Bio-inspired neural networks for decision-making mechanisms and neuromodulation for motor control in a differential robot
The aim of this work is to propose bio-inspired neural networks for decision-making mechanisms and modulation of motor control of an automaton. In this work, we have adapted and applied cortical synaptic circuits, such as short-term memory circuits, winner-take-all (WTA) class competitive neural net...
- Autores:
-
Guerrero-Criollo, Roberto José
Castaño-López, Jason Alejandro
Hurtado López, Julián
Ramírez Moreno, David Fernando
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2023
- Institución:
- Universidad Autónoma de Occidente
- Repositorio:
- RED: Repositorio Educativo Digital UAO
- Idioma:
- eng
- OAI Identifier:
- oai:red.uao.edu.co:10614/15545
- Acceso en línea:
- https://hdl.handle.net/10614/15545
https://red.uao.edu.co/
- Palabra clave:
- Bio-inspired neural network
Neuromodulation network
Adaptation stage
Signal processing
Dierential robot
Exploration behavior
Automa
- Rights
- openAccess
- License
- Derechos reservados - Frontiers Media S.A., 2023
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dc.title.eng.fl_str_mv |
Bio-inspired neural networks for decision-making mechanisms and neuromodulation for motor control in a differential robot |
title |
Bio-inspired neural networks for decision-making mechanisms and neuromodulation for motor control in a differential robot |
spellingShingle |
Bio-inspired neural networks for decision-making mechanisms and neuromodulation for motor control in a differential robot Bio-inspired neural network Neuromodulation network Adaptation stage Signal processing Dierential robot Exploration behavior Automa |
title_short |
Bio-inspired neural networks for decision-making mechanisms and neuromodulation for motor control in a differential robot |
title_full |
Bio-inspired neural networks for decision-making mechanisms and neuromodulation for motor control in a differential robot |
title_fullStr |
Bio-inspired neural networks for decision-making mechanisms and neuromodulation for motor control in a differential robot |
title_full_unstemmed |
Bio-inspired neural networks for decision-making mechanisms and neuromodulation for motor control in a differential robot |
title_sort |
Bio-inspired neural networks for decision-making mechanisms and neuromodulation for motor control in a differential robot |
dc.creator.fl_str_mv |
Guerrero-Criollo, Roberto José Castaño-López, Jason Alejandro Hurtado López, Julián Ramírez Moreno, David Fernando |
dc.contributor.author.none.fl_str_mv |
Guerrero-Criollo, Roberto José Castaño-López, Jason Alejandro Hurtado López, Julián Ramírez Moreno, David Fernando |
dc.subject.proposal.eng.fl_str_mv |
Bio-inspired neural network Neuromodulation network Adaptation stage Signal processing Dierential robot Exploration behavior Automa |
topic |
Bio-inspired neural network Neuromodulation network Adaptation stage Signal processing Dierential robot Exploration behavior Automa |
description |
The aim of this work is to propose bio-inspired neural networks for decision-making mechanisms and modulation of motor control of an automaton. In this work, we have adapted and applied cortical synaptic circuits, such as short-term memory circuits, winner-take-all (WTA) class competitive neural networks, modulation neural networks, and nonlinear oscillation circuits, in order to make the automaton able to avoid obstacles and explore simulated and real environments. The performance achieved by using biologically inspired neural networks to solve the task at hand is similar to that of several works mentioned in the specialized literature. Furthermore, this work contributed to bridging the fields of computational neuroscience and robotics |
publishDate |
2023 |
dc.date.issued.none.fl_str_mv |
2023-02-03 |
dc.date.accessioned.none.fl_str_mv |
2024-04-17T16:21:07Z |
dc.date.available.none.fl_str_mv |
2024-04-17T16:21:07Z |
dc.type.spa.fl_str_mv |
Artículo de revista |
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Guerrero-Criollo, R. J.; Castaño-López, J. A.; Hurtado-López, J.; o Ramírez-Moreno, D.F. (2023). Bio-inspired neural networks for decision-making mechanisms and neuromodulation for motor control in a differential robot. Frontiers in Neurorobotics. p.p. 1-16. DOI 10.3389/fnbot.2023.1078074 |
dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/10614/15545 |
dc.identifier.doi.none.fl_str_mv |
DOI 10.3389/fnbot.2023.1078074 |
dc.identifier.eissn.spa.fl_str_mv |
1662-5218 |
dc.identifier.instname.spa.fl_str_mv |
Universidad Autónoma de Occidente |
dc.identifier.reponame.spa.fl_str_mv |
Respositorio Educativo Digital UAO |
dc.identifier.repourl.none.fl_str_mv |
https://red.uao.edu.co/ |
identifier_str_mv |
Guerrero-Criollo, R. J.; Castaño-López, J. A.; Hurtado-López, J.; o Ramírez-Moreno, D.F. (2023). Bio-inspired neural networks for decision-making mechanisms and neuromodulation for motor control in a differential robot. Frontiers in Neurorobotics. p.p. 1-16. DOI 10.3389/fnbot.2023.1078074 DOI 10.3389/fnbot.2023.1078074 1662-5218 Universidad Autónoma de Occidente Respositorio Educativo Digital UAO |
url |
https://hdl.handle.net/10614/15545 https://red.uao.edu.co/ |
dc.language.iso.eng.fl_str_mv |
eng |
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eng |
dc.relation.citationendpage.none.fl_str_mv |
16 |
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1 |
dc.relation.ispartofjournal.eng.fl_str_mv |
Frontiers in neurorobotics |
dc.relation.references.none.fl_str_mv |
Cao, Z., Cheng, L., Zhou, C., Gu, N., Wang, X., and Tan, M. (2015). Spiking neural network-based target tracking control for autonomous mobile robots. Neural Comput. Appl. 26, 1839–1847. doi: 10.1007/s00521-015-1848-5 Foundation, O. S. R. (2014). Gazebo. Available online at: http://gazebosim.org/ Guerrero-Criollo, R. J., Castaño-López, J. A., Díaz-Cuchala, R. E., David Rozo- Giraldo, Y., and Ramirez-Moreno, D. F. (2022). “Design and simulation of a bio-inspired neural network for the motor control of a mobile automaton,” in 2022 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI) (Cali: IEEE), 1–6. Héricé, C., Khalil, R., Moftah, M., Boraud, T., Guthrie, M., and Garenne, A. (2016). Decision making under uncertainty in a spiking neural network model of the basal ganglia. J. Integr. Neurosci. 15, 515–53. doi: 10.1142/S021963521650028X Hikosaka, O., Ghazizadeh, A., Griggs, W., and Amita, H. (2018). Parallel basal ganglia circuits for decision making. J. Neural Transm. 125, 515–529. doi: 10.1007/s00702-017-1691-1 Hurtado-López, J., and Ramirez-Moreno, D. F. (2019). Dynamics of a neural circuit that mediates social and nonsocial behaviors. Int. J. Bifurcat. Chaos 29, 1950138. doi: 10.1142/S0218127419501384 Hurtado-López, J., Ramirez-Moreno, D. F., and Sejnowski, T. J. (2017). Decisionmaking neural circuits mediating social behaviors. J. Comput. Neurosci. 43, 127–142. doi: 10.1007/s10827-017-0654-8 Liu, J., Hua, Y., Yang, R., Luo, Y., Lu, H., Wang, Y., et al. (2022). Bio-inspired autonomous learning algorithm with application to mobile robot obstacle avoidance. Front. Neurosci. 16, 905596. doi: 10.3389/fnins.2022.905596 Lobov, S. A., Mikhaylov, A. N., Shamshin, M., Makarov, V. A., and Kazantsev, V. B. (2020). Spatial properties of stdp in a self-learning spiking neural network enable controlling a mobile robot. Front. Neurosci. 14, 88. doi: 10.3389/fnins.2020.00088 Macktoobian, M., and Khataminejad, A. T. (2016). On the formal development of behavioral reactive agents: a systematic braitenberg-vehicle approach. arXiv[Preprint].arXiv:1612.03979. doi: 10.48550/arXiv.1612.03979 Miguel-Blanco, A., and Manoonpong, P. (2020). General distributed neural control and sensory adaptation for self-organized locomotion and fast adaptation to damage of walking robots. Front. Neural Circ. 14, 46. doi: 10.3389/fncir.2020.00046 Ngamkajornwiwat, P., Homchanthanakul, J., Teerakittikul, P., and Manoonpong, P. (2020). Bio-inspired adaptive locomotion control system for online adaptation of a walking robot on complex terrains. IEEE Access 8, 91587–91602. doi: 10.1109/ACCESS.2020.2992794 Open Source Robotics Foundation, I. (2020). Turtlebot3. Available online at: http:// wiki.ros.org/Robots/TurtleBot Pardo-Cabrera, J., Rivero-Ortega, J. D., Hurtado-López, J., and Ramírez-Moreno, D. F. (2022). Bio-inspired navigation and exploration system for a hexapod robotic platform. Eng. Res. Express 4, 025019. doi: 10.1088/2631-8695/ac6bde Ramirez-Moreno, D., and Hurtado-Lopez, J. (2014). Modelamiento Y Simulación De Circuitos Sipnáticos Sensoriomotores: Introducción a la Neurobiología Computacional. Calle: Universidad Autónoma de Occidente. Ramirez-Moreno, D. F., and Sejnowski, T. J. (2012). A computational model for the modulation of the prepulse inhibition of the acoustic startle reflex. Biol. Cybern. 106, 169–176. doi: 10.1007/s00422-012-0485-7 Robotics, O. (2021). Ros (robot operation system). Available online at: https://www. ros.org/ Robotis (2022). Turtlebot3 specifications. Available online at: https://emanual.robotis. com/docs/en/platform/turtlebot3/features/ Shim, M. S., and Li, P. (2017). “Biologically inspired reinforcement learning for mobile robot collision avoidance,” in 2017 International Joint Conference on Neural Networks (IJCNN), 3098–3105. doi: 10.1109/IJCNN.2017.7966242. Available online at: https://ieeexplore.ieee.org/document/7966242 Suzuki, S., Kano, T., Ijspeert, A. J., and Ishiguro, A. (2021). Sprawling quadruped robot driven by decentralized control with cross-coupled sensory feedback between legs and trunk. Front. Neurorobot. 14, 607455. doi: 10.3389/fnbot.2020.607455 Thor, M., Strohmer, B., and Manoonpong, P. (2021). Locomotion control with frequency and motor pattern adaptations. Front. Neural Circ. 15, 743888. doi: 10.3389/fncir.2021.743888 Wei, H., Bu, Y., and Dai, D. (2017). A decision-making model based on a spiking neural circuit and synaptic plasticity. Cogn. Neurodyn. 11, 415–431. doi: 10.1007/s11571-017-9436-2 Wilson, H. R., and Cowan, J. D. (1972). Excitatory and inhibitory interactions in localized populations of model neurons. Biophys. J. 12, 1–24. doi: 10.1016/S0006-3495(72)86068-5 Yan, Z., Fabresse, L., Laval, J., and Bouraqadi, N. (2015). “Metrics for performance benchmarking of multi-robot exploration,” in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (Hamburg: IEEE), 3407–3414. Zahra, O., Tolu, S., Zhou, P., Duan, A., and Navarro-Alarcon, D. (2022). A bio-inspired mechanism for learning robot motion from mirrored human demonstrations. Front. Neurorobot. 16, 826410. doi: 10.3389/fnbot.2022.826410 Zhao, F., Zeng, Y., Guo, A., Su, H., and Xu, B. (2020). A neural algorithm for drosophila linear and nonlinear decision-making. Sci. Rep. 10, 1–16. doi: 10.1038/s41598-020-75628-y |
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Guerrero-Criollo, Roberto JoséCastaño-López, Jason AlejandroHurtado López, Juliánvirtual::5333-1Ramírez Moreno, David Fernandovirtual::5334-12024-04-17T16:21:07Z2024-04-17T16:21:07Z2023-02-03Guerrero-Criollo, R. J.; Castaño-López, J. A.; Hurtado-López, J.; o Ramírez-Moreno, D.F. (2023). Bio-inspired neural networks for decision-making mechanisms and neuromodulation for motor control in a differential robot. Frontiers in Neurorobotics. p.p. 1-16. DOI 10.3389/fnbot.2023.1078074https://hdl.handle.net/10614/15545DOI 10.3389/fnbot.2023.10780741662-5218Universidad Autónoma de OccidenteRespositorio Educativo Digital UAOhttps://red.uao.edu.co/The aim of this work is to propose bio-inspired neural networks for decision-making mechanisms and modulation of motor control of an automaton. In this work, we have adapted and applied cortical synaptic circuits, such as short-term memory circuits, winner-take-all (WTA) class competitive neural networks, modulation neural networks, and nonlinear oscillation circuits, in order to make the automaton able to avoid obstacles and explore simulated and real environments. The performance achieved by using biologically inspired neural networks to solve the task at hand is similar to that of several works mentioned in the specialized literature. Furthermore, this work contributed to bridging the fields of computational neuroscience and robotics16 páginasapplication/pdfengFrontiers Media S.A.SwitzerlandDerechos reservados - Frontiers Media S.A., 2023https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2Bio-inspired neural networks for decision-making mechanisms and neuromodulation for motor control in a differential robotArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85161Frontiers in neuroroboticsCao, Z., Cheng, L., Zhou, C., Gu, N., Wang, X., and Tan, M. (2015). Spiking neural network-based target tracking control for autonomous mobile robots. Neural Comput. Appl. 26, 1839–1847. doi: 10.1007/s00521-015-1848-5Foundation, O. S. R. (2014). Gazebo. Available online at: http://gazebosim.org/Guerrero-Criollo, R. J., Castaño-López, J. A., Díaz-Cuchala, R. E., David Rozo- Giraldo, Y., and Ramirez-Moreno, D. F. (2022). “Design and simulation of a bio-inspired neural network for the motor control of a mobile automaton,” in 2022 IEEE Colombian Conference on Applications of Computational Intelligence (ColCACI) (Cali: IEEE), 1–6.Héricé, C., Khalil, R., Moftah, M., Boraud, T., Guthrie, M., and Garenne, A. (2016). Decision making under uncertainty in a spiking neural network model of the basal ganglia. J. Integr. Neurosci. 15, 515–53. doi: 10.1142/S021963521650028XHikosaka, O., Ghazizadeh, A., Griggs, W., and Amita, H. (2018). Parallel basal ganglia circuits for decision making. J. Neural Transm. 125, 515–529. doi: 10.1007/s00702-017-1691-1Hurtado-López, J., and Ramirez-Moreno, D. F. (2019). Dynamics of a neural circuit that mediates social and nonsocial behaviors. Int. J. Bifurcat. Chaos 29, 1950138. doi: 10.1142/S0218127419501384Hurtado-López, J., Ramirez-Moreno, D. F., and Sejnowski, T. J. (2017). Decisionmaking neural circuits mediating social behaviors. J. Comput. Neurosci. 43, 127–142. doi: 10.1007/s10827-017-0654-8Liu, J., Hua, Y., Yang, R., Luo, Y., Lu, H., Wang, Y., et al. (2022). Bio-inspired autonomous learning algorithm with application to mobile robot obstacle avoidance. Front. Neurosci. 16, 905596. doi: 10.3389/fnins.2022.905596Lobov, S. A., Mikhaylov, A. N., Shamshin, M., Makarov, V. A., and Kazantsev, V. B. (2020). Spatial properties of stdp in a self-learning spiking neural network enable controlling a mobile robot. Front. Neurosci. 14, 88. doi: 10.3389/fnins.2020.00088Macktoobian, M., and Khataminejad, A. T. (2016). On the formal development of behavioral reactive agents: a systematic braitenberg-vehicle approach. arXiv[Preprint].arXiv:1612.03979. doi: 10.48550/arXiv.1612.03979Miguel-Blanco, A., and Manoonpong, P. (2020). General distributed neural control and sensory adaptation for self-organized locomotion and fast adaptation to damage of walking robots. Front. Neural Circ. 14, 46. doi: 10.3389/fncir.2020.00046Ngamkajornwiwat, P., Homchanthanakul, J., Teerakittikul, P., and Manoonpong, P. (2020). Bio-inspired adaptive locomotion control system for online adaptation of a walking robot on complex terrains. IEEE Access 8, 91587–91602. doi: 10.1109/ACCESS.2020.2992794Open Source Robotics Foundation, I. (2020). Turtlebot3. Available online at: http:// wiki.ros.org/Robots/TurtleBotPardo-Cabrera, J., Rivero-Ortega, J. D., Hurtado-López, J., and Ramírez-Moreno, D. F. (2022). Bio-inspired navigation and exploration system for a hexapod robotic platform. Eng. Res. Express 4, 025019. doi: 10.1088/2631-8695/ac6bdeRamirez-Moreno, D., and Hurtado-Lopez, J. (2014). Modelamiento Y Simulación De Circuitos Sipnáticos Sensoriomotores: Introducción a la Neurobiología Computacional. Calle: Universidad Autónoma de Occidente.Ramirez-Moreno, D. F., and Sejnowski, T. J. (2012). A computational model for the modulation of the prepulse inhibition of the acoustic startle reflex. Biol. Cybern. 106, 169–176. doi: 10.1007/s00422-012-0485-7Robotics, O. (2021). Ros (robot operation system). Available online at: https://www. ros.org/Robotis (2022). Turtlebot3 specifications. Available online at: https://emanual.robotis. com/docs/en/platform/turtlebot3/features/Shim, M. S., and Li, P. (2017). “Biologically inspired reinforcement learning for mobile robot collision avoidance,” in 2017 International Joint Conference on Neural Networks (IJCNN), 3098–3105. doi: 10.1109/IJCNN.2017.7966242. Available online at: https://ieeexplore.ieee.org/document/7966242Suzuki, S., Kano, T., Ijspeert, A. J., and Ishiguro, A. (2021). Sprawling quadruped robot driven by decentralized control with cross-coupled sensory feedback between legs and trunk. Front. Neurorobot. 14, 607455. doi: 10.3389/fnbot.2020.607455Thor, M., Strohmer, B., and Manoonpong, P. (2021). Locomotion control with frequency and motor pattern adaptations. Front. Neural Circ. 15, 743888. doi: 10.3389/fncir.2021.743888Wei, H., Bu, Y., and Dai, D. (2017). A decision-making model based on a spiking neural circuit and synaptic plasticity. Cogn. Neurodyn. 11, 415–431. doi: 10.1007/s11571-017-9436-2Wilson, H. R., and Cowan, J. D. (1972). Excitatory and inhibitory interactions in localized populations of model neurons. Biophys. J. 12, 1–24. doi: 10.1016/S0006-3495(72)86068-5Yan, Z., Fabresse, L., Laval, J., and Bouraqadi, N. (2015). “Metrics for performance benchmarking of multi-robot exploration,” in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (Hamburg: IEEE), 3407–3414.Zahra, O., Tolu, S., Zhou, P., Duan, A., and Navarro-Alarcon, D. (2022). A bio-inspired mechanism for learning robot motion from mirrored human demonstrations. Front. Neurorobot. 16, 826410. doi: 10.3389/fnbot.2022.826410Zhao, F., Zeng, Y., Guo, A., Su, H., and Xu, B. (2020). A neural algorithm for drosophila linear and nonlinear decision-making. Sci. Rep. 10, 1–16. doi: 10.1038/s41598-020-75628-yBio-inspired neural networkNeuromodulation networkAdaptation stageSignal processingDierential robotExploration behaviorAutomaComunidad generalPublication77636374-92e2-4d63-9b49-bdb0a2ea1182virtual::5333-161e20236-82c5-4dcc-b05c-0eaa9ac06b11virtual::5334-177636374-92e2-4d63-9b49-bdb0a2ea1182virtual::5333-161e20236-82c5-4dcc-b05c-0eaa9ac06b11virtual::5334-1https://scholar.google.com/citations?user=7Pqx31YAAAAJ&hl=esvirtual::5333-1https://scholar.google.com/citations?user=RTce1fkAAAAJ&hl=esvirtual::5334-10000-0002-3773-0598virtual::5333-10000-0003-2372-3554virtual::5334-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000828963virtual::5333-1https://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0000353744virtual::5334-1ORIGINALBio_inspired_neural_networks_for_decision_making_mechanisms_and_neuromodulation_for_motor_control_in_a_differential_robot.pdfBio_inspired_neural_networks_for_decision_making_mechanisms_and_neuromodulation_for_motor_control_in_a_differential_robot.pdfArchivo texto completo del artículo de revista, PDFapplication/pdf3170663https://red.uao.edu.co/bitstreams/285a8ab6-bf48-4156-a67e-dfa071693271/downloadbd3eeaf7e4bf6285ddab128e404e724cMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81672https://red.uao.edu.co/bitstreams/d3a7b3c5-d245-4712-a66b-28e7d880b148/download6987b791264a2b5525252450f99b10d1MD52TEXTBio_inspired_neural_networks_for_decision_making_mechanisms_and_neuromodulation_for_motor_control_in_a_differential_robot.pdf.txtBio_inspired_neural_networks_for_decision_making_mechanisms_and_neuromodulation_for_motor_control_in_a_differential_robot.pdf.txtExtracted texttext/plain60051https://red.uao.edu.co/bitstreams/e76fea4c-fd02-4c4c-bb82-43d2a95c352b/download9075a831c1736b669e5b2005fbf6cdddMD53THUMBNAILBio_inspired_neural_networks_for_decision_making_mechanisms_and_neuromodulation_for_motor_control_in_a_differential_robot.pdf.jpgBio_inspired_neural_networks_for_decision_making_mechanisms_and_neuromodulation_for_motor_control_in_a_differential_robot.pdf.jpgGenerated Thumbnailimage/jpeg12240https://red.uao.edu.co/bitstreams/2670e181-6e54-4444-acf7-c75f96c3a1e9/downloaddcfef6ba3a12088dd948ef56aaa998bcMD5410614/15545oai:red.uao.edu.co:10614/155452024-04-18 03:01:56.826https://creativecommons.org/licenses/by-nc-nd/4.0/Derechos reservados - Frontiers Media S.A., 2023open.accesshttps://red.uao.edu.coRepositorio Digital Universidad Autonoma de Occidenterepositorio@uao.edu.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 |