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Vision-Based Control for Robots by a Fully Spiking Neural System Relying on Cerebellar Predictive Learning

TL;DR: This work proposes a novel fully spiking neural system that relies on a forward predictive learning by means of a cellular cerebellar model and predicts sensory corrections in input to a differential mappingSpiking neural network during a visual servoing task of a robot arm manipulator.
Abstract: The cerebellum plays a distinctive role within our motor control system to achieve fine and coordinated motions. While cerebellar lesions do not lead to a complete loss of motor functions, both action and perception are severally impacted. Hence, it is assumed that the cerebellum uses an internal forward model to provide anticipatory signals by learning from the error in sensory states. In some studies, it was demonstrated that the learning process relies on the joint-space error. However, this may not exist. This work proposes a novel fully spiking neural system that relies on a forward predictive learning by means of a cellular cerebellar model. The forward model is learnt thanks to the sensory feedback in task-space and it acts as a Smith predictor. The latter predicts sensory corrections in input to a differential mapping spiking neural network during a visual servoing task of a robot arm manipulator. In this paper, we promote the developed control system to achieve more accurate target reaching actions and reduce the motion execution time for the robotic reaching tasks thanks to the cerebellar predictive capabilities.
Citations
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Journal ArticleDOI
06 Jun 1986-JAMA
TL;DR: The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or her own research.
Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

7,563 citations

Posted Content
TL;DR: In this article, a detailed cellular-level forward cerebellar model is developed, including modeling of Golgi and basket cells which are usually neglected in previous studies, and a hyperparameter optimization method tunes the network accordingly.
Abstract: While the original goal for developing robots is replacing humans in dangerous and tedious tasks, the final target shall be completely mimicking the human cognitive and motor behaviour. Hence, building detailed computational models for the human brain is one of the reasonable ways to attain this. The cerebellum is one of the key players in our neural system to guarantee dexterous manipulation and coordinated movements as concluded from lesions in that region. Studies suggest that it acts as a forward model providing anticipatory corrections for the sensory signals based on observed discrepancies from the reference values. While most studies consider providing the teaching signal as error in joint-space, few studies consider the error in task-space and even fewer consider the spiking nature of the cerebellum on the cellular-level. In this study, a detailed cellular-level forward cerebellar model is developed, including modeling of Golgi and Basket cells which are usually neglected in previous studies. To preserve the biological features of the cerebellum in the developed model, a hyperparameter optimization method tunes the network accordingly. The efficiency and biological plausibility of the proposed cerebellar-based controller is then demonstrated under different robotic manipulation tasks reproducing motor behaviour observed in human reaching experiments.
References
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Book
01 Jun 1981
TL;DR: The principles of neural science as mentioned in this paper have been used in neural networks for the purpose of neural network engineering and neural networks have been applied in the field of neural networks, such as:
Abstract: Principles of neural science , Principles of neural science , کتابخانه دانشگاه علوم پزشکی و خدمات بهداشتی درمانی کرمان

8,872 citations

Journal ArticleDOI
06 Jun 1986-JAMA
TL;DR: The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or her own research.
Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

7,563 citations


"Vision-Based Control for Robots by ..." refers background in this paper

  • ...Research studies have shown that patients with cerebellar lesions suffer from clumsy staggering movements [2]....

    [...]

Journal ArticleDOI
TL;DR: A model is presented that reproduces spiking and bursting behavior of known types of cortical neurons and combines the biologically plausibility of Hodgkin-Huxley-type dynamics and the computational efficiency of integrate-and-fire neurons.
Abstract: A model is presented that reproduces spiking and bursting behavior of known types of cortical neurons. The model combines the biologically plausibility of Hodgkin-Huxley-type dynamics and the computational efficiency of integrate-and-fire neurons. Using this model, one can simulate tens of thousands of spiking cortical neurons in real time (1 ms resolution) using a desktop PC.

4,082 citations


"Vision-Based Control for Robots by ..." refers background in this paper

  • ...Izhikevich’s simple model of spiking neurons [21] is adopted for its capability of reproducing various firing patterns while holding a balance between computational cost and biological plausibility [21]....

    [...]

Book
01 Jan 2007
TL;DR: A circular cribbage board having a circular base plate on which a circular counter disc, bearing a circular scale having 122 divisions numbered consecutively from 0, is mounted for rotation.
Abstract: From the Publisher: Dramatically updating and extending the first edition, published in 1995, the second edition of The Handbook of Brain Theory and Neural Networks presents the enormous progress made in recent years in the many subfields related to the two great questions: How does the brain work? and, How can we build intelligent machines? Once again, the heart of the book is a set of almost 300 articles covering the whole spectrum of topics in brain theory and neural networks. The first two parts of the book, prepared by Michael Arbib, are designed to help readers orient themselves in this wealth of material. Part I provides general background on brain modeling and on both biological and artificial neural networks. Part II consists of "Road Maps" to help readers steer through articles in part III on specific topics of interest. The articles in part III are written so as to be accessible to readers of diverse backgrounds. They are cross-referenced and provide lists of pointers to Road Maps, background material, and related reading. The second edition greatly increases the coverage of models of fundamental neurobiology, cognitive neuroscience, and neural network approaches to language. It contains 287 articles, compared to the 266 in the first edition. Articles on topics from the first edition have been updated by the original authors or written anew by new authors, and there are 106 articles on new topics.

3,487 citations


"Vision-Based Control for Robots by ..." refers methods in this paper

  • ...The encoding of variables is based on the central (preferred) value ψc assigned to each neuron, with the contribution of the whole assembly utilizing “population coding” [17])....

    [...]

Journal ArticleDOI
TL;DR: A modular approach to motor learning and control based on multiple pairs of inverse (controller) and forward (predictor) models that can simultaneously learn the multiple inverse models necessary for control as well as how to select the inverse models appropriate for a given environment is proposed.

2,101 citations


"Vision-Based Control for Robots by ..." refers methods in this paper

  • ...In particular, the cerebellum seems to act as an inverse model to generate corrective motor commands, as a forward model to enhance sensory predictions, and as a combination of both models [4], [5]....

    [...]