260 research outputs found

    Neuroethology, Computational

    No full text
    Over the past decade, a number of neural network researchers have used the term computational neuroethology to describe a specific approach to neuroethology. Neuroethology is the study of the neural mechanisms underlying the generation of behavior in animals, and hence it lies at the intersection of neuroscience (the study of nervous systems) and ethology (the study of animal behavior); for an introduction to neuroethology, see Simmons and Young (1999). The definition of computational neuroethology is very similar, but is not quite so dependent on studying animals: animals just happen to be biological autonomous agents. But there are also non-biological autonomous agents such as some types of robots, and some types of simulated embodied agents operating in virtual worlds. In this context, autonomous agents are self-governing entities capable of operating (i.e., coordinating perception and action) for extended periods of time in environments that are complex, uncertain, and dynamic. Thus, computational neuroethology can be characterised as the attempt to analyze the computational principles underlying the generation of behavior in animals and in artificial autonomous agents

    A bridge from neuroscientific models to recurrent neural networks. Derivation of continuous-time connectionist models from neuroscience computational principles

    Get PDF
    In the last years, recurrent neural networks with continuous dynamics have been applied to model many neurobiological phenomena. However, the literature on the physiological foundations of these connectionist networks is practically non-existent, as they are closer to artificial neural networks than neuroscientific computational models. In this article, we explicitly derive the equations of these recurrent connectionist systems from neuroscientific models, such as leaky integrate-and-fire (LIF) neurons and synaptic chemical kinetics. We specify under what conditions this modelling is supposed to hold, and we run simulations of networks wired like some simple neural circuits, such as those that possess species like Tritonia Diomedea, Aplysia Californica or lampreys, in order to show their similar behaviour. Finally, in the first annex we introduce some of the emerging properties of these networks, such as being universal approximators of dynamical systems, and we remark that this approach is congruent with the spontaneous synchronic activity that is known to take place in the cortex.A través d'aquestes pàgines, oferim una derivació completament nova d'alguns models de freqüència neuronal, partint del formalisme dels "integrate and fire" i de la cinètica sinàptica. El propòsit principal és investigar els fonaments neurocientífics dels models connexionistes, ja que les equacions que obtenim es tracten de xarxes neuronals recurrents. Llavors, realitzem simulacions de circuits neuronals reals fent servir aquestes equacions, provant que poden ajustar-se a les dades registrades en tres experiments diferents. Finalment, investiguem una propietat emergent de les xarxes derivades, i mostrem que aquesta característica està en sintonia amb les correlacions que han sigut observades entre les neurones del còrtex

    Spinal Cord of Lamprey

    Get PDF

    Design of artificial neural oscillatory circuits for the control of lamprey- and salamander-like locomotion using evolutionary algorithms

    Get PDF
    This dissertation investigates the evolutionary design of oscillatory artificial neural networks for the control of animal-like locomotion. It is inspired by the neural organ¬ isation of locomotor circuitries in vertebrates, and explores in particular the control of undulatory swimming and walking. The difficulty with designing such controllers is to find mechanisms which can transform commands concerning the direction and the speed of motion into the multiple rhythmic signals sent to the multiple actuators typically involved in animal-like locomotion. In vertebrates, such control mechanisms are provided by central pattern generators which are neural circuits capable of pro¬ ducing the patterns of oscillations necessary for locomotion without oscillatory input from higher control centres or from sensory feedback. This thesis explores the space of possible neural configurations for the control of undulatory locomotion, and addresses the problem of how biologically plausible neural controllers can be automatically generated.Evolutionary algorithms are used to design connectionist models of central pattern generators for the motion of simulated lampreys and salamanders. This work is inspired by Ekeberg's neuronal and mechanical simulation of the lamprey [Ekeberg 93]. The first part of the thesis consists of developing alternative neural controllers for a similar mechanical simulation. Using a genetic algorithm and an incremental approach, a variety of controllers other than the biological configuration are successfully developed which can control swimming with at least the same efficiency. The same method is then used to generate synaptic weights for a controller which has the observed biological connectivity in order to illustrate how the genetic algorithm could be used for developing neurobiological models. Biologically plausible controllers are evolved which better fit physiological observations than Ekeberg's hand-crafted model. Finally, in collaboration with Jerome Kodjabachian, swimming controllers are designed using a developmental encoding scheme, in which developmental programs are evolved which determine how neurons divide and get connected to each other on a two-dimensional substrate.The second part of this dissertation examines the control of salamander-like swimming and trotting. Salamanders swim like lampreys but, on the ground, they switch to a trotting gait in which the trunk performs a standing wave with the nodes at the girdles. Little is known about the locomotion circuitry of the salamander, but neurobiologists have hypothesised that it is based on a lamprey-like organisation. A mechanical sim¬ ulation of a salamander-like animat is developed, and neural controllers capable of exhibiting the two types of gaits are evolved. The controllers are made of two neural oscillators projecting to the limb motoneurons and to lamprey-like trunk circuitry. By modulating the tonic input applied to the networks, the type of gait, the speed and the direction of motion can be varied.By developing neural controllers for lamprey- and salamander-like locomotion, this thesis provides insights into the biological control of undulatory swimming and walking, and shows how evolutionary algorithms can be used for developing neurobiological models and for generating neural controllers for locomotion. Such a method could potentially be used for designing controllers for swimming or walking robots, for instance

    A modular architecture for transparent computation in recurrent neural networks

    Get PDF
    publisher: Elsevier articletitle: A modular architecture for transparent computation in recurrent neural networks journaltitle: Neural Networks articlelink: http://dx.doi.org/10.1016/j.neunet.2016.09.001 content_type: article copyright: © 2016 Elsevier Ltd. All rights reserved

    Exploring aspects of cell intelligence with artificial reaction networks.

    Get PDF
    The Artificial Reaction Network (ARN) is a Cell Signalling Network inspired connectionist representation belonging to the branch of A-Life known as Artificial Chemistry. Its purpose is to represent chemical circuitry and to explore computational properties responsible for generating emergent high-level behaviour associated with cells. In this paper, the computational mechanisms involved in pattern recognition and spatio-temporal pattern generation are examined in robotic control tasks. The results show that the ARN has application in limbed robotic control and computational functionality in common with Artificial Neural Networks. Like spiking neural models, the ARN can combine pattern recognition and complex temporal control functionality in a single network, however it offers increased flexibility. Furthermore, the results illustrate parallels between emergent neural and cell intelligence

    Robust simulation of lamprey tracking

    Full text link
    Biologically realistic computer simulation of vertebrates is a challenging problem with exciting applications in computer graphics and robotics. Once the mechanics of locomotion are available it is interesting to mediate this locomotion with higher level behavior such as target tracking. One recent approach simulates a relatively simple vertebrate, the lamprey, using recurrent neural networks to model the central pattern generator of the spine and a physical model for the body. Target tracking behavior has also been implemented for such a model. However, previous approaches suffer from deficiencies where particular orientations of the body to the target cause the central pattern generator to shutdown. This paper describes an approach to making target tracking more robust. © Springer-Verlag Berlin Heidelberg 2006

    Investigating modularity and transparency within bioinspired connectionist architectures using genetic and epigenetic models

    Get PDF
    Machine learning algorithms allow computers to deal with incomplete data in tasks such as speech recognition and object detection. Some machine learning algorithms take inspiration from biological systems due to useful properties such as robustness, allowing algorithms to be flexible and domain agnostic. This comes at a cost, resulting in difficulty when one attempts to understand the reasoning behind decisions. This is problematic when such models are applied in realworld situations where accountability, legality, and maintenance are of concern. Artificial gene regulatory networks (AGRNs) are a type of connectionist architecture inspired by gene regulatory mechanisms. AGRNs are of interest within this thesis due to their ability to solve tasks in chaotic dynamical systems despite their relatively small size.The overarching aim of this work was to investigate the properties of connectionist architectures to improve the transparency of their execution. Initially, the evolutionary process and internal structure of AGRNs were investigated. Following this, the creation of an external control layer used to improve the transparency of execution of an external connectionist architecture was attempted.When investigating the evolutionary process of AGRNs, pathways were found that when followed, produced more performant networks in a shorter time frame. Evidence that AGRNs are capable of performing well despite internal interference was found when investigating their modularity, where it was also discovered that they do not develop strict modularity consistently. A control layer inspired by epigenetics that selectively deactivates nodes in trained artificial neural networks (ANNs) was developed; the analysis of its behaviour provided an insight into the internal workings of the ANN

    Evolutionary robotics and neuroscience

    Get PDF
    No description supplie
    • …
    corecore