Drosophila olfactory memory: single genes to complex neural circuits

A central goal of neuroscience is to understand how neural circuits encode memory and guide behaviour. Studying simple, genetically tractable organisms, such as Drosophila melanogaster, can illuminate principles of neural circuit organization and function. Early genetic dissection of D. melanogaster olfactory memory focused on individual genes and molecules. These molecular tags subsequently revealed key neural circuits for memory. Recent advances in genetic technology have allowed us to manipulate and observe activity in these circuits, and even individual neurons, in live animals. The studies have transformed D. melanogaster from a useful organism for gene discovery to an ideal model to understand neural circuit function in memory.     

Contemporary approaches to neural circuit manipulation and mapping: focus on reward and addiction

Tying complex psychological processes to precisely defined neural circuits is a major goal of systems and behavioural neuroscience. This is critical for understanding adaptive behaviour, and also how neural systems are altered in states of psychopathology, such as addiction. Efforts to relate psychological processes relevant to addiction to activity within defined neural circuits have been complicated by neural heterogeneity. Recent advances in technology allow for manipulation and mapping of genetically and anatomically defined neurons, which when used in concert with sophisticated behavioural models, have the potential to provide great insight into neural circuit bases of behaviour. Here we discuss contemporary approaches for understanding reward and addiction, with a focus on midbrain dopamine and cortico-striato-pallidal circuits.     

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.     

Songbird: a unique animal model for studying the molecular basis of disorders of vocal development and communication

Like humans, songbirds are one of the few animal groups that learn vocalization. Vocal learning requires coordination of auditory input and vocal output using auditory feedback to guide one’s own vocalizations during a specific developmental stage known as the critical period. Songbirds are good animal models for understand the neural basis of vocal learning, a complex form of imitation, because they have many parallels to humans with regard to the features of vocal behavior and neural circuits dedicated to vocal learning. In this review, we will summarize the behavioral, neural, and genetic traits of birdsong. We will also discuss how studies of birdsong can help us understand how the development of neural circuits for vocal learning and production is driven by sensory input (auditory information) and motor output (vocalization).     

Development of circuits that generate simple rhythmic behaviors in vertebrates

Neurobiologists have long sought to understand how circuits in the nervous system are organized to generate the precise neural outputs that underlie particular behaviors. Given the complexity of the nervous system in higher vertebrates this is a daunting task. Nevertheless, recent advances in developmental genetics hold out hope that studies of locomotor and respiratory circuits will provide general insight for understanding how ensembles of neurons are wired to control specific behaviors.     

Dynamics on the manifold: Identifying computational dynamical activity from neural population recordings

The question of how the collective activity of neural populations gives rise to complex behaviour is fundamental to neuroscience. At the core of this question lie considerations about how neural circuits can perform computations that enable sensory perception, decision making, and motor control. It is thought that such computations are implemented through the dynamical evolution of distributed activity in recurrent circuits. Thus, identifying dynamical structure in neural population activity is a key challenge towards a better understanding of neural computation. At the same time, interpreting this structure in light of the computation of interest is essential for linking the time-varying activity patterns of the neural population to ongoing computational processes. Here, we review methods that aim to quantify structure in neural population recordings through a dynamical system defined in a low-dimensional latent variable space. We discuss advantages and limitations of different modelling approaches and address future challenges for the field.     

Neurodynamics: nonlinear dynamics and neurobiology

The use of methods from contemporary nonlinear dynamics in studying neurobiology has been rather limited.Yet, nonlinear dynamics has become a practical tool for analyzing data and verifying models. This has led to productive coupling of nonlinear dynamics with experiments in neurobiology in which the neural circuits are forced with constant stimuli, with slowly varying stimuli, with periodic stimuli, and with more complex information-bearing stimuli. Analysis of these more complex stimuli of neural circuits goes to the heart of how one is to understand the encoding and transmission of information by nervous systems.     

Neural networks for perceptual processing: from simulation tools to theories

Neural networks are modelling tools that are, in principle, able to capture the input-output behaviour of arbitrary systems that may include the dynamics of animal populations or brain circuits. While a neural network model is useful if it captures phenomenologically the behaviour of the target system in this way, its utility is amplified if key mechanisms of the model can be discovered, and identified with those of the underlying system. In this review, we first describe, at a fairly high level with minimal mathematics, some of the tools used in constructing neural network models. We then go on to discuss the implications of network models for our understanding of the system they are supposed to describe, paying special attention to those models that deal with neural circuits and brain systems. We propose that neural nets are useful for brain modelling if they are viewed in a wider computational framework originally devised by Marr. Here, neural networks are viewed as an intermediate mechanistic abstraction between 'algorithm' and 'implementation', which can provide insights into biological neural representations and their putative supporting architectures.     

Transgenic technology for visualization and manipulation of the neural circuits controlling behavior in zebrafish

The vertebrate brain is innately equipped with neural circuits that make quick behavioral decisions possible. Elucidating these neural circuits, determining how their master plans are encoded in the genome, and revealing how they can be modified by postnatal experiences will facilitate our understanding of how nature and nurture interact to establish an animal's behavior. In this review, we explain how transgenic zebrafish can cast insights into the developmental mechanisms and functional roles of the neural circuits that directly and indirectly control visuomotor behavior, by taking as an example a transgenic line Tg( brn3a-hsp70:GFP ) enabling visualization of the tectobulbar and habenulo-interpeduncular tracts. These insights emphasize the benefits of applying advanced transgenic technology in zebrafish to future research into this area.     

Premotor neurons in the feeding system of Aplysia californica

Central pattern generator (CPG) circuits control cyclic motor output underlying rhythmic behaviors. Although there have been extensive behavioral and cellular studies of food-induced feeding arousal as well as satiation in Aplysia, very little is known about the neuronal circuits controlling rhythmic consummatory feeding behavior. However, recent studies have identified premotor neurons that initiate and maintain buccal motor programs underlying ingestion and egestion in Aplysia. Other newly identified neurons receive synaptic input from feeding CPGs and in turn synapse with and control the output of buccal motor neurons. Some of these neurons and their effects within the buccal system are modulated by endogenous neuropeptides. With this information we can begin to understand how neuronal networks control buccal motor output and how their activity is modulated to produce flexibility in observed feeding behavior.     

The neural circuits and synaptic mechanisms underlying motor initiation in C. elegans

C. elegans is widely used to dissect how neural circuits and genes generate behavior. During locomotion, worms initiate backward movement to change locomotion direction spontaneously or in response to sensory cues; however, the underlying neural circuits are not well defined. We applied a multidisciplinary approach to map neural circuits in freely behaving worms by integrating functional imaging, optogenetic interrogation, genetic manipulation, laser ablation, and electrophysiology. We found that a disinhibitory circuit and a stimulatory circuit together promote initiation of backward movement and that circuitry dynamics is differentially regulated by sensory cues. Both circuits require glutamatergic transmission but depend on distinct glutamate receptors. This dual mode of motor initiation control is found in mammals, suggesting that distantly related organisms with anatomically distinct nervous systems may adopt similar strategies for motor control. Additionally, our studies illustrate how a multidisciplinary approach facilitates dissection of circuit and synaptic mechanisms underlying behavior in a genetic model organism.     

Pollution going multimodal: the complex impact of the human-altered sensory environment on animal perception and performance

Anthropogenic sensory pollution is affecting ecosystems worldwide. Human actions generate acoustic noise, emanate artificial light and emit chemical substances. All of these pollutants are known to affect animals. Most studies on anthropogenic pollution address the impact of pollutants in unimodal sensory domains. High levels of anthropogenic noise, for example, have been shown to interfere with acoustic signals and cues. However, animals rely on multiple senses, and pollutants often co-occur. Thus, a full ecological assessment of the impact of anthropogenic activities requires a multimodal approach. We describe how sensory pollutants can co-occur and how covariance among pollutants may differ from natural situations. We review how animals combine information that arrives at their sensory systems through different modalities and outline how sensory conditions can interfere with multimodal perception. Finally, we describe how sensory pollutants can affect the perception, behaviour and endocrinology of animals within and across sensory modalities. We conclude that sensory pollution can affect animals in complex ways due to interactions among sensory stimuli, neural processing and behavioural and endocrinal feedback. We call for more empirical data on covariance among sensory conditions, for instance, data on correlated levels in noise and light pollution. Furthermore, we encourage researchers to test animal responses to a full-factorial set of sensory pollutants in the presence or the absence of ecologically important signals and cues. We realize that such approach is often time and energy consuming, but we think this is the only way to fully understand the multimodal impact of sensory pollution on animal performance and perception.     

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.     

Illuminating neural circuits and behaviour in Caenorhabditis elegans with optogenetics

The development of optogenetics, a family of methods for using light to control neural activity via light-sensitive proteins, has provided a powerful new set of tools for neurobiology. These techniques have been particularly fruitful for dissecting neural circuits and behaviour in the compact and transparent roundworm Caenorhabditis elegans. Researchers have used optogenetic reagents to manipulate numerous excitable cell types in the worm, from sensory neurons, to interneurons, to motor neurons and muscles. Here, we show how optogenetics applied to this transparent roundworm has contributed to our understanding of neural circuits.     

Dynamic functional connectivity in the static connectome of Caenorhabditis elegans

A hallmark of adaptive behavior is the ability to flexibly respond to sensory cues. To understand how neural circuits implement this flexibility, it is critical to resolve how a static anatomical connectome can be modulated such that functional connectivity in the network can be dynamically regulated. Here, we review recent work in the roundworm Caenorhabditis elegans on this topic. EM studies have mapped anatomical connectomes of many C. elegans animals, highlighting the level of stereotypy in the anatomical network. Brain-wide calcium imaging and studies of specified neural circuits have uncovered striking flexibility in the functional coupling of neurons. The coupling between neurons is controlled by neuromodulators that act over long timescales. This gives rise to persistent behavioral states that animals switch between, allowing them to generate adaptive behavioral responses across environmental conditions. Thus, the dynamic coupling of neurons enables multiple behavioral states to be encoded in a physically stereotyped connectome.     

Variability, compensation and homeostasis in neuron and network function

Neurons in most animals live a very long time relative to the half-lives of all of the proteins that govern excitability and synaptic transmission. Consequently, homeostatic mechanisms are necessary to ensure stable neuronal and network function over an animal's lifetime. To understand how these homeostatic mechanisms might function, it is crucial to understand how tightly regulated synaptic and intrinsic properties must be for adequate network performance, and the extent to which compensatory mechanisms allow for multiple solutions to the production of similar behaviour. Here, we use examples from theoretical and experimental studies of invertebrates and vertebrates to explore several issues relevant to understanding the precision of tuning of synaptic and intrinsic currents for the operation of functional neuronal circuits.     

Drosophila memory: will Orb(2) predict the future?

Genetically tractable organisms with relatively simple nervous systems offer a realistic platform to understand how and where memories are formed and stored in defined neural circuits. Recent work in Drosophila provides promise that this analysis may soon reach the resolution of identifiable synapses.     

Top-Down Modulation on Perceptual Decision with Balanced Inhibition through Feedforward and Feedback Inhibitory Neurons

Recent physiological studies have shown that neurons in various regions of the central nervous systems continuously receive noisy excitatory and inhibitory synaptic inputs in a balanced and covaried fashion. While this balanced synaptic input (BSI) is typically described in terms of maintaining the stability of neural circuits, a number of experimental and theoretical studies have suggested that BSI plays a proactive role in brain functions such as top-down modulation for executive control. Two issues have remained unclear in this picture. First, given the noisy nature of neuronal activities in neural circuits, how do the modulatory effects change if the top-down control implements BSI with different ratios between inhibition and excitation? Second, how is a top-down BSI realized via only excitatory long-range projections in the neocortex? To address the first issue, we systematically tested how the inhibition/excitation ratio affects the accuracy and reaction times of a spiking neural circuit model of perceptual decision. We defined an energy function to characterize the network dynamics, and found that different ratios modulate the energy function of the circuit differently and form two distinct functional modes. To address the second issue, we tested BSI with long-distance projection to inhibitory neurons that are either feedforward or feedback, depending on whether these inhibitory neurons do or do not receive inputs from local excitatory cells, respectively. We found that BSI occurs in both cases. Furthermore, when relying on feedback inhibitory neurons, through the recurrent interactions inside the circuit, BSI dynamically and automatically speeds up the decision by gradually reducing its inhibitory component in the course of a trial when a decision process takes too long.     

Neurogenetics and the "fly-stampede": dissecting neural circuits involved in visual behaviors

A central goal of systems neuroscience is to understand how neural circuits represent quantitative aspects of the outside world and transform these signals into the motor code for behavior. By contrast to olfactory perception in which odors are encoded by a population of ligand-binding receptors at the input stage, the visual system extracts complex information about color, form and movement from just a few types of photoreceptor inputs. The algorithms for many of these transformations are poorly understood. We designed a high throughput real-time quantitative testing system, the "fly-stampede", to evaluate behavioral responses to light and motion cues in Drosophila. With this system, we identified a neural circuit that does not participate in sensing light but is crucial for computing visual motion. When neurons of this circuit are genetically inactivated, the flies show normal walking phototaxis, but are completely motion blind. Using neurogenetics to study the circuits mediating sophisticated animal behaviors is currently a field of intense study. This extra view attempts to summarize our work within historical background of fly biocybernetics and other recent advances.