Motor-Skill Learning in an Insect Inspired Neuro-Computational Control System

Arena, Eleonora; Arena, Paolo; Strauß, Roland; Patané, Luca

doi:10.3389/fnbot.2017.00012

Cited by 30 publications

(17 citation statements)

References 65 publications

(93 reference statements)

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“…This leads us to formalize a definition of computational neurobehavior as: The study of biological sensory-motor system behavior using biologically accurate models of sensory input, brain and motor sub-systems operating in a closed-loop . We believe our oculomotor model is one of the first such models using this approach and shares many features with that of Arena et al ( 2017 ) which describes a robotic insect system based on the fly species Drosophila Melanogaster . It has biomimetic insect legs implemented in a virtual robot and a neuromimetic brain.…”

Section: Discussionmentioning

confidence: 65%

Integrating Brain and Biomechanical Models—A New Paradigm for Understanding Neuro-muscular Control

et al. 2018

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To date, realistic models of how the central nervous system governs behavior have been restricted in scope to the brain, brainstem or spinal column, as if these existed as disembodied organs. Further, the model is often exercised in relation to an in vivo physiological experiment with input comprising an impulse, a periodic signal or constant activation, and output as a pattern of neural activity in one or more neural populations. Any link to behavior is inferred only indirectly via these activity patterns. We argue that to discover the principles of operation of neural systems, it is necessary to express their behavior in terms of physical movements of a realistic motor system, and to supply inputs that mimic sensory experience. To do this with confidence, we must connect our brain models to neuro-muscular models and provide relevant visual and proprioceptive feedback signals, thereby closing the loop of the simulation. This paper describes an effort to develop just such an integrated brain and biomechanical system using a number of pre-existing models. It describes a model of the saccadic oculomotor system incorporating a neuromuscular model of the eye and its six extraocular muscles. The position of the eye determines how illumination of a retinotopic input population projects information about the location of a saccade target into the system. A pre-existing saccadic burst generator model was incorporated into the system, which generated motoneuron activity patterns suitable for driving the biomechanical eye. The model was demonstrated to make accurate saccades to a target luminance under a set of environmental constraints. Challenges encountered in the development of this model showed the importance of this integrated modeling approach. Thus, we exposed shortcomings in individual model components which were only apparent when these were supplied with the more plausible inputs available in a closed loop design. Consequently we were able to suggest missing functionality which the system would require to reproduce more realistic behavior. The construction of such closed-loop animal models constitutes a new paradigm of computational neurobehavior and promises a more thoroughgoing approach to our understanding of the brain's function as a controller for movement and behavior.

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Section: Discussionmentioning

confidence: 65%

Integrating Brain and Biomechanical Models—A New Paradigm for Understanding Neuro-muscular Control

et al. 2018

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“…LSMs seem to be a potential and promising theory to explain brain operation mainly because neuron activities are not hard coded and limited for specific tasks. Burgsteiner ( 2005 ), Probst et al ( 2012 ), and Arena et al ( 2017 ) showed how liquid state machines can be trained for robot control tasks.…”

Section: Learning and Robotics Applicationsmentioning

confidence: 99%

A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks

et al. 2018

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Biological intelligence processes information using impulses or spikes, which makes those living creatures able to perceive and act in the real world exceptionally well and outperform state-of-the-art robots in almost every aspect of life. To make up the deficit, emerging hardware technologies and software knowledge in the fields of neuroscience, electronics, and computer science have made it possible to design biologically realistic robots controlled by spiking neural networks (SNNs), inspired by the mechanism of brains. However, a comprehensive review on controlling robots based on SNNs is still missing. In this paper, we survey the developments of the past decade in the field of spiking neural networks for control tasks, with particular focus on the fast emerging robotics-related applications. We first highlight the primary impetuses of SNN-based robotics tasks in terms of speed, energy efficiency, and computation capabilities. We then classify those SNN-based robotic applications according to different learning rules and explicate those learning rules with their corresponding robotic applications. We also briefly present some existing platforms that offer an interaction between SNNs and robotics simulations for exploration and exploitation. Finally, we conclude our survey with a forecast of future challenges and some associated potential research topics in terms of controlling robots based on SNNs.

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“…This efficiently handles the problem with exponential convergence to behaviors and to specific relations among the state variables of the control system. Finally, descending commands for navigation control and behavior selection (e.g., steering and climbing control), can modify the network dynamics by selecting the suitable parameters at the level of neurons and connection to be tuned [51,56].…”

Section: Locomotion Control Systemmentioning

confidence: 99%

Bio-Inspired Robotic Solutions for Landslide Monitoring

Patané

2019

Energies

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Bio-inspired solutions are often taken into account to solve problems that nature took millions of years to deal with. In the field of robotics, when we need to design systems able to perform in unstructured environments, bio-inspiration can be a useful instrument both for mechanical design and for the control architecture. In the proposed work the problem of landslide monitoring is addressed proposing a bio-inspired robotic structure developed to deploy a series of smart sensors on target locations with the aim of creating a sensor network capable of acquiring information on the status of the area of interest. The acquired data can be used both to create models and to generate alert signals when a landslide event is identified in the early stage. The design process of the robotic system, including dynamic simulations and robot experiments, will be presented here.

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Motor-Skill Learning in an Insect Inspired Neuro-Computational Control System

Cited by 30 publications

References 65 publications

Integrating Brain and Biomechanical Models—A New Paradigm for Understanding Neuro-muscular Control

Integrating Brain and Biomechanical Models—A New Paradigm for Understanding Neuro-muscular Control

A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks

Bio-Inspired Robotic Solutions for Landslide Monitoring

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