion structure a randomly connected learning circuit

The brain can represent almost limitless objects to "categorize an unlabeled world" (Edelman, 1989). This feat is supported by expansion layer circuit architectures, in which neurons carrying information about discrete sensory channels make combinatorial connections onto much larger postsynaptic populations. Combinatorial connections in expansion layers are modeled as randomized sets. The extent to which randomized wiring exists in vivo is debated, and how combinatorial connectivity patterns are generated during development is not understood. Non-deterministic wiring algorithms could program such connectivity using minimal genomic information. Here, we investigate anatomic and transcriptional patterns and perturb partner availability to ask how Kenyon cells, the expansion layer neurons of the insect mushroom body, obtain combinatorial input from olfactory projection neurons. Olfactory projection neurons form their presynaptic outputs in an orderly, predictable, and biased fashion. We find that Kenyon cells accept spatially co-located but molecularly heterogeneous inputs from this orderly map, and ask how Kenyon cell surface molecule expression impacts partner choice. Cell surface immunoglobulins are broadly depleted in Kenyon cells, and we propose that this allows them to form connections with molecularly heterogeneous partners. This model can explain how developmentally identical neurons acquire diverse wiring identities.

Diversity of visual inputs to Kenyon cells of the Drosophila mushroom body

The arthropod mushroom body is well-studied as an expansion layer representing olfactory stimuli and linking them to contingent events. However, 8% of mushroom body Kenyon cells in Drosophila melanogaster receive predominantly visual input, and their function remains unclear. Here, we identify inputs to visual Kenyon cells using the FlyWire adult whole-brain connectome. Input repertoires are similar across hemispheres and connectomes with certain inputs highly overrepresented. Many visual neurons presynaptic to Kenyon cells have large receptive fields, while interneuron inputs receive spatially restricted signals that may be tuned to specific visual features. Individual visual Kenyon cells randomly sample sparse inputs from combinations of visual channels, including multiple optic lobe neuropils. These connectivity patterns suggest that visual coding in the mushroom body, like olfactory coding, is sparse, distributed, and combinatorial. However, the specific input repertoire to the smaller population of visual Kenyon cells suggests a constrained encoding of visual stimuli.

Common principles for odour coding across vertebrates and invertebrates

The olfactory system is an ideal and tractable system for exploring how the brain transforms sensory inputs into behaviour. The basic tasks of any olfactory system include odour detection, discrimination and categorization. The challenge for the olfactory system is to transform the high-dimensional space of olfactory stimuli into the much smaller space of perceived objects and valence that endows odours with meaning. Our current understanding of how neural circuits address this challenge has come primarily from observations of the mechanisms of the brain for processing other sensory modalities, such as vision and hearing, in which optimized deep hierarchical circuits are used to extract sensory features that vary along continuous physical dimensions. The olfactory system, by contrast, contends with an ill-defined, high-dimensional stimulus space and discrete stimuli using a circuit architecture that is shallow and parallelized. Here, we present recent observations in vertebrate and invertebrate systems that relate the statistical structure and state-dependent modulation of olfactory codes to mechanisms of perception and odour-guided behaviour.

What do the mushroom bodies do for the insect brain? Twenty-five years of progress

In 1998, a special edition of Learning & Memory was published with a discrete focus of synthesizing the state of the field to provide an overview of the function of the insect mushroom body. While molecular neuroscience and optical imaging of larger brain areas were advancing, understanding the basic functioning of neuronal circuits, particularly in the context of the mushroom body, was rudimentary. In the past 25 years, technological innovations have allowed researchers to map and understand the in vivo function of the neuronal circuits of the mushroom body system, making it an ideal model for investigating the circuit basis of sensory encoding, memory formation, and behavioral decisions. Collaborative efforts within the community have played a crucial role, leading to an interactive connectome of the mushroom body and accessible genetic tools for studying mushroom body circuit function. Looking ahead, continued technological innovation and collaborative efforts are likely to further advance our understanding of the mushroom body and its role in behavior and cognition, providing insights that generalize to other brain structures and species.

Interactions between specialized gain control mechanisms in olfactory processing

Gain control is a process that adjusts a system's sensitivity when input levels change. Neural systems contain multiple mechanisms of gain control, but we do not understand why so many mechanisms are needed or how they interact. Here, we investigate these questions in the Drosophila antennal lobe, where we identify several types of inhibitory interneurons with specialized gain control functions. We find that some interneurons are nonspiking, with compartmentalized calcium signals, and they specialize in intra-glomerular gain control. Conversely, we find that other interneurons are recruited by strong and widespread network input; they specialize in global presynaptic gain control. Using computational modeling and optogenetic perturbations, we show how these mechanisms can work together to improve stimulus discrimination while also minimizing temporal distortions in network activity. Our results demonstrate how the robustness of neural network function can be increased by interactions among diverse and specialized mechanisms of gain control.

Diversity of visual inputs to Kenyon cells of the Drosophila mushroom body

The arthropod mushroom body is well-studied as an expansion layer that represents olfactory stimuli and links them to contingent events. However, 8% of mushroom body Kenyon cells in Drosophila melanogaster receive predominantly visual input, and their tuning and function are poorly understood. Here, we use the FlyWire adult whole-brain connectome to identify inputs to visual Kenyon cells. The types of visual neurons we identify are similar across hemispheres and connectomes with certain inputs highly overrepresented. Many visual projection neurons presynaptic to Kenyon cells receive input from large swathes of visual space, while local visual interneurons, providing smaller fractions of input, receive more spatially restricted signals that may be tuned to specific features of the visual scene. Like olfactory Kenyon cells, visual Kenyon cells receive sparse inputs from different combinations of visual channels, including inputs from multiple optic lobe neuropils. The sets of inputs to individual visual Kenyon cells are consistent with random sampling of available inputs. These connectivity patterns suggest that visual coding in the mushroom body, like olfactory coding, is sparse, distributed, and combinatorial. However, the expansion coding properties appear different, with a specific repertoire of visual inputs projecting onto a relatively small number of visual Kenyon cells.

Neuronal wiring diagram of an adult brain

Connections between neurons can be mapped by acquiring and analyzing electron microscopic (EM) brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative, yet inadequate for understanding brain function more globally. Here, we present the first neuronal wiring diagram of a whole adult brain, containing 5×107 chemical synapses between ~130,000 neurons reconstructed from a female Drosophila melanogaster. The resource also incorporates annotations of cell classes and types, nerves, hemilineages, and predictions of neurotransmitter identities. Data products are available by download, programmatic access, and interactive browsing and made interoperable with other fly data resources. We show how to derive a projectome, a map of projections between regions, from the connectome. We demonstrate the tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine, and descending neurons), across both hemispheres, and between the central brain and the optic lobes. Tracing from a subset of photoreceptors all the way to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviors. The technologies and open ecosystem of the FlyWire Consortium set the stage for future large-scale connectome projects in other species.

Input density tunes Kenyon cell sensory responses in the Drosophila mushroom body

The ability to discriminate sensory stimuli with overlapping features is thought to arise in brain structures called expansion layers, where neurons carrying information about sensory features make combinatorial connections onto a much larger set of cells. For 50 years, expansion coding has been a prime topic of theoretical neuroscience, which seeks to explain how quantitative parameters of the expansion circuit influence sensory sensitivity, discrimination, and generalization. Here, we investigate the developmental events that produce the quantitative parameters of the arthropod expansion layer, called the mushroom body. Using Drosophila melanogaster as a model, we employ genetic and chemical tools to engineer changes to circuit development. These allow us to produce living animals with hypothesis-driven variations on natural expansion layer wiring parameters. We then test the functional and behavioral consequences. By altering the number of expansion layer neurons (Kenyon cells) and their dendritic complexity, we find that input density, but not cell number, tunes neuronal odor selectivity. Simple odor discrimination behavior is maintained when the Kenyon cell number is reduced and augmented by Kenyon cell number expansion. Animals with increased input density to each Kenyon cell show increased overlap in Kenyon cell odor responses and become worse at odor discrimination tasks.

Transient photocurrents in a subthreshold evidence accumulator accelerate perceptual decisions

Perceptual decisions are complete when a continuously updated score of sensory evidence reaches a threshold. In Drosophila, αβ core Kenyon cells (αβc KCs) of the mushroom bodies integrate odor-evoked synaptic inputs to spike threshold at rates that parallel the speed of olfactory choices. Here we perform a causal test of the idea that the biophysical process of synaptic integration underlies the psychophysical process of bounded evidence accumulation in this system. Injections of single brief, EPSP-like depolarizations into the dendrites of αβc KCs during odor discrimination, using closed-loop control of a targeted opsin, accelerate decision times at a marginal cost of accuracy. Model comparisons favor a mechanism of temporal integration over extrema detection and suggest that the optogenetically evoked quanta are added to a growing total of sensory evidence, effectively lowering the decision bound. The subthreshold voltage dynamics of αβc KCs thus form an accumulator memory for sequential samples of information.

Experience-dependent plasticity in the olfactory system of Drosophila melanogaster and other insects

It is long known that the nervous system of vertebrates can be shaped by internal and external factors. On the other hand, the nervous system of insects was long assumed to be stereotypic, although evidence for plasticity effects accumulated for several decades. To cover the topic comprehensively, this review recapitulates the establishment of the term "plasticity" in neuroscience and introduces its original meaning. We describe the basic composition of the insect olfactory system using Drosophila melanogaster as a representative example and outline experience-dependent plasticity effects observed in this part of the brain in a variety of insects, including hymenopterans, lepidopterans, locusts, and flies. In particular, we highlight recent advances in the study of experience-dependent plasticity effects in the olfactory system of D. melanogaster, as it is the most accessible olfactory system of all insect species due to the genetic tools available. The partly contradictory results demonstrate that morphological, physiological and behavioral changes in response to long-term olfactory stimulation are more complex than previously thought. Different molecular mechanisms leading to these changes were unveiled in the past and are likely responsible for this complexity. We discuss common problems in the study of experience-dependent plasticity, ways to overcome them, and future directions in this area of research. In addition, we critically examine the transferability of laboratory data to natural systems to address the topic as holistically as possible. As a mechanism that allows organisms to adapt to new environmental conditions, experience-dependent plasticity contributes to an animal's resilience and is therefore a crucial topic for future research, especially in an era of rapid environmental changes.

Restructuring of olfactory representations in the fly brain around odor relationships in natural sources

A core challenge of olfactory neuroscience is to understand how neural representations of odor are generated and progressively transformed across different layers of the olfactory circuit into formats that support perception and behavior. The encoding of odor by odorant receptors in the input layer of the olfactory system reflects, at least in part, the chemical relationships between odor compounds. Neural representations of odor in higher order associative olfactory areas, generated by random feedforward networks, are expected to largely preserve these input odor relationships1-3. We evaluated these ideas by examining how odors are represented at different stages of processing in the olfactory circuit of the vinegar fly D. melanogaster. We found that representations of odor in the mushroom body (MB), a third-order associative olfactory area in the fly brain, are indeed structured and invariant across flies. However, the structure of MB representational space diverged significantly from what is expected in a randomly connected network. In addition, odor relationships encoded in the MB were better correlated with a metric of the similarity of their distribution across natural sources compared to their similarity with respect to chemical features, and the converse was true for odor relationships encoded in primary olfactory receptor neurons (ORNs). Comparison of odor coding at primary, secondary, and tertiary layers of the circuit revealed that odors were significantly regrouped with respect to their representational similarity across successive stages of olfactory processing, with the largest changes occurring in the MB. The non-linear reorganization of odor relationships in the MB indicates that unappreciated structure exists in the fly olfactory circuit, and this structure may facilitate the generalization of odors with respect to their co-occurence in natural sources.

Hacking brain development to test models of sensory coding

Animals can discriminate myriad sensory stimuli but can also generalize from learned experience. You can probably distinguish the favorite teas of your colleagues while still recognizing that all tea pales in comparison to coffee. Tradeoffs between detection, discrimination, and generalization are inherent at every layer of sensory processing. During development, specific quantitative parameters are wired into perceptual circuits and set the playing field on which plasticity mechanisms play out. A primary goal of systems neuroscience is to understand how material properties of a circuit define the logical operations-computations--that it makes, and what good these computations are for survival. A cardinal method in biology-and the mechanism of evolution--is to change a unit or variable within a system and ask how this affects organismal function. Here, we make use of our knowledge of developmental wiring mechanisms to modify hard-wired circuit parameters in the Drosophila melanogaster mushroom body and assess the functional and behavioral consequences. By altering the number of expansion layer neurons (Kenyon cells) and their dendritic complexity, we find that input number, but not cell number, tunes odor selectivity. Simple odor discrimination performance is maintained when Kenyon cell number is reduced and augmented by Kenyon cell expansion.

Drosophila olfaction: past, present and future

Among the many wonders of nature, the sense of smell of the fly Drosophila melanogaster might seem, at first glance, of esoteric interest. Nevertheless, for over a century, the 'nose' of this insect has been an extraordinary system to explore questions in animal behaviour, ecology and evolution, neuroscience, physiology and molecular genetics. The insights gained are relevant for our understanding of the sensory biology of vertebrates, including humans, and other insect species, encompassing those detrimental to human health. Here, I present an overview of our current knowledge of D. melanogaster olfaction, from molecules to behaviours, with an emphasis on the historical motivations of studies and illustration of how technical innovations have enabled advances. I also highlight some of the pressing and long-term questions.

Mushroom body input connections form independently of sensory activity in Drosophila melanogaster

Associative brain centers, such as the insect mushroom body, need to represent sensory information in an efficient manner. In Drosophila melanogaster, the Kenyon cells of the mushroom body integrate inputs from a random set of olfactory projection neurons, but some projection neurons-namely those activated by a few ethologically meaningful odors-connect to Kenyon cells more frequently than others. This biased and random connectivity pattern is conceivably advantageous, as it enables the mushroom body to represent a large number of odors as unique activity patterns while prioritizing the representation of a few specific odors. How this connectivity pattern is established remains largely unknown. Here, we test whether the mechanisms patterning the connections between Kenyon cells and projection neurons depend on sensory activity or whether they are hardwired. We mapped a large number of mushroom body input connections in partially anosmic flies-flies lacking the obligate odorant co-receptor Orco-and in wild-type flies. Statistical analyses of these datasets reveal that the random and biased connectivity pattern observed between Kenyon cells and projection neurons forms normally in the absence of most olfactory sensory activity. This finding supports the idea that even comparatively subtle, population-level patterns of neuronal connectivity can be encoded by fixed genetic programs and are likely to be the result of evolved prioritization of ecologically and ethologically salient stimuli.

Structured sampling of olfactory input by the fly mushroom body

Associative memory formation and recall in the fruit fly Drosophila melanogaster is subserved by the mushroom body (MB). Upon arrival in the MB, sensory information undergoes a profound transformation from broadly tuned and stereotyped odorant responses in the olfactory projection neuron (PN) layer to narrowly tuned and nonstereotyped responses in the Kenyon cells (KCs). Theory and experiment suggest that this transformation is implemented by random connectivity between KCs and PNs. However, this hypothesis has been challenging to test, given the difficulty of mapping synaptic connections between large numbers of brain-spanning neurons. Here, we used a recent whole-brain electron microscopy volume of the adult fruit fly to map PN-to-KC connectivity at synaptic resolution. The PN-KC connectome revealed unexpected structure, with preponderantly food-responsive PN types converging at above-chance levels on downstream KCs. Axons of the overconvergent PN types tended to arborize near one another in the MB main calyx, making local KC dendrites more likely to receive input from those types. Overconvergent PN types preferentially co-arborize and connect with dendrites of αβ and α'β' KC subtypes. Computational simulation of the observed network showed degraded discrimination performance compared with a random network, except when all signal flowed through the overconvergent, primarily food-responsive PN types. Additional theory and experiment will be needed to fully characterize the impact of the observed non-random network structure on associative memory formation and recall.

Not so spontaneous: Multi-dimensional representations of behaviors and context in sensory areas

Sensory areas are spontaneously active in the absence of sensory stimuli. This spontaneous activity has long been studied; however, its functional role remains largely unknown. Recent advances in technology, allowing large-scale neural recordings in the awake and behaving animal, have transformed our understanding of spontaneous activity. Studies using these recordings have discovered high-dimensional spontaneous activity patterns, correlation between spontaneous activity and behavior, and dissimilarity between spontaneous and sensory-driven activity patterns. These findings are supported by evidence from developing animals, where a transition toward these characteristics is observed as the circuit matures, as well as by evidence from mature animals across species. These newly revealed characteristics call for the formulation of a new role for spontaneous activity in neural sensory computation.

Pairing-Dependent Plasticity in a Dissected Fly Brain Is Input-Specific and Requires Synaptic CaMKII Enrichment and Nighttime Sleep

In Drosophila, in vivo functional imaging studies revealed that associative memory formation is coupled to a cascade of neural plasticity events in distinct compartments of the mushroom body (MB). In-depth investigation of the circuit dynamics, however, will require an ex vivo model that faithfully mirrors these events to allow direct manipulations of circuit elements that are inaccessible in the intact fly. The current ex vivo models have been able to reproduce the fundamental plasticity of aversive short-term memory, a potentiation of the MB intrinsic neuron (Kenyon cells [KCs]) responses after artificial learning ex vivo However, this potentiation showed different localization and encoding properties from those reported in vivo and failed to generate the previously reported suppression plasticity in the MB output neurons (MBONs). Here, we develop an ex vivo model using the female Drosophila brain that recapitulates behaviorally evoked plasticity in the KCs and MBONs. We demonstrate that this plasticity accurately localizes to the MB α'3 compartment and is encoded by a coincidence between KC activation and dopaminergic input. The formed plasticity is input-specific, requiring pairing of the conditioned stimulus and unconditioned stimulus pathways; hence, we name it pairing-dependent plasticity. Pairing-dependent plasticity formation requires an intact CaMKII gene and is blocked by previous-night sleep deprivation but is rescued by rebound sleep. In conclusion, we show that our ex vivo preparation recapitulates behavioral and imaging results from intact animals and can provide new insights into mechanisms of memory formation at the level of molecules, circuits, and brain state.SIGNIFICANCE STATEMENT The mammalian ex vivo LTP model enabled in-depth investigation of the hippocampal memory circuit. We develop a parallel model to study the Drosophila mushroom body (MB) memory circuit. Pairing activation of the conditioned stimulus and unconditioned stimulus pathways in dissected brains induces a potentiation pairing-dependent plasticity (PDP) in the axons of α'β' Kenyon cells and a suppression PDP in the dendrites of their postsynaptic MB output neurons, localized in the MB α'3 compartment. This PDP is input-specific and requires the 3' untranslated region of CaMKII Interestingly, ex vivo PDP carries information about the animal's experience before dissection; brains from sleep-deprived animals fail to form PDP, whereas those from animals who recovered 2 h of their lost sleep form PDP.

Shedding Light on Inter-Individual Variability of Olfactory Circuits in Drosophila

Inter-individual differences in behavioral responses, anatomy or functional properties of neuronal populations of animals having the same genotype were for a long time disregarded. The majority of behavioral studies were conducted at a group level, and usually the mean behavior of all individuals was considered. Similarly, in neurophysiological studies, data were pooled and normalized from several individuals. This approach is mostly suited to map and characterize stereotyped neuronal properties between individuals, but lacks the ability to depict inter-individual variability regarding neuronal wiring or physiological characteristics. Recent studies have shown that behavioral biases and preferences to olfactory stimuli can vary significantly among individuals of the same genotype. The origin and the benefit of these diverse "personalities" is still unclear and needs to be further investigated. A perspective taken into account the inter-individual differences is needed to explore the cellular mechanisms underlying this phenomenon. This review focuses on olfaction in the vinegar fly Drosophila melanogaster and summarizes previous and recent studies on odor-guided behavior and the underlying olfactory circuits in the light of inter-individual variability. We address the morphological and physiological variabilities present at each layer of the olfactory circuitry and attempt to link them to individual olfactory behavior. Additionally, we discuss the factors that might influence individuality with regard to olfactory perception.

Translational relevance of forward genetic screens in animal models for the study of psychiatric disease

Psychiatric disorders represent a significant burden in our societies. Despite the convincing evidence pointing at gene and gene-environment interaction contributions, the role of genetics in the etiology of psychiatric disease is still poorly understood. Forward genetic screens in animal models have helped elucidate causal links. Here we discuss the application of mutagenesis-based forward genetic approaches in common animal model species: two invertebrates, nematodes (Caenorhabditis elegans) and fruit flies (Drosophila sp.); and two vertebrates, zebrafish (Danio rerio) and mice (Mus musculus), in relation to psychiatric disease. We also discuss the use of large scale genomic studies in human populations. Despite the advances using data from human populations, animal models coupled with next-generation sequencing strategies are still needed. Although with its own limitations, zebrafish possess characteristics that make them especially well-suited to forward genetic studies exploring the etiology of psychiatric disorders.

Sleep deprivation results in diverse patterns of synaptic scaling across the Drosophila mushroom bodies

Sleep is essential for a variety of plastic processes, including learning and memory. However, the consequences of insufficient sleep on circuit connectivity remain poorly understood. To better appreciate the effects of sleep loss on synaptic connectivity across a memory-encoding circuit, we examined changes in the distribution of synaptic markers in the Drosophila mushroom body (MB). Protein-trap tags for active zone components indicate that recent sleep time is inversely correlated with Bruchpilot (BRP) abundance in the MB lobes; sleep loss elevates BRP while sleep induction reduces BRP across the MB. Overnight sleep deprivation also elevated levels of dSyd-1 and Cacophony, but not other pre-synaptic proteins. Cell-type-specific genetic reporters show that MB-intrinsic Kenyon cells (KCs) exhibit increased pre-synaptic BRP throughout the axonal lobes after sleep deprivation; similar increases were not detected in projections from large interneurons or dopaminergic neurons that innervate the MB. These results indicate that pre-synaptic plasticity in KCs is responsible for elevated levels of BRP in the MB lobes of sleep-deprived flies. Because KCs provide synaptic inputs to several classes of post-synaptic partners, we next used a fluorescent reporter for synaptic contacts to test whether each class of KC output connections is scaled uniformly by sleep loss. The KC output synapses that we observed here can be divided into three classes: KCs to MB interneurons; KCs to dopaminergic neurons; and KCs to MB output neurons. No single class showed uniform scaling across each constituent member, indicating that different rules may govern plasticity during sleep loss across cell types.

The relationship between birth timing, circuit wiring, and physiological response properties of cerebellar granule cells

Cerebellar granule cells (GrCs) are usually regarded as a uniform cell type that collectively expands the coding space of the cerebellum by integrating diverse combinations of mossy fiber inputs. Accordingly, stable molecularly or physiologically defined GrC subtypes within a single cerebellar region have not been reported. The only known cellular property that distinguishes otherwise homogeneous GrCs is the correspondence between GrC birth timing and the depth of the molecular layer to which their axons project. To determine the role birth timing plays in GrC wiring and function, we developed genetic strategies to access early- and late-born GrCs. We initiated retrograde monosynaptic rabies virus tracing from control (birth timing unrestricted), early-born, and late-born GrCs, revealing the different patterns of mossy fiber input to GrCs in vermis lobule 6 and simplex, as well as to early- and late-born GrCs of vermis lobule 6: sensory and motor nuclei provide more input to early-born GrCs, while basal pontine and cerebellar nuclei provide more input to late-born GrCs. In vivo multidepth two-photon Ca2+ imaging of axons of early- and late-born GrCs revealed representations of diverse task variables and stimuli by both populations, with modest differences in the proportions encoding movement, reward anticipation, and reward consumption. Our results suggest neither organized parallel processing nor completely random organization of mossy fiber→GrC circuitry but instead a moderate influence of birth timing on GrC wiring and encoding. Our imaging data also provide evidence that GrCs can represent generalized responses to aversive stimuli, in addition to recently described reward representations.

The Role of Dopamine in Associative Learning in Drosophila: An Updated Unified Model

Learning to associate a positive or negative experience with an unrelated cue after the presentation of a reward or a punishment defines associative learning. The ability to form associative memories has been reported in animal species as complex as humans and as simple as insects and sea slugs. Associative memory has even been reported in tardigrades [1], species that diverged from other animal phyla 500 million years ago. Understanding the mechanisms of memory formation is a fundamental goal of neuroscience research. In this article, we work on resolving the current contradictions between different Drosophila associative memory circuit models and propose an updated version of the circuit model that predicts known memory behaviors that current models do not. Finally, we propose a model for how dopamine may function as a reward prediction error signal in Drosophila, a dopamine function that is well-established in mammals but not in insects [2, 3].

Autoencoder networks extract latent variables and encode these variables in their connectomes

Advances in electron microscopy and data processing techniques are leading to increasingly large and complete microscale connectomes. At the same time, advances in artificial neural networks have produced model systems that perform comparably rich computations with perfectly specified connectivity. This raises an exciting scientific opportunity for the study of both biological and artificial neural networks: to infer the underlying circuit function from the structure of its connectivity. A potential roadblock, however, is that - even with well constrained neural dynamics - there are in principle many different connectomes that could support a given computation. Here, we define a tractable setting in which the problem of inferring circuit function from circuit connectivity can be analyzed in detail: the function of input compression and reconstruction, in an autoencoder network with a single hidden layer. Here, in general there is substantial ambiguity in the weights that can produce the same circuit function, because largely arbitrary changes to input weights can be undone by applying the inverse modifications to the output weights. However, we use mathematical arguments and simulations to show that adding simple, biologically motivated regularization of connectivity resolves this ambiguity in an interesting way: weights are constrained such that the latent variable structure underlying the inputs can be extracted from the weights by using nonlinear dimensionality reduction methods.

Dopamine modulation of sensory processing and adaptive behavior in flies

Behavioral flexibility for appropriate action selection is an advantage when animals are faced with decisions that will determine their survival or death. In order to arrive at the right decision, animals evaluate information from their external environment, internal state, and past experiences. How these different signals are integrated and modulated in the brain, and how context- and state-dependent behavioral decisions are controlled are poorly understood questions. Studying the molecules that help convey and integrate such information in neural circuits is an important way to approach these questions. Many years of work in different model organisms have shown that dopamine is a critical neuromodulator for (reward based) associative learning. However, recent findings in vertebrates and invertebrates have demonstrated the complexity and heterogeneity of dopaminergic neuron populations and their functional implications in many adaptive behaviors important for survival. For example, dopaminergic neurons can integrate external sensory information, internal and behavioral states, and learned experience in the decision making circuitry. Several recent advances in methodologies and the availability of a synaptic level connectome of the whole-brain circuitry of Drosophila melanogaster make the fly an attractive system to study the roles of dopamine in decision making and state-dependent behavior. In particular, a learning and memory center-the mushroom body-is richly innervated by dopaminergic neurons that enable it to integrate multi-modal information according to state and context, and to modulate decision-making and behavior.

Untangling the wires: development of sparse, distributed connectivity in the mushroom body calyx

Appropriate perception and representation of sensory stimuli pose an everyday challenge to the brain. In order to represent the wide and unpredictable array of environmental stimuli, principle neurons of associative learning regions receive sparse, combinatorial sensory inputs. Despite the broad role of such networks in sensory neural circuits, the developmental mechanisms underlying their emergence are not well understood. As mammalian sensory coding regions are numerically complex and lack the accessibility of simpler invertebrate systems, we chose to focus this review on the numerically simpler, yet functionally similar, Drosophila mushroom body calyx. We bring together current knowledge about the cellular and molecular mechanisms orchestrating calyx development, in addition to drawing insights from literature regarding construction of sparse wiring in the mammalian cerebellum. From this, we formulate hypotheses to guide our future understanding of the development of this critical perceptual center.

Auditory activity is diverse and widespread throughout the central brain of Drosophila

Sensory pathways are typically studied by starting at receptor neurons and following postsynaptic neurons into the brain. However, this leads to a bias in analyses of activity toward the earliest layers of processing. Here, we present new methods for volumetric neural imaging with precise across-brain registration to characterize auditory activity throughout the entire central brain of Drosophila and make comparisons across trials, individuals and sexes. We discover that auditory activity is present in most central brain regions and in neurons responsive to other modalities. Auditory responses are temporally diverse, but the majority of activity is tuned to courtship song features. Auditory responses are stereotyped across trials and animals in early mechanosensory regions, becoming more variable at higher layers of the putative pathway, and this variability is largely independent of ongoing movements. This study highlights the power of using an unbiased, brain-wide approach for mapping the functional organization of sensory activity.

Structural and Functional Plasticity in the Regenerating Olfactory System of the Migratory Locust

Regeneration after injury is accompanied by transient and lasting changes in the neuroarchitecture of the nervous system and, thus, a form of structural plasticity. In this review, we introduce the olfactory pathway of a particular insect as a convenient model to visualize neural regeneration at an anatomical level and study functional recovery at an electrophysiological level. The olfactory pathway of the locust (Locusta migratoria) is characterized by a multiglomerular innervation of the antennal lobe by olfactory receptor neurons. These olfactory afferents were axotomized by crushing the base of the antenna. The resulting degeneration and regeneration in the antennal lobe could be quantified by size measurements, dye labeling, and immunofluorescence staining of cell surface proteins implicated in axonal guidance during development. Within 3 days post lesion, the antennal lobe volume was reduced by 30% and from then onward regained size back to normal by 2 weeks post injury. The majority of regenerating olfactory receptor axons reinnervated the glomeruli of the antennal lobe. A few regenerating axons project erroneously into the mushroom body on a pathway that is normally chosen by second-order projection neurons. Based on intracellular responses of antennal lobe output neurons to odor stimulation, regenerated fibers establish functional synapses again. Following complete absence after nerve crush, responses to odor stimuli return to control level within 10–14 days. On average, regeneration of afferents, and re-established synaptic connections appear faster in younger fifth instar nymphs than in adults. The initial degeneration of olfactory receptor axons has a trans-synaptic effect on a second order brain center, leading to a transient size reduction of the mushroom body calyx. Odor-evoked oscillating field potentials, absent after nerve crush, were restored in the calyx, indicative of regenerative processes in the network architecture. We conclude that axonal regeneration in the locust olfactory system appears to be possible, precise, and fast, opening an avenue for future mechanistic studies. As a perspective of biomedical importance, the current evidence for nitric oxide/cGMP signaling as positive regulator of axon regeneration in connectives of the ventral nerve cord is considered in light of particular regeneration studies in vertebrate central nervous systems.

BAcTrace, a tool for retrograde tracing of neuronal circuits in Drosophila

Animal behavior is encoded in neuronal circuits in the brain. To elucidate the function of these circuits, it is necessary to identify, record from and manipulate networks of connected neurons. Here we present BAcTrace (Botulinum Activated Tracer), a genetically encoded, retro-grade, transsynaptic labelling system. BAcTrace is based on C. botulinum neurotoxin A, Botox, which we have engineered to travel retrogradely between neurons to activate an otherwise silent transcription factor. We validate BAcTrace at three neuronal connections in the Drosophila olfactory system. We show that BAcTrace-mediated labeling allows electrophysiological recordings of connected neurons. Finally, in a challenging circuit with highly divergent connections, BAcTrace correctly identifies 12 out of 16 connections, which were previously observed by electron microscopy.

Synthesis of Conserved Odor Object Representations in a Random, Divergent-Convergent Network

Animals are capable of recognizing mixtures and groups of odors as a unitary object. However, how odor object representations are generated in the brain remains elusive. Here, we investigate sensory transformation between the primary olfactory center and its downstream region, the mushroom body (MB), in Drosophila and show that clustered representations for mixtures and groups of odors emerge in the MB at the population and single-cell levels. Decoding analyses demonstrate that neurons selective for mixtures and groups enhance odor generalization. Responses of these neurons and those selective for individual odors all emerge in an experimentally well-constrained model implementing divergent-convergent, random connectivity between the primary center and the MB. Furthermore, we found that relative odor representations are conserved across animals despite this random connectivity. Our results show that the generation of distinct representations for individual odors and groups and mixtures of odors in the MB can be understood in a unified computational and mechanistic framework.

Everyone on Their Own! Individualization of Synaptic Boutons

Animals associate different odors with good and bad memories. This memory formation requires high neuronal plasticity. In this issue of Neuron, Bilz et al. (2020) demonstrate that associative learning modulates individual synaptic boutons of the intrinsic neurons of the memory center of Drosophila.

Flexible neural connectivity under constraints on total connection strength

Neural computation is determined by neurons’ dynamics and circuit connectivity. Uncertain and dynamic environments may require neural hardware to adapt to different computational tasks, each requiring different connectivity configurations. At the same time, connectivity is subject to a variety of constraints, placing limits on the possible computations a given neural circuit can perform. Here we examine the hypothesis that the organization of neural circuitry favors computational flexibility: that it makes many computational solutions available, given physiological constraints. From this hypothesis, we develop models of connectivity degree distributions based on constraints on a neuron’s total synaptic weight. To test these models, we examine reconstructions of the mushroom bodies from the first instar larva and adult Drosophila melanogaster. We perform a Bayesian model comparison for two constraint models and a random wiring null model. Overall, we find that flexibility under a homeostatically fixed total synaptic weight describes Kenyon cell connectivity better than other models, suggesting a principle shaping the apparently random structure of Kenyon cell wiring. Furthermore, we find evidence that larval Kenyon cells are more flexible earlier in development, suggesting a mechanism whereby neural circuits begin as flexible systems that develop into specialized computational circuits.

High-throughput electron microscopic anatomical experiments have begun to yield detailed maps of neural circuit connectivity. Uncovering the principles that govern these circuit structures is a major challenge for systems neuroscience. Healthy neural circuits must be able to perform computational tasks while satisfying physiological constraints. Those constraints can restrict a neuron’s possible connectivity, and thus potentially restrict its computation. Here we examine simple models of constraints on total synaptic weights, and calculate the number of circuit configurations they allow: a simple measure of their computational flexibility. We propose probabilistic models of connectivity that weight the number of synaptic partners according to computational flexibility under a constraint and test them using recent wiring diagrams from a learning center, the mushroom body, in the fly brain. We compare constraints that fix or bound a neuron’s total connection strength to a simple random wiring null model. Of these models, the fixed total connection strength matched the overall connectivity best in mushroom bodies from both larval and adult flies. We also provide evidence suggesting that neural circuits are more flexible in early stages of development and lose this flexibility as they grow towards specialized function.

Extrinsic activin signaling cooperates with an intrinsic temporal program to increase mushroom body neuronal diversity

Temporal patterning of neural progenitors leads to the sequential production of diverse neurons. To understand how extrinsic cues influence intrinsic temporal programs, we studied Drosophila mushroom body progenitors (neuroblasts) that sequentially produce only three neuronal types: γ, then α’β’, followed by αβ. Opposing gradients of two RNA-binding proteins Imp and Syp comprise the intrinsic temporal program. Extrinsic activin signaling regulates the production of α’β’ neurons but whether it affects the intrinsic temporal program was not known. We show that the activin ligand Myoglianin from glia regulates the temporal factor Imp in mushroom body neuroblasts. Neuroblasts missing the activin receptor Baboon have a delayed intrinsic program as Imp is higher than normal during the α’β’ temporal window, causing the loss of α’β’ neurons, a decrease in αβ neurons, and a likely increase in γ neurons, without affecting the overall number of neurons produced. Our results illustrate that an extrinsic cue modifies an intrinsic temporal program to increase neuronal diversity.

Evolution of olfactory circuits in insects

Recent years have seen an explosion of interest in the evolution of neural circuits. Comparison of animals from different families, orders, and phyla reveals fascinating variation in brain morphology, circuit structure, and neural cell types. However, it can be difficult to connect the complex changes that occur across long evolutionary distances to behavior. Luckily, these changes accumulate through processes that should also be observable in recent time, making more tractable comparisons of closely related species relevant and complementary. Here, we review several decades of research on the evolution of insect olfactory circuits across short evolutionary time scales. We describe two well-studied systems, Drosophila sechellia flies and Heliothis moths, in detailed case studies. We then move through key types of circuit evolution, cataloging examples from other insects and looking for general patterns. The literature is dominated by changes in sensory neuron number and tuning at the periphery-often enhancing neural response to odorants with new ecological or social relevance. However, changes in the way olfactory information is processed by central circuits is clearly important in a few cases, and we suspect the development of genetic tools in non-model species will reveal a broad role for central circuit evolution. Moving forward, such tools should also be used to rigorously test causal links between brain evolution and behavior.

Visualization of a Distributed Synaptic Memory Code in the Drosophila Brain

During associative conditioning, animals learn which sensory cues are predictive for positive or negative conditions. Because sensory cues are encoded by distributed neurons, one has to monitor plasticity across many synapses to capture how learned information is encoded. We analyzed synaptic boutons of Kenyon cells of the Drosophila mushroom body γ lobe, a brain structure that mediates olfactory learning. A fluorescent Ca²⁺ sensor was expressed in single Kenyon cells so that axonal boutons could be assigned to distinct cells and Ca²⁺ could be measured across many animals. Learning induced directed synaptic plasticity in specific compartments along the axons. Moreover, we show that odor-evoked Ca²⁺ dynamics across boutons decorrelate as a result of associative learning. Information theory indicates that learning renders the stimulus representation more distinct compared with naive stimuli. These data reveal that synaptic boutons rather than cells act as individually modifiable units, and coherence among them is a memory-encoding parameter.

Multiple network properties overcome random connectivity to enable stereotypic sensory responses

Connections between neuronal populations may be genetically hardwired or random. In the insect olfactory system, projection neurons of the antennal lobe connect randomly to Kenyon cells of the mushroom body. Consequently, while the odor responses of the projection neurons are stereotyped across individuals, the responses of the Kenyon cells are variable. Surprisingly, downstream of Kenyon cells, mushroom body output neurons show stereotypy in their responses. We found that the stereotypy is enabled by the convergence of inputs from many Kenyon cells onto an output neuron, and does not require learning. The stereotypy emerges in the total response of the Kenyon cell population using multiple odor-specific features of the projection neuron responses, benefits from the nonlinearity in the transfer function, depends on the convergence:randomness ratio, and is constrained by sparseness. Together, our results reveal the fundamental mechanisms and constraints with which convergence enables stereotypy in sensory responses despite random connectivity.

Stochastic and Arbitrarily Generated Input Patterns to the Mushroom Bodies Can Serve as Conditioned Stimuli in Drosophila

Single neurons in the brains of insects often have individual genetic identities and can be unambiguously identified between animals. The overall neuronal connectivity is also genetically determined and hard-wired to a large degree. Experience-dependent structural and functional plasticity is believed to be superimposed onto this more-or-less fixed connectome. However, in Drosophila melanogaster, it has been shown that the connectivity between the olfactory projection neurons (OPNs) and Kenyon cells, the intrinsic neurons of the mushroom body, is highly stochastic and idiosyncratic between individuals. Ensembles of distinctly and sparsely activated Kenyon cells represent information about the identity of the olfactory input, and behavioral relevance can be assigned to this representation in the course of associative olfactory learning. Previously, we showed that in the absence of any direct sensory input, artificially and stochastically activated groups of Kenyon cells could be trained to encode aversive cues when their activation coincided with aversive stimuli. Here, we have tested the hypothesis that the mushroom body can learn any stochastic neuronal input pattern as behaviorally relevant, independent of its exact origin. We show that fruit flies can learn thermogenetically generated, stochastic activity patterns of OPNs as conditioned stimuli, irrespective of glomerular identity, the innate valence that the projection neurons carry, or inter-hemispheric symmetry.

Presynaptic developmental plasticity allows robust sparse wiring of the Drosophila mushroom body

In order to represent complex stimuli, principle neurons of associative learning regions receive combinatorial sensory inputs. Density of combinatorial innervation is theorized to determine the number of distinct stimuli that can be represented and distinguished from one another, with sparse innervation thought to optimize the complexity of representations in networks of limited size. How the convergence of combinatorial inputs to principle neurons of associative brain regions is established during development is unknown. Here, we explore the developmental patterning of sparse olfactory inputs to Kenyon cells of the Drosophila melanogaster mushroom body. By manipulating the ratio between pre- and post-synaptic cells, we find that postsynaptic Kenyon cells set convergence ratio: Kenyon cells produce fixed distributions of dendritic claws while presynaptic processes are plastic. Moreover, we show that sparse odor responses are preserved in mushroom bodies with reduced cellular repertoires, suggesting that developmental specification of convergence ratio allows functional robustness.

Neuronal mechanisms underlying innate and learned olfactory processing in Drosophila

Olfaction allows animals to adapt their behavior in response to different chemical cues in their environment. How does the brain efficiently discriminate different odors to drive appropriate behavior, and how does it flexibly assign value to odors to adjust behavior according to experience? This review traces neuronal mechanisms underlying these processes in adult Drosophila melanogaster from olfactory receptors to higher brain centers. We highlight neural circuit principles such as lateral inhibition, segregation and integration of olfactory channels, temporal accumulation of sensory evidence, and compartmentalized synaptic plasticity underlying associative memory.

Constraining computational models using electron microscopy wiring diagrams

Numerous efforts to generate "connectomes," or synaptic wiring diagrams, of large neural circuits or entire nervous systems are currently underway. These efforts promise an abundance of data to guide theoretical models of neural computation and test their predictions. However, there is not yet a standard set of tools for incorporating the connectivity constraints that these datasets provide into the models typically studied in theoretical neuroscience. This article surveys recent approaches to building models with constrained wiring diagrams and the insights they have provided. It also describes challenges and the need for new techniques to scale these approaches to ever more complex datasets.

Functional and anatomical specificity in a higher olfactory centre

Most sensory systems are organized into parallel neuronal pathways that process distinct aspects of incoming stimuli. In the insect olfactory system, second order projection neurons target both the mushroom body, required for learning, and the lateral horn (LH), proposed to mediate innate olfactory behavior. Mushroom body neurons form a sparse olfactory population code, which is not stereotyped across animals. In contrast, odor coding in the LH remains poorly understood. We combine genetic driver lines, anatomical and functional criteria to show that the Drosophila LH has ~1400 neurons and >165 cell types. Genetically labeled LHNs have stereotyped odor responses across animals and on average respond to three times more odors than single projection neurons. LHNs are better odor categorizers than projection neurons, likely due to stereotyped pooling of related inputs. Our results reveal some of the principles by which a higher processing area can extract innate behavioral significance from sensory stimuli.

Mechanisms of Sensory Discrimination: Insights from Drosophila Olfaction

All an animal can do to infer the state of its environment is to observe the sensory-evoked activity of its own neurons. These inferences about the presence, quality, or similarity of objects are probabilistic and inform behavioral decisions that are often made in close to real time. Neural systems employ several strategies to facilitate sensory discrimination: Biophysical mechanisms separate the neuronal response distributions in coding space, compress their variances, and combine information from sequential observations. We review how these strategies are implemented in the olfactory system of the fruit fly. The emerging principles of odor discrimination likely apply to other neural circuits of similar architecture.

Re-evaluating Circuit Mechanisms Underlying Pattern Separation

When animals interact with complex environments, their neural circuits must separate overlapping patterns of activity that represent sensory and motor information. Pattern separation is thought to be a key function of several brain regions, including the cerebellar cortex, insect mushroom body, and dentate gyrus. However, recent findings have questioned long-held ideas on how these circuits perform this fundamental computation. Here, we re-evaluate the functional and structural mechanisms underlying pattern separation. We argue that the dimensionality of the space available for population codes representing sensory and motor information provides a common framework for understanding pattern separation. We then discuss how these three circuits use different strategies to separate activity patterns and facilitate associative learning in the presence of trial-to-trial variability.

A stochastic framework to model axon interactions within growing neuronal populations

The confined and crowded environment of developing brains imposes spatial constraints on neuronal cells that have evolved individual and collective strategies to optimize their growth. These include organizing neurons into populations extending their axons to common target territories. How individual axons interact with each other within such populations to optimize innervation is currently unclear and difficult to analyze experimentally in vivo. Here, we developed a stochastic model of 3D axon growth that takes into account spatial environmental constraints, physical interactions between neighboring axons, and branch formation. This general, predictive and robust model, when fed with parameters estimated on real neurons from the Drosophila brain, enabled the study of the mechanistic principles underlying the growth of axonal populations. First, it provided a novel explanation for the diversity of growth and branching patterns observed in vivo within populations of genetically identical neurons. Second, it uncovered that axon branching could be a strategy optimizing the overall growth of axons competing with others in contexts of high axonal density. The flexibility of this framework will make it possible to investigate the rules underlying axon growth and regeneration in the context of various neuronal populations.

Understanding how neuronal cells establish complex circuits with specific functions within a developing brain is a major current challenge. Over the last past years, enormous progress has been done to precisely resolve brain anatomy and to dissect the mechanisms controlling the establishment of precise neuronal networks. However, due to the extreme complexity of the brain, it is still experimentally difficult to investigate in vivo how neurons interact with each other and with their physical environments to innervate target territories during development. Here, we have developed a framework that integrates a dynamic 3D mathematical model of single axonal growth with parameters estimated from neurons grown in vivo and simulations of entire populations of growing axons. The emergent properties of our model enable the study of the mechanistic principles underlying the growth of axonal population in developing brains. Specifically, our results highlight the impact of mechanical interactions on both individual and collective axon growth, and uncover how branching regulate this process.

Structural aspects of plasticity in the nervous system of Drosophila

Neurons extend and retract dynamically their neurites during development to form complex morphologies and to reach out to their appropriate synaptic partners. Their capacity to undergo structural rearrangements is in part maintained during adult life when it supports the animal's ability to adapt to a changing environment or to form lasting memories. Nonetheless, the signals triggering structural plasticity and the mechanisms that support it are not yet fully understood at the molecular level. Here, we focus on the nervous system of the fruit fly to ask to which extent activity modulates neuronal morphology and connectivity during development. Further, we summarize the evidence indicating that the adult nervous system of flies retains some capacity for structural plasticity at the synaptic or circuit level. For simplicity, we selected examples mostly derived from studies on the visual system and on the mushroom body, two regions of the fly brain with extensively studied neuroanatomy.

Odor Perception on the Two Sides of the Brain: Consistency Despite Randomness

Neurons in piriform cortex receive input from a random collection of glomeruli, resulting in odor representations that lack the stereotypic organization of the olfactory bulb. We have performed in vivo optical imaging and mathematical modeling to demonstrate that correlations are retained in the transformation from bulb to piriform cortex, a feature essential for generalization across odors. Random connectivity also implies that the piriform representation of a given odor will differ among different individuals and across brain hemispheres in a single individual. We show that these different representations can nevertheless support consistent agreement about odor quality across a range of odors. Our model also demonstrates that, whereas odor discrimination and categorization require far fewer neurons than reside in piriform cortex, consistent generalization may require the full complement of piriform neurons.

Dendritic Integration of Sensory Evidence in Perceptual Decision-Making

Perceptual decisions require the accumulation of sensory information to a response criterion. Most accounts of how the brain performs this process of temporal integration have focused on evolving patterns of spiking activity. We report that subthreshold changes in membrane voltage can represent accumulating evidence before a choice. αβ core Kenyon cells (αβ_c KCs) in the mushroom bodies of fruit flies integrate odor-evoked synaptic inputs to action potential threshold at timescales matching the speed of olfactory discrimination. The forkhead box P transcription factor (FoxP) sets neuronal integration and behavioral decision times by controlling the abundance of the voltage-gated potassium channel Shal (K_V4) in αβ_c KC dendrites. αβ_c KCs thus tailor, through a particular constellation of biophysical properties, the generic process of synaptic integration to the demands of sequential sampling.

Aversive olfactory associative memory loses odor specificity over time

Avoiding associatively learned predictors of danger is crucial for survival. Aversive memories can, however, become counter-adaptive when they are overly generalized to harmless cues and contexts. In a fruit fly odor–electric shock associative memory paradigm, we found that learned avoidance lost its specificity for the trained odor and became general to novel odors within a day of training. We discuss the possible neural circuit mechanisms of this effect and highlight the parallelism to over-generalization of learned fear behavior after an incubation period in rodents and humans, with due relevance for post-traumatic stress disorder.

Highlighted Article: Associative memories of noxious experiences can become detrimental if overly generalized; fruit fly aversive memories lose their specificity over time, mimicking the situation in rodents and humans.