186 research outputs found

    A neural circuit for navigation inspired by C. elegans Chemotaxis

    Full text link
    We develop an artificial neural circuit for contour tracking and navigation inspired by the chemotaxis of the nematode Caenorhabditis elegans. In order to harness the computational advantages spiking neural networks promise over their non-spiking counterparts, we develop a network comprising 7-spiking neurons with non-plastic synapses which we show is extremely robust in tracking a range of concentrations. Our worm uses information regarding local temporal gradients in sodium chloride concentration to decide the instantaneous path for foraging, exploration and tracking. A key neuron pair in the C. elegans chemotaxis network is the ASEL & ASER neuron pair, which capture the gradient of concentration sensed by the worm in their graded membrane potentials. The primary sensory neurons for our network are a pair of artificial spiking neurons that function as gradient detectors whose design is adapted from a computational model of the ASE neuron pair in C. elegans. Simulations show that our worm is able to detect the set-point with approximately four times higher probability than the optimal memoryless Levy foraging model. We also show that our spiking neural network is much more efficient and noise-resilient while navigating and tracking a contour, as compared to an equivalent non-spiking network. We demonstrate that our model is extremely robust to noise and with slight modifications can be used for other practical applications such as obstacle avoidance. Our network model could also be extended for use in three-dimensional contour tracking or obstacle avoidance

    Bio-inspired robotic locomotion model: Response towards food gradient changes and temperature variation

    Get PDF
    The nervous system is a complex yet efficient structure - with superior information processing capabilities that surely surpass any man-made high-performance computer. Understanding this technology and utilising it in robotic navigation applications is essential to understand its underlying mechanism. One of the approaches is using a nematode’s biological network model, as having a simple network structure while holding a complex locomotion behaviour. For instance, its ability to navigate via local concentration cue (chemotaxis) and the ability to dynamically respond towards surrounding temperature (thermotaxis). To date, the simulation of currently available models is on static environment conditions and the nematode’s movement decision is based on the deterministic non-linear response towards gradient changes. Commonly, parameters of these models were optimised based on static conditions and require adjustment if simulated within a dynamic environment. Therefore, this work proposed a new nematode’s biological locomotion model where the movement trajectory is determined by the probability of “Run” and “Turn” signals. The model is simulated within a 2D virtual environment with complex concentration gradient and variants of temperature distribution. The analysis result shows the nematode’s movement of the proposed model agreed with the finding from experimental studies. Later, the proposed model in this work will be employed to develop a biological inspired multi-sensory robotic system for navigating within a dynamic and complex environmen

    Mechanisms of Odor-Tracking: Multiple Sensors for Enhanced Perception and Behavior

    Get PDF
    Early in evolution, the ability to sense and respond to changing environments must have provided a critical survival advantage to living organisms. From bacteria and worms to flies and vertebrates, sophisticated mechanisms have evolved to enhance odor detection and localization. Here, we review several modes of chemotaxis. We further consider the relevance of a striking and recurrent motif in the organization of invertebrate and vertebrate sensory systems, namely the existence of two symmetrical olfactory sensors. By combining our current knowledge about the olfactory circuits of larval and adult Drosophila, we examine the molecular and neural mechanisms underlying robust olfactory perception and extend these analyses to recent behavioral studies addressing the relevance and function of bilateral olfactory input for gradient detection. Finally, using a comparative theoretical approach based on Braitenberg's vehicles, we speculate about the relationships between anatomy, circuit architecture and stereotypical orientation behaviors

    A Mechanism for Spatial Orientation Based on Sensory Adaptation in Caenorhabditis Elegans

    Get PDF
    During chemotaxis, animals compute spatial information about odor gradients to make navigational choices for finding or avoiding an odor source. The challenge to the neural circuitry is to interpret and respond to odor concentrations that change over time as animals traverse a gradient. In this thesis, I ask how a nervous system regulates spatial navigation by studying the chemotaxis response of Caenorhabditis elegans to diacetyl. A behavioral analysis demonstrated that AWA sensory neurons drive chemotaxis over several orders of magnitude in odor concentration, providing an entry point for dissecting the mechanistic basis of chemotaxis at the level of neural activity. Precise microfluidic stimulation enabled me to dissociate space from time in the olfactory input to characterize how odor sensing relates to behavior. I systematically measured neuronal responses to odor in the diacetyl chemotaxis circuit, aided by a newly developed imaging system with flexible stimulus delivery and elevated throughput. I found reliable sensory responses to the behaviorally relevant range of odor concentrations. I then followed odor-evoked activity to downstream interneurons that integrate sensory input. Adaptation of neuronal responses to odor yielded a highly sensitive response to small increases in odor concentration at the interneuron level, providing a mechanism for efficient gradient sensing during klinokinesis. Adaptation dynamics at the sensory level were stimulus-dependent and cell-autonomously altered in several classes of mutant animals. Behavioral responses to different concentrations of diacetyl resulted from overlapping contributions from multiple sensory neurons. AWA was specifically required for orientation behavior in response to small increases in odor concentration that are encountered in shallow gradients, demonstrating functional specialization amongst sensory neurons for stimulus characteristics. This work sheds light on an algorithm underlying acute behavioral computation and its biological implementation. The experimental results are presented in two parts: Chapter 2 describes the development of a microscope for high-throughput imaging of neuronal activity in Caenorhabditis elegans. I present a characterization of chemosensory responses to odor and its correlation with behavior. This work has been published (Larsch et al., 2013). Chapter 3 describes the functional architecture of the AWA chemosensory circuit and the role of adaptation in maintaining sensitivity over a wide range of stimulus intensities. This work is currently being prepared for publication

    A computational model of internal representations of chemical gradients in environments for chemotaxis of Caenorhabditis elegans

    Get PDF
    The small roundworm Caenorhabditis elegans employs two strategies, termed pirouette and weathervane, which are closely related to the internal representation of chemical gradients parallel and perpendicular to the travelling direction, respectively, to perform chemotaxis. These gradients must be calculated from the chemical information obtained at a single point, because the sensory neurons are located close to each other at the nose tip. To formulate the relationship between this sensory input and internal representations of the chemical gradient, this study proposes a simple computational model derived from the directional decomposition of the chemical concentration at the nose tip that can generate internal representations of the chemical gradient. The ability of the computational model was verified by using a chemotaxis simulator that can simulate the body motions of pirouette and weathervane, which confirmed that the computational model enables the conversion of the sensory input and head-bending angles into both types of gradients with high correlations of approximately r > 0.90 (p < 0.01) with the true gradients. In addition, the chemotaxis index of the model was 0.64, which is slightly higher than that in the actual animal (0.57). In addition, simulation using a connectome-based neural network model confirmed that the proposed computational model is implementable in the actual network structure.This work was supported by JSPS KAKENHI Grant Number 15H03950 to T.T and MEXT KAKENHI Grant Numbers 20115010 to T.T. and 20115002 to Y.I

    Novel design methods of central nervous system of C. elegans and olfactory bulb model of mammal based on sequential logic and numerical integration

    Get PDF
    This study proposes a novel design method of a neuromorphic electronic circuit: design of a neuromorphic circuit based on appropriately selected hybrid dynamics of synchronous sequential logic, asynchronous sequential logic, and numerical integration. Based on the proposed design method, a novel central nervous system model of C. elegans, and an olfactory bulb model are presented. It is then shown that the presented models can realize typical responses of a conventional central nervous system model of C. elegans, and the observation of chaos in the olfactory bulb. Furthermore, the presented models are implemented by a field programmable gate array and the presented model of C.elegans is used to control a prototype robot of C. elegans body. Then, experiments validate that the presented central nervous system model enables the body robot to reproduce typical chemotaxis behaviors of the conventional C. elegans model. In addition, comparisons show that the presented model consumes fewer circuit elements and lower power compared to various central nervous system models of C. elegans based on synchronous sequential logic, asynchronous sequential logic, and numerical integration
    corecore