How the insect central complex could coordinate multimodal navigation.
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ABSTRACT:
SUBMITTER: Sun X
PROVIDER: S-EPMC8741217 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The central complex in the insect brain is a composite of midline neuropils involved in processing sensory cues and mediating behavioral outputs to orchestrate spatial navigation. Despite recent advances, however, the neural mechanisms underlying sensory integration and motor action selections have remained largely elusive. In particular, it is not yet understood how the central complex exploits sensory inputs to realize motor functions associated with spatial navigation. Here we report an in silico interrogation of central complex-mediated spatial navigation with a special emphasis on the ellipsoid body. Based on known connectivity and function, we developed a computational model to test how the local connectome of the central complex can mediate sensorimotor integration to guide different forms of behavioral outputs. Our simulations show integration of multiple sensory sources can be effectively performed in the ellipsoid body. This processed information is used to trigger continuous sequences of action selections resulting in self-motion, obstacle avoidance and the navigation of simulated environments of varying complexity. The motor responses to perceived sensory stimuli can be stored in the neural structure of the central complex to simulate navigation relying on a collective of guidance cues, akin to sensory-driven innate or habitual behaviors. By comparing behaviors under different conditions of accessible sources of input information, we show the simulated insect computes visual inputs and body posture to estimate its position in space. Finally, we tested whether the local connectome of the central complex might also allow the flexibility required to recall an intentional behavioral sequence, among different courses of actions. Our simulations suggest that the central complex can encode combined representations of motor and spatial information to pursue a goal and thus successfully guide orientation behavior. Together, the observed computational features identify central complex circuitry, and especially the ellipsoid body, as a key neural correlate involved in spatial navigation.
| S-EPMC5540904 | biostudies-literature
Project description:Insects use path integration (PI) to maintain a home vector, but can also store and recall vector-memories that take them from home to a food location, and even allow them to take novel shortcuts between food locations. The neural circuit of the Central Complex (a brain area that receives compass and optic flow information) forms a plausible substrate for these behaviors. A recent model, grounded in neurophysiological and neuroanatomical data, can account for PI during outbound exploratory routes and the control of steering to return home. Here, we show that minor, hypothetical but neurally plausible, extensions of this model can additionally explain how insects could store and recall PI vectors to follow food-ward paths, take shortcuts, search at the feeder and re-calibrate their vector-memories with experience. In addition, a simple assumption about how one of multiple vector-memories might be chosen at any point in time can produce the development and maintenance of efficient routes between multiple locations, as observed in bees. The central complex circuitry is therefore well-suited to allow for a rich vector-based navigational repertoire.
| S-EPMC6460943 | biostudies-literature
Project description:In the last decades, desert ants have become model organisms for the study of insect navigation. In finding their way, they use two major navigational routines: path integration using a celestial compass and landmark guidance based on sets of panoramic views of the terrestrial environment. It has been claimed that this information would enable the insect to acquire and use a centralized cognitive map of its foraging terrain. Here, we present a decentralized architecture, in which the concurrently operating path integration and landmark guidance routines contribute optimally to the directions to be steered, with "optimal" meaning maximizing the certainty (reliability) of the combined information. At any one time during its journey, the animal computes a path integration (global) vector and landmark guidance (local) vector, in which the length of each vector is proportional to the certainty of the individual estimates. Hence, these vectors represent the limited knowledge that the navigator has at any one place about the direction of the goal. The sum of the global and local vectors indicates the navigator's optimal directional estimate. Wherever applied, this decentralized model architecture is sufficient to simulate the results of quite a number of diverse cue-conflict experiments, which have recently been performed in various behavioral contexts by different authors in both desert ants and honeybees. They include even those experiments that have deliberately been designed by former authors to strengthen the evidence for a metric cognitive map in bees.
| S-EPMC5856556 | biostudies-literature
Project description:Mobile microrobots have a promising future in various applications. These include targeted drug delivery, local measurement, biopsy or microassembly. Studying mobile microrobots inside microfluidics is an essential step towards such applications. But in this environment that was not designed for the robot, integration process and propulsion robustness still pose technological challenges. In this paper, we present a helical microrobot with three different motions, designed to achieve these goals. These motions are rolling, spintop motion and swimming. Through these multiple motions, microrobots are able to selectively integrate a chip through a microfluidic channel. This enables them to perform propulsion characterizations, 3D (Three Dimensional) maneuverability, particle cargo transport manipulation and exit from the chip. The microrobot selective integration inside microfluidics could lead to various in-vitro biologic or in-vivo biomedical applications.
| S-EPMC4726319 | biostudies-literature
Project description:The adult insect brain is composed of neuropils present in most taxa. However, the relative size, shape, and developmental timing differ between species. This diversity of adult insect brain morphology has been extensively described while the genetic mechanisms of brain development are studied predominantly in Drosophila melanogaster. However, it has remained enigmatic what cellular and genetic mechanisms underlie the evolution of neuropil diversity or heterochronic development. In this perspective paper, we propose a novel approach to study these questions. We suggest using genome editing to mark homologous neural cells in the fly D. melanogaster, the beetle Tribolium castaneum, and the Mediterranean field cricket Gryllus bimaculatus to investigate developmental differences leading to brain diversification. One interesting aspect is the heterochrony observed in central complex development. Ancestrally, the central complex is formed during embryogenesis (as in Gryllus) but in Drosophila, it arises during late larval and metamorphic stages. In Tribolium, it forms partially during embryogenesis. Finally, we present tools for brain research in Tribolium including 3D reconstruction and immunohistochemistry data of first instar brains and the generation of transgenic brain imaging lines. Further, we characterize reporter lines labeling the mushroom bodies and reflecting the expression of the neuroblast marker gene Tc-asense, respectively.
| S-EPMC4896989 | biostudies-literature
Project description:Insects can navigate efficiently in both novel and familiar environments, and this requires flexiblity in how they are guided by sensory cues. A prominent landmark, for example, can elicit strong innate behaviours (attraction or menotaxis) but can also be used, after learning, as a specific directional cue as part of a navigation memory. However, the mechanisms that allow both pathways to co-exist, interact or override each other are largely unknown. Here we propose a model for the behavioural integration of innate and learned guidance based on the neuroanatomy of the central complex (CX), adapted to control landmark guided behaviours. We consider a reward signal provided either by an innate attraction to landmarks or a long-term visual memory in the mushroom bodies (MB) that modulates the formation of a local vector memory in the CX. Using an operant strategy for a simulated agent exploring a simple world containing a single visual cue, we show how the generated short-term memory can support both innate and learned steering behaviour. In addition, we show how this architecture is consistent with the observed effects of unilateral MB lesions in ants that cause a reversion to innate behaviour. We suggest the formation of a directional memory in the CX can be interpreted as transforming rewarding (positive or negative) sensory signals into a mapping of the environment that describes the geometrical attractiveness (or repulsion). We discuss how this scheme might represent an ideal way to combine multisensory information gathered during the exploration of an environment and support optimal cue integration.
| S-EPMC8491911 | biostudies-literature
Project description:In many animals the ability to navigate over long distances is an important prerequisite for foraging. For example, it is widely accepted that desert ants and honey bees, but also mammals, use path integration for finding the way back to their home site. It is however a matter of a long standing debate whether animals in addition are able to acquire and use so called cognitive maps. Such a 'map', a global spatial representation of the foraging area, is generally assumed to allow the animal to find shortcuts between two sites although the direct connection has never been travelled before. Using the artificial neural network approach, here we develop an artificial memory system which is based on path integration and various landmark guidance mechanisms (a bank of individual and independent landmark-defined memory elements). Activation of the individual memory elements depends on a separate motivation network and an, in part, asymmetrical lateral inhibition network. The information concerning the absolute position of the agent is present, but resides in a separate memory that can only be used by the path integration subsystem to control the behaviour, but cannot be used for computational purposes with other memory elements of the system. Thus, in this simulation there is no neural basis of a cognitive map. Nevertheless, an agent controlled by this network is able to accomplish various navigational tasks known from ants and bees and often discussed as being dependent on a cognitive map. For example, map-like behaviour as observed in honey bees arises as an emergent property from a decentralized system. This behaviour thus can be explained without referring to the assumption that a cognitive map, a coherent representation of foraging space, must exist. We hypothesize that the proposed network essentially resides in the mushroom bodies of the insect brain.
| S-EPMC3060166 | biostudies-literature
Project description:Despite their small size, insect brains are able to produce robust and efficient navigation in complex environments. Specifically in social insects, such as ants and bees, these navigational capabilities are guided by orientation directing vectors generated by a process called path integration. During this process, they integrate compass and odometric cues to estimate their current location as a vector, called the home vector for guiding them back home on a straight path. They further acquire and retrieve path integration-based vector memories globally to the nest or based on visual landmarks. Although existing computational models reproduced similar behaviors, a neurocomputational model of vector navigation including the acquisition of vector representations has not been described before. Here we present a model of neural mechanisms in a modular closed-loop control-enabling vector navigation in artificial agents. The model consists of a path integration mechanism, reward-modulated global learning, random search, and action selection. The path integration mechanism integrates compass and odometric cues to compute a vectorial representation of the agent's current location as neural activity patterns in circular arrays. A reward-modulated learning rule enables the acquisition of vector memories by associating the local food reward with the path integration state. A motor output is computed based on the combination of vector memories and random exploration. In simulation, we show that the neural mechanisms enable robust homing and localization, even in the presence of external sensory noise. The proposed learning rules lead to goal-directed navigation and route formation performed under realistic conditions. Consequently, we provide a novel approach for vector learning and navigation in a simulated, situated agent linking behavioral observations to their possible underlying neural substrates.
| S-EPMC5388780 | biostudies-literature
Project description:Living cells of many types need to move in response to external stimuli in order to accomplish their functional tasks; these tasks range from wound healing to immune response to fertilization. While the directional motion is typically dictated by an external signal, the actual motility is also restricted by physical constraints, such as the presence of other cells and the extracellular matrix. The ability to successfully navigate in the presence of obstacles is not only essential for organisms, but might prove relevant in the study of autonomous robotic motion.We study a computational model of amoeboid chemotactic navigation under differing conditions, from motion in an obstacle-free environment to navigation between obstacles and finally to moving in a maze. We use the maze as a simple stand-in for a motion task with severe constraints, as might be expected in dense extracellular matrix. Whereas agents using simple chemotaxis can successfully navigate around small obstacles, the presence of large barriers can often lead to agent trapping. We further show that employing a simple memory mechanism, namely secretion of a repulsive chemical by the agent, helps the agent escape from such trapping.Our main conclusion is that cells employing simple chemotactic strategies will often be unable to navigate through maze-like geometries, but a simple chemical marker mechanism (which we refer to as "self-assistance") significantly improves success rates. This realization provides important insights into mechanisms that might be employed by real cells migrating in complex environments as well as clues for the design of robotic navigation strategies. The results can be extended to more complicated multi-cellular systems and can be used in the study of mammalian cell migration and cancer metastasis.
| S-EPMC3150345 | biostudies-other
Project description:Complex networks are formal frameworks capturing the interdependencies between the elements of large systems and databases. This formalism allows to use network navigation methods to rank the importance that each constituent has on the global organization of the system. A key example is Pagerank navigation which is at the core of the most used search engine of the World Wide Web. Inspired in this classical algorithm, we define a quantum navigation method providing a unique ranking of the elements of a network. We analyze the convergence of quantum navigation to the stationary rank of networks and show that quantumness decreases the number of navigation steps before convergence. In addition, we show that quantum navigation allows to solve degeneracies found in classical ranks. By implementing the quantum algorithm in real networks, we confirm these improvements and show that quantum coherence unveils new hierarchical features about the global organization of complex systems.
| S-EPMC3428603 | biostudies-other