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| Presentation | 2019-12-06 10:10 Implementation of Cerebellar Spiking Neural Network Model on a FPGA Yusuke Shinji (Chubu Univ.), Hirotsugu Okuno (OIT), Yutaka Hirata (Chubu Univ.)  MBE2019-46 NC2019-37 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | The cerebellum is crucially involved in motor control and learning. Its neuronal network architecture and firing properties of the neuron types constituting the cerebellar cortical network have been identified by anatomical and physiological studies for the past four decades. Several artificial cerebellums, namely mathematical models of the cerebellum, have been constructed by referring to such anatomical/physiological evidence to understand neural basis of motor control and learning, envisioning applications to real-world robotics. One of the distinctive characteristics of the cerebellum is a large number of neurons forming the neural network. Thus, simulating a realistic artificial cerebellum requires abundant computational power and resources, and running it in realtime for real-world robotics applications is quite challenging. In this study, we implemented an artificial cerebellum on a field-programmable gate array (FPGA) that is characterized as compact, lightweight, low power consumption, and parallel computing. We applied this FPGA artificial cerebellum to real-world adaptive control of a direct-current motor as a simple example, and demonstrated its effectiveness in comparison with an ordinary feedback controller. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Artificial Cerebellum / Spiking Neural Network / FPGA / Adaptive Control / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 328, NC2019-37, pp. 7-12, Dec. 2019. | |
| Paper # | NC2019-37 | |
| Date of Issue | 2019-11-29 (MBE, NC) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | MBE2019-46 NC2019-37 |
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| Conference Information | |
| Committee | NC MBE |
| Conference Date | 2019-12-06 - 2019-12-06 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Toyohashi Tech |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | NC |
| Conference Code | 2019-12-NC-MBE |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Implementation of Cerebellar Spiking Neural Network Model on a FPGA |
| Sub Title (in English) | |
| Keyword(1) | Artificial Cerebellum |
| Keyword(2) | Spiking Neural Network |
| Keyword(3) | FPGA |
| Keyword(4) | Adaptive Control |
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| 1st Author's Name | Yusuke Shinji |
| 1st Author's Affiliation | Chubu University (Chubu Univ.) |
| 2nd Author's Name | Hirotsugu Okuno |
| 2nd Author's Affiliation | Osaka Institute of Technology (OIT) |
| 3rd Author's Name | Yutaka Hirata |
| 3rd Author's Affiliation | Chubu University (Chubu Univ.) |
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| Speaker | Author-1 |
| Date Time | 2019-12-06 10:10:00 |
| Presentation Time | 25 minutes |
| Registration for | NC |
| Paper # | MBE2019-46, NC2019-37 |
| Volume (vol) | vol.119 |
| Number (no) | no.327(MBE), no.328(NC) |
| Page | pp.7-12 |
| #Pages | 6 |
| Date of Issue | 2019-11-29 (MBE, NC) |
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