The morphology, physiology and function of suboesophageal neck motor neurons in the honeybee

Analysing Head-Thorax Choreography During Free-Flights in Bumblebees

Animals coordinate their various body parts, sometimes in elaborate manners to swim, walk, climb, fly, and navigate their environment. The coordination of body parts is essential to behaviors such as, chasing, escaping, landing, and the extraction of relevant information. For example, by shaping the movement of the head and body in an active and controlled manner, flying insects structure their flights to facilitate the acquisition of distance information. They condense their turns into a short period of time (the saccade) interspaced by a relatively long translation (the intersaccade). However, due to technological limitations, the precise coordination of the head and thorax during insects' free-flight remains unclear. Here, we propose methods to analyse the orientation of the head and thorax of bumblebees Bombus terrestris, to segregate the trajectories of flying insects into saccades and intersaccades by using supervised machine learning (ML) techniques, and finally to analyse the coordination between head and thorax by using artificial neural networks (ANN). The segregation of flights into saccades and intersaccades by ML, based on the thorax angular velocities, decreased the misclassification by 12% compared to classically used methods. Our results demonstrate how machine learning techniques can be used to improve the analyses of insect flight structures and to learn about the complexity of head-body coordination. We anticipate our assay to be a starting point for more sophisticated experiments and analysis on freely flying insects. For example, the coordination of head and body movements during collision avoidance, chasing behavior, or negotiation of gaps could be investigated by monitoring the head and thorax orientation of freely flying insects within and across behavioral tasks, and in different species.

A Model for Basic Emotions Using Observations of Behavior in Drosophila

Emotion plays a crucial role, both in general human experience and in psychiatric illnesses. Despite the importance of emotion, the relative lack of objective methodologies to scientifically studying emotional phenomena limits our current understanding and thereby calls for the development of novel methodologies, such us the study of illustrative animal models. Analysis of Drosophila and other insects has unlocked new opportunities to elucidate the behavioral phenotypes of fundamentally emotional phenomena. Here we propose an integrative model of basic emotions based on observations of this animal model. The basic emotions are internal states that are modulated by neuromodulators, and these internal states are externally expressed as certain stereotypical behaviors, such as instinct, which is proposed as ancient mechanisms of survival. There are four kinds of basic emotions: happiness, sadness, fear, and anger, which are differentially associated with three core affects: reward (happiness), punishment (sadness), and stress (fear and anger). These core affects are analogous to the three primary colors (red, yellow, and blue) in that they are combined in various proportions to result in more complex “higher order” emotions, such as love and aesthetic emotion. We refer to our proposed model of emotions as called the “Three Primary Color Model of Basic Emotions.”

Structure and development of the subesophageal zone of the Drosophila brain. II. Sensory compartments

The subesophageal zone (SEZ) of the Drosophila brain processes mechanosensory and gustatory sensory input from sensilla located on the head, mouth cavity and trunk. Motor output from the SEZ directly controls the movements involved in feeding behavior. In an accompanying paper (Hartenstein et al., ), we analyzed the systems of fiber tracts and secondary lineages to establish reliable criteria for defining boundaries between the four neuromeres of the SEZ, as well as discrete longitudinal neuropil domains within each SEZ neuromere. Here we use this anatomical framework to systematically map the sensory projections entering the SEZ throughout development. Our findings show continuity between larval and adult sensory neuropils. Gustatory axons from internal and external taste sensilla of the larva and adult form two closely related sensory projections, (a) the anterior central sensory center located deep in the ventromedial neuropil of the tritocerebrum and mandibular neuromere, and (b) the anterior ventral sensory center (AVSC), occupying a superficial layer within the ventromedial tritocerebrum. Additional, presumed mechanosensory terminal axons entering via the labial nerve define the ventromedial sensory center (VMSC) in the maxilla and labium. Mechanosensory afferents of the massive array of chordotonal organs (Johnston's organ) of the adult antenna project into the centrolateral neuropil column of the anterior SEZ, creating the antenno-mechanosensory and motor center (AMMC). Dendritic projections of dye back-filled motor neurons extend throughout a ventral layer of the SEZ, overlapping widely with the AVSC and VMSC. Our findings elucidate fundamental structural aspects of the developing sensory systems in Drosophila.

Neural basis of forward flight control and landing in honeybees

The impressive repertoire of honeybee visually guided behaviors, and their ability to learn has made them an important tool for elucidating the visual basis of behavior. Like other insects, bees perform optomotor course correction to optic flow, a response that is dependent on the spatial structure of the visual environment. However, bees can also distinguish the speed of image motion during forward flight and landing, as well as estimate flight distances (odometry), irrespective of the visual scene. The neural pathways underlying these abilities are unknown. Here we report on a cluster of descending neurons (DNIIIs) that are shown to have the directional tuning properties necessary for detecting image motion during forward flight and landing on vertical surfaces. They have stable firing rates during prolonged periods of stimulation and respond to a wide range of image speeds, making them suitable to detect image flow during flight behaviors. While their responses are not strictly speed tuned, the shape and amplitudes of their speed tuning functions are resistant to large changes in spatial frequency. These cells are prime candidates not only for the control of flight speed and landing, but also the basis of a neural 'front end' of the honeybee's visual odometer.

Insect-machine hybrid system for understanding and evaluating sensory-motor control by sex pheromone in Bombyx mori

To elucidate the dynamic information processing in a brain underlying adaptive behavior, it is necessary to understand the behavior and corresponding neural activities. This requires animals which have clear relationships between behavior and corresponding neural activities. Insects are precisely such animals and one of the adaptive behaviors of insects is high-accuracy odor source orientation. The most direct way to know the relationships between neural activity and behavior is by recording neural activities in a brain from freely behaving insects. There is also a method to give stimuli mimicking the natural environment to tethered insects allowing insects to walk or fly at the same position. In addition to these methods an 'insect-machine hybrid system' is proposed, which is another experimental system meeting the conditions necessary for approaching the dynamic processing in the brain of insects for generating adaptive behavior. This insect-machine hybrid system is an experimental system which has a mobile robot as its body. The robot is controlled by the insect through its behavior or the neural activities recorded from the brain. As we can arbitrarily control the motor output of the robot, we can intervene at the relationship between the insect and the environmental conditions.

Visual response properties of neck motor neurons in the honeybee

Recent behavioural studies have demonstrated that honeybees use visual feedback to stabilize their gaze. However, little is known about the neural circuits that perform the visual motor computations that underlie this ability. We investigated the motor neurons that innervate two neck muscles (m44 and m51), which produce stabilizing yaw movements of the head. Intracellular recordings were made from five (out of eight) identified neuron types in the first cervical nerve (IK1) of honeybees. Two motor neurons that innervate muscle 51 were found to be direction-selective, with a preference for horizontal image motion from the contralateral to the ipsilateral side of the head. Three neurons that innervate muscle 44 were tuned to detect motion in the opposite direction (from ipsilateral to contralateral). These cells were binocularly sensitive and responded optimally to frontal stimulation. By combining the directional tuning of the motor neurons in an opponent manner, the neck motor system would be able to mediate reflexive optomotor head turns in the direction of image motion, thus stabilising the retinal image. When the dorsal ocelli were covered, the spontaneous activity of neck motor neurons increased and visual responses were modified, suggesting an ocellar input in addition to that from the compound eyes.

A three-dimensional atlas of the honeybee neck

Three-dimensional digital atlases are rapidly becoming indispensible in modern biology. We used serial sectioning combined with manual registration and segmentation of images to develop a comprehensive and detailed three-dimensional atlas of the honeybee head-neck system. This interactive atlas includes skeletal structures of the head and prothorax, the neck musculature, and the nervous system. The scope and resolution of the model exceeds atlases previously developed on similar sized animals, and the interactive nature of the model provides a far more accessible means of interpreting and comprehending insect anatomy and neuroanatomy.

The processing of color, motion, and stimulus timing are anatomically segregated in the bumblebee brain

Animals use vision to perform such diverse behaviors as finding food, interacting socially with other animals, choosing a mate, and avoiding predators. These behaviors are complex and the visual system must process color, motion, and pattern cues efficiently so that animals can respond to relevant stimuli. The visual system achieves this by dividing visual information into separate pathways, but to what extent are these parallel streams separated in the brain? To answer this question, we recorded intracellularly in vivo from 105 morphologically identified neurons in the lobula, a major visual processing structure of bumblebees (Bombus impatiens). We found that these cells have anatomically segregated dendritic inputs confined to one or two of six lobula layers. Lobula neurons exhibit physiological characteristics common to their respective input layer. Cells with arborizations in layers 1-4 are generally indifferent to color but sensitive to motion, whereas layer 5-6 neurons often respond to both color and motion cues. Furthermore, the temporal characteristics of these responses differ systematically with dendritic branching pattern. Some layers are more temporally precise, whereas others are less precise but more reliable across trials. Because different layers send projections to different regions of the central brain, we hypothesize that the anatomical layers of the lobula are the structural basis for the segregation of visual information into color, motion, and stimulus timing.