Smelling on the fly: sensory cues and strategies for olfactory navigation in Drosophila

Navigating toward (or away from) a remote odor source is a challenging problem that requires integrating olfactory information with visual and mechanosensory cues. Drosophila melanogaster is a useful organism for studying the neural mechanisms of these navigation behaviors. There are a wealth of genetic tools in this organism, as well as a history of inventive behavioral experiments. There is also a large and growing literature in Drosophila on the neural coding of olfactory, visual, and mechanosensory stimuli. Here we review recent progress in understanding how these stimulus modalities are encoded in the Drosophila nervous system. We also discuss what strategies a fly might use to navigate in a natural olfactory landscape while making use of all these sources of sensory information. We emphasize that Drosophila are likely to switch between multiple strategies for olfactory navigation, depending on the availability of various sensory cues. Finally, we highlight future research directions that will be important in understanding the neural circuits that underlie these behaviors.     

Noise-Driven Neocortical Interaction: A Simple Generation Mechanism for Complex Neuron Spiking

We discuss a generic scenario along which complex spiking behavior evolves in biologically realistic neural networks. Our nonlinear dynamics approach is based directly on rat neocortical in vitro recordings. Using this experimental data, we obtain a full overview on the possible spiking behaviors of pyramidal neurons that are engaged in binary interactions. Universality arguments imply that the observed spiking behaviors are largely independent from the specific properties of individual neurons; theoretical arguments and numerical experiments indicate that they should be observable in in vivo neocortical neuron networks.     

Genetic dissection of neural circuits

Understanding the principles of information processing in neural circuits requires systematic characterization of the participating cell types and their connections, and the ability to measure and perturb their activity. Genetic approaches promise to bring experimental access to complex neural systems, including genetic stalwarts such as the fly and mouse, but also to nongenetic systems such as primates. Together with anatomical and physiological methods, cell-type-specific expression of protein markers and sensors and transducers will be critical to construct circuit diagrams and to measure the activity of genetically defined neurons. Inactivation and activation of genetically defined cell types will establish causal relationships between activity in specific groups of neurons, circuit function, and animal behavior. Genetic analysis thus promises to reveal the logic of the neural circuits in complex brains that guide behaviors. Here we review progress in the genetic analysis of neural circuits and discuss directions for future research and development.     

Using imaging and genetics in zebrafish to study developing spinal circuits in vivo

Imaging and molecular approaches are perfectly suited to young, transparent zebrafish (Danio rerio), where they have allowed novel functional studies of neural circuits and their links to behavior. Here, we review cutting-edge optical and genetic techniques used to dissect neural circuits in vivo and discuss their application to future studies of developing spinal circuits using living zebrafish. We anticipate that these experiments will reveal general principles governing the assembly of neural circuits that control movements.     

A bird's eye view of neural circuit formation

Neural circuits underlying complex learned behaviors, such as speech in humans, develop under genetic constraints and in response to environmental influences. Little is known about the rules and mechanisms through which such circuits form. We argue that songbirds, with their discrete and well studied neural pathways underlying a complex and naturally learned behavior, provide a powerful model for addressing these questions. We briefly review current knowledge of how the song circuit develops during learning and discuss new possibilities for advancing the field given recent technological advances.     

Research progress on Drosophila visual cognition in China

Visual cognition,as one of the fundamental aspects of cognitive neuroscience,is generally associated with high-order brain functions in animals and human.Drosophila,as a model organism,shares certain features of visual cognition in common with mammals at the genetic,molecular,cellular,and even higher behavioral levels.From learning and memory to decision making,Drosophila covers a broad spectrum of higher cognitive behaviors beyond what we had expected.Armed with powerful tools of genetic manipulation in Drosophila,an increasing number of studies have been conducted in order to elucidate the neural circuit mechanisms underlying these cognitive behaviors from a genes-brain-behavior perspective.The goal of this review is to integrate the most important studies on visual cognition in Drosophila carried out in mainland China during the last decade into a body of knowledge encompassing both the basic neural operations and circuitry of higher brain function in Drosophila.Here,we consider a series of the higher cognitive behaviors beyond learning and memory,such as visual pattern recognition,feature and context generalization,different feature memory traces,salience-based decision,attention-like behavior,and cross-modal leaning and memory.We discuss the possible general gain-gating mechanism implementing by dopamine-mushroom body circuit in fly's visual cognition.We hope that our brief review on this aspect will inspire further study on visual cognition in flies,or even beyond.     

Toward molecular genetic dissection of neural circuits for emotional and motivational behaviors

How does the brain process the emotional meaning of sensory stimuli and in turn drive behavior?Studies in the mammalian systems have identified various brain regions and neurotransmitter systems that are critical for emotional and motivational behaviors and have implicated their involvement in neuropsychiatric disorders including anxiety, depression, schizophrenia, and addiction. Despite these significant advancements, the precise neural circuitry underlying emotional and motivational behaviors remains to be understood at molecular and cellular levels. In this review, we discuss how the vertebrate model organism zebrafish can help us gain insights into the underlying circuitry. We first describe studies of several simple and relevant preference behaviors in this model organism, and then discuss approaches and technologies that can be used to uncover the development and function of neural circuits underlying these behaviors.     

Deconstructing memory in Drosophila

Unlike most organ systems, which have evolved to maintain homeostasis, the brain has been selected to sense and adapt to environmental stimuli by constantly altering interactions in a gene network that functions within a larger neural network. This unique feature of the central nervous system provides a remarkable plasticity of behavior, but also makes experimental investigations challenging. Each experimental intervention ramifies through both gene and neural networks, resulting in unpredicted and sometimes confusing phenotypic adaptations. Experimental dissection of mechanisms underlying behavioral plasticity ultimately must accomplish an integration across many levels of biological organization, including genetic pathways acting within individual neurons, neural network interactions which feed back to gene function, and phenotypic observations at the behavioral level. This dissection will be more easily accomplished for model systems such as Drosophila, which, compared with mammals, have relatively simple and manipulable nervous systems and genomes. The evolutionary conservation of behavioral phenotype and the underlying gene function ensures that much of what we learn in such model systems will be relevant to human cognition. In this essay, we have not attempted to review the entire Drosophila memory field. Instead, we have tried to discuss particular findings that provide some level of intellectual synthesis across three levels of biological organization: behavior, neural circuitry and biochemical pathways. We have attempted to use this integrative approach to evaluate distinct mechanistic hypotheses, and to propose critical experiments that will advance this field.     

Evolution of motor innervation to vertebrate fins and limbs

The evolution and diversification of vertebrate behaviors associated with locomotion depend highly on the functional transformation of paired appendages. Although the evolution of fins into limbs has long been a focus of interest to scientists, the evolution of neural control during this transition has not received much attention. Recent studies have provided significant progress in the understanding of the genetic and developmental bases of the evolution of fin/limb motor circuitry in vertebrates. Here we compare the organization of the motor neurons in the spinal cord of various vertebrates. We also discuss recent advances in our understanding of these events and how they can provide a mechanistic explanation for the evolution of fin/limb motor circuitry in vertebrates.     

Dual Reward Prediction Components Yield Pavlovian Sign- and Goal-Tracking

Reinforcement learning (RL) has become a dominant paradigm for understanding animal behaviors and neural correlates of decision-making, in part because of its ability to explain Pavlovian conditioned behaviors and the role of midbrain dopamine activity as reward prediction error (RPE). However, recent experimental findings indicate that dopamine activity, contrary to the RL hypothesis, may not signal RPE and differs based on the type of Pavlovian response (e.g. sign- and goal-tracking responses). In this study, we address this discrepancy by introducing a new neural correlate for learning reward predictions; the correlate is called “cue-evoked reward”. It refers to a recall of reward evoked by the cue that is learned through simple cue-reward associations. We introduce a temporal difference learning model, in which neural correlates of the cue itself and cue-evoked reward underlie learning of reward predictions. The animal''s reward prediction supported by these two correlates is divided into sign and goal components respectively. We relate the sign and goal components to approach responses towards the cue (i.e. sign-tracking) and the food-tray (i.e. goal-tracking) respectively. We found a number of correspondences between simulated models and the experimental findings (i.e. behavior and neural responses). First, the development of modeled responses is consistent with those observed in the experimental task. Second, the model''s RPEs were similar to dopamine activity in respective response groups. Finally, goal-tracking, but not sign-tracking, responses rapidly emerged when RPE was restored in the simulated models, similar to experiments with recovery from dopamine-antagonist. These results suggest two complementary neural correlates, corresponding to the cue and its evoked reward, form the basis for learning reward predictions in the sign- and goal-tracking rats.     

Behavioral genetics in larval zebrafish: learning from the young

Deciphering the genetic code that determines how the vertebrate nervous system assembles into neural circuits that ultimately control behavior is a fascinating and challenging question in modern neurobiology. Because of the complexity of this problem, successful strategies require a simple yet focused experimental approach without limiting the scope of the discovery. Unbiased, large-scale forward genetic screens in invertebrate organisms have yielded great insight into the genetic regulation of neural circuit assembly and function. For many reasons, this highly successful approach has been difficult to recapitulate in the behavioral neuroscience field's classic vertebrate model organisms-rodents. Here, we discuss how larval zebrafish provide a promising model system to which we can apply the design of invertebrate behavior-based screens to reveal the genetic mechanisms critical for neural circuit assembly and function in vertebrates.     

Aedes-AI: Neural network models of mosquito abundance

We present artificial neural networks as a feasible replacement for a mechanistic model of mosquito abundance. We develop a feed-forward neural network, a long short-term memory recurrent neural network, and a gated recurrent unit network. We evaluate the networks in their ability to replicate the spatiotemporal features of mosquito populations predicted by the mechanistic model, and discuss how augmenting the training data with time series that emphasize specific dynamical behaviors affects model performance. We conclude with an outlook on how such equation-free models may facilitate vector control or the estimation of disease risk at arbitrary spatial scales.     

Neural mechanisms for the recognition of biological movements

The visual recognition of complex movements and actions is crucial for the survival of many species. It is important not only for communication and recognition at a distance, but also for the learning of complex motor actions by imitation. Movement recognition has been studied in psychophysical, neurophysiological and imaging experiments, and several cortical areas involved in it have been identified. We use a neurophysiologically plausible and quantitative model as a tool for organizing and making sense of the experimental data, despite their growing size and complexity. We review the main experimental findings and discuss possible neural mechanisms, and show that a learning-based, feedforward model provides a neurophysiologically plausible and consistent summary of many key experimental results.     

Motoring ahead with rodents

How neural circuits underlie the acquisition and control of learned motor behaviors has traditionally been explored in monkeys and, more recently, songbirds. The development of genetic tools for functional circuit analysis in rodents, the availability of transgenic animals with well characterized phenotypes, and the relative ease with which rats and mice can be trained to perform various motor tasks, make rodents attractive models for exploring the neural circuit mechanisms underlying the acquisition and production of learned motor skills. Here we discuss the advantages and drawbacks of this approach, review recent trends and results, and outline possible strategies for wider adoption of rodents as a model system for complex motor learning.     

Visualizing sexual dimorphism in the brain

Sexually dimorphic behaviors are likely to involve neural pathways that express the androgen receptor (AR). We have genetically modified the AR locus to visualize dimorphisms in neuronal populations that express AR. Analysis of AR-positive neurons reveals both known dimorphisms in the preoptic area of the hypothalamus and the bed nucleus of the stria terminalis as well as novel dimorphic islands in the basal forebrain with a clarity unencumbered by the vast population of AR-negative neurons. This genetic approach allows the visualization of dimorphic subpopulations of AR-positive neurons along with their projections and may ultimately permit an association between neural circuits and specific dimorphic behaviors.     

Functional neuroimaging of genetic variation in serotonergic neurotransmission

Serotonin (5-hydroxytryptamine; 5-HT) is a potent modulator of the physiology and behavior involved in generating appropriate responses to environmental cues such as danger or threat. Furthermore, genetic variation in 5-HT subsystem genes can impact upon several dimensions of emotional behavior including neuroticism and psychopathology, but especially anxiety traits. Recently, functional neuroimaging has provided a dramatic illustration of how a promoter polymorphism in the human 5-HT transporter (5-HTT) gene, which has been weakly related to these behaviors, is strongly related to the engagement of neural systems, namely the amygdala, subserving emotional processes. In this commentary, we discuss how functional neuroimaging can be used to characterize the effects of polymorphisms in 5-HT subsystem genes on the response of neural circuits underlying the generation and regulation of mood and temperament as well as susceptibility to affective illness. We argue that in time, such knowledge will allow us to not only transcend phenomenological diagnosis and represent mechanisms of disease, but also identify at-risk individuals and biological pathways for the development of new treatments.     

Perspective: reverse evolution

For some time, the reversibility of evolution was primarily discussed in terms of comparative patterns. Only recently has this problem been studied using experimental evolution over shorter evolutionary time frames. This has raised questions of definition, experimental procedure, and the hypotheses being tested. Experimental evolution has provided evidence for multiple population genetic mechanisms in reverse evolution, including pleiotropy and mutation accumulation. It has also pointed to genetic factors that might prevent reverse evolution, such as a lack of genetic variability, epistasis, and differential genotype-by-environment interactions. The main focus of this perspective is on laboratory studies and their relevance to the genetics of reverse evolution. We discuss reverse evolution experiments with Drosophila, bacterial, and viral populations. Field studies of the reverse evolution of melanism in the peppered moth are also reviewed.     

A dynamical systems view of neuroethology: Uncovering stateful computation in natural behaviors

State-dependent computation is key to cognition in both biological and artificial systems. Alan Turing recognized the power of stateful computation when he created the Turing machine with theoretically infinite computational capacity in 1936. Independently, by 1950, ethologists such as Tinbergen and Lorenz also began to implicitly embed rudimentary forms of state-dependent computation to create qualitative models of internal drives and naturally occurring animal behaviors. Here, we reformulate core ethological concepts in explicitly dynamical systems terms for stateful computation. We examine, based on a wealth of recent neural data collected during complex innate behaviors across species, the neural dynamics that determine the temporal structure of internal states. We will also discuss the degree to which the brain can be hierarchically partitioned into nested dynamical systems and the need for a multi-dimensional state-space model of the neuromodulatory system that underlies motivational and affective states.     

Viewing DVM via general behaviors of zooplankton: a way bridging the success of individual and population

In this paper, we viewed the diel vertical migration (DVM) of copepod in the context of the animal's immediate behaviors of everyday concerns and constructed an instantaneous behavioral criterion effective for DVM and non-DVM behaviors. This criterion employed the function of 'venturous revenue' (VR), which is the product of the food intake and probability of the survival, to evaluate the gains and losses of the behaviors that the copepod could trade-off. The optimal behaviors are to find the optimal habitats to maximize VR. Two types of VRs are formulated and tested by the theoretical analysis and simulations. The sensed VR, monitoring the real-time changes of trade-offs and thereby determining the optimum habitat, is validated to be the effective objective function for the optimization of the behavior; whereas, the realized VR, quantifying the actual profit obtained by an optimal copepod in the sensed-VR-determined habitat, defines the life history of a specific age cohort. The achievement of a robust copepod overwintering stock through integrating the dynamics of the constituent age cohorts subjected to the instantaneous behavioral criterion for DVM clearly exemplified a possible way bridging the immediate pursuit of an individual and the end success of the population.     

Phenotypic plasticity in female mate choice behavior is mediated by an interaction of direct and indirect genetic effects in Drosophila melanogaster

Female mate choice is a complex decision‐making process that involves many context‐dependent factors. In Drosophila melanogaster, a model species for the study of sexual selection, indirect genetic effects (IGEs) of general social interactions can influence female mate choice behaviors, but the potential impacts of IGEs associated with mating experiences are poorly understood. Here, we examined whether the IGEs associated with a previous mating experience had an effect on subsequent female mate choice behaviors and quantified the degree of additive genetic variation associated with this effect. Females from 21 different genetic backgrounds were housed with males from one of two distinct genetic backgrounds for either a short (3 hr) or long (48 hr) exposure period and their subsequent mate choice behaviors were scored. We found that the genetic identity of a previous mate significantly influenced a female's subsequent interest in males and preference of males. Additionally, a hemiclonal analysis revealed significant additive genetic variation associated with experience‐dependent mate choice behaviors, indicating a genotype‐by‐environment interaction for both of these parameters. We discuss the significance of these results with regard to the evolution of plasticity in female mate choice behaviors and the maintenance of variation in harmful male traits.