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Hamburg S, Jimenez Rodriguez A, Htet A, Di Nuovo A. Active Inference for Learning and Development in Embodied Neuromorphic Agents. ENTROPY (BASEL, SWITZERLAND) 2024; 26:582. [PMID: 39056944 PMCID: PMC11276484 DOI: 10.3390/e26070582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 06/23/2024] [Accepted: 06/27/2024] [Indexed: 07/28/2024]
Abstract
Taking inspiration from humans can help catalyse embodied AI solutions for important real-world applications. Current human-inspired tools include neuromorphic systems and the developmental approach to learning. However, this developmental neurorobotics approach is currently lacking important frameworks for human-like computation and learning. We propose that human-like computation is inherently embodied, with its interface to the world being neuromorphic, and its learning processes operating across different timescales. These constraints necessitate a unified framework: active inference, underpinned by the free energy principle (FEP). Herein, we describe theoretical and empirical support for leveraging this framework in embodied neuromorphic agents with autonomous mental development. We additionally outline current implementation approaches (including toolboxes) and challenges, and we provide suggestions for next steps to catalyse this important field.
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Affiliation(s)
- Sarah Hamburg
- Department of Computing, Sheffield Hallam University, Sheffield S1 1WB, UK; (A.J.R.); (A.H.); (A.D.N.)
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2
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Cruz F, Solis MA, Navarro-Guerrero N. Editorial: Cognitive inspired aspects of robot learning. Front Neurorobot 2023; 17:1256788. [PMID: 37692887 PMCID: PMC10484559 DOI: 10.3389/fnbot.2023.1256788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 08/09/2023] [Indexed: 09/12/2023] Open
Affiliation(s)
- Francisco Cruz
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia
- Escuela de Ingeniería, Universidad Central de Chile, Santiago, Chile
| | - Miguel A. Solis
- Facultad de Ingenieria, Universidad Andres Bello, Santiago, Chile
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3
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Ranieri CM, Moioli RC, Vargas PA, Romero RAF. A neurorobotics approach to behaviour selection based on human activity recognition. Cogn Neurodyn 2023; 17:1009-1028. [PMID: 37522044 PMCID: PMC10374508 DOI: 10.1007/s11571-022-09886-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Revised: 08/04/2022] [Accepted: 09/14/2022] [Indexed: 11/03/2022] Open
Abstract
Behaviour selection has been an active research topic for robotics, in particular in the field of human-robot interaction. For a robot to interact autonomously and effectively with humans, the coupling between techniques for human activity recognition and robot behaviour selection is of paramount importance. However, most approaches to date consist of deterministic associations between the recognised activities and the robot behaviours, neglecting the uncertainty inherent to sequential predictions in real-time applications. In this paper, we address this gap by presenting an initial neurorobotics model that embeds, in a simulated robot, computational models of parts of the mammalian brain that resembles neurophysiological aspects of the basal ganglia-thalamus-cortex (BG-T-C) circuit, coupled with human activity recognition techniques. A robotics simulation environment was developed for assessing the model, where a mobile robot accomplished tasks by using behaviour selection in accordance with the activity being performed by the inhabitant of an intelligent home. Initial results revealed that the initial neurorobotics model is advantageous, especially considering the coupling between the most accurate activity recognition approaches and the computational models of more complex animals.
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Affiliation(s)
- Caetano M. Ranieri
- Institute of Mathematical and Computer Sciences, University of Sao Paulo, Avenida Trabalhador Sao Carlense, 400, Sao Carlos, SP 13566-590 Brazil
| | - Renan C. Moioli
- Bioinformatics Multidisciplinary Environment (BioME), Digital Metropolis Institute, Federal University of Rio Grande do Norte, Avenida Senador Salgado Filho, 3000, Natal, RN 59078-970 Brazil
| | - Patricia A. Vargas
- Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, EH14 4AS Scotland, UK
| | - Roseli A. F. Romero
- Institute of Mathematical and Computer Sciences, University of Sao Paulo, Avenida Trabalhador Sao Carlense, 400, Sao Carlos, SP 13566-590 Brazil
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4
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Prescott TJ, Wilson SP. Understanding brain functional architecture through robotics. Sci Robot 2023; 8:eadg6014. [PMID: 37256968 DOI: 10.1126/scirobotics.adg6014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 05/05/2023] [Indexed: 06/02/2023]
Abstract
Robotics is increasingly seen as a useful test bed for computational models of the brain functional architecture underlying animal behavior. We provide an overview of past and current work, focusing on probabilistic and dynamical models, including approaches premised on the free energy principle, situating this endeavor in relation to evidence that the brain constitutes a layered control system. We argue that future neurorobotic models should integrate multiple neurobiological constraints and be hybrid in nature.
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Affiliation(s)
- Tony J Prescott
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Stuart P Wilson
- Department of Computer Science, University of Sheffield, Sheffield, UK
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5
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Khan MS, Olds JL. When neuro-robots go wrong: A review. Front Neurorobot 2023; 17:1112839. [PMID: 36819005 PMCID: PMC9935594 DOI: 10.3389/fnbot.2023.1112839] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 01/19/2023] [Indexed: 02/05/2023] Open
Abstract
Neuro-robots are a class of autonomous machines that, in their architecture, mimic aspects of the human brain and cognition. As such, they represent unique artifacts created by humans based on human understanding of healthy human brains. European Union's Convention on Roboethics 2025 states that the design of all robots (including neuro-robots) must include provisions for the complete traceability of the robots' actions, analogous to an aircraft's flight data recorder. At the same time, one can anticipate rising instances of neuro-robotic failure, as they operate on imperfect data in real environments, and the underlying AI behind such neuro-robots has yet to achieve explainability. This paper reviews the trajectory of the technology used in neuro-robots and accompanying failures. The failures demand an explanation. While drawing on existing explainable AI research, we argue explainability in AI limits the same in neuro-robots. In order to make robots more explainable, we suggest potential pathways for future research.
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6
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Renaudo E, Zech P, Chatila R, Khamassi M. Editorial: Computational models of affordance for robotics. Front Neurorobot 2022; 16:1045355. [PMID: 36277333 PMCID: PMC9583360 DOI: 10.3389/fnbot.2022.1045355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 09/20/2022] [Indexed: 11/25/2022] Open
Affiliation(s)
- Erwan Renaudo
- Intelligent and Interactive Systems, Department of Computer Science, University of Innsbruck, Innsbruck, Austria
- *Correspondence: Erwan Renaudo
| | - Philipp Zech
- Quality Engineering, Department of Computer Science, University of Innsbruck, Innsbruck, Austria
| | - Raja Chatila
- Institute of Intelligent Systems and Robotics, Sorbonne Université, CNRS, Paris, France
| | - Mehdi Khamassi
- Institute of Intelligent Systems and Robotics, Sorbonne Université, CNRS, Paris, France
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7
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Krauhausen I, Koutsouras DA, Melianas A, Keene ST, Lieberth K, Ledanseur H, Sheelamanthula R, Giovannitti A, Torricelli F, Mcculloch I, Blom PWM, Salleo A, van de Burgt Y, Gkoupidenis P. Organic neuromorphic electronics for sensorimotor integration and learning in robotics. SCIENCE ADVANCES 2021; 7:eabl5068. [PMID: 34890232 PMCID: PMC8664264 DOI: 10.1126/sciadv.abl5068] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
In living organisms, sensory and motor processes are distributed, locally merged, and capable of forming dynamic sensorimotor associations. We introduce a simple and efficient organic neuromorphic circuit for local sensorimotor merging and processing on a robot that is placed in a maze. While the robot is exposed to external environmental stimuli, visuomotor associations are formed on the adaptable neuromorphic circuit. With this on-chip sensorimotor integration, the robot learns to follow a path to the exit of a maze, while being guided by visually indicated paths. The ease of processability of organic neuromorphic electronics and their unconventional form factors, in combination with education-purpose robotics, showcase a promising approach of an affordable, versatile, and readily accessible platform for exploring, designing, and evaluating behavioral intelligence through decentralized sensorimotor integration.
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Affiliation(s)
- Imke Krauhausen
- Max Planck Institute for Polymer Research, Mainz, Germany
- Microsystems, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, Netherlands
| | | | - Armantas Melianas
- Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA
- Exponent, 149 Commonwealth Dr, Menlo Park, CA 94025, USA
| | - Scott T. Keene
- Department of Engineering, University of Cambridge, Cambridge, UK
| | | | | | - Rajendar Sheelamanthula
- KAUST Solar Center, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Alexander Giovannitti
- Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA
| | - Fabrizio Torricelli
- Department of Information Engineering, University of Brescia, 25123 Brescia, Italy
| | - Iain Mcculloch
- KAUST Solar Center, Physical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Department of Chemistry, University of Oxford, Oxford, UK
| | | | - Alberto Salleo
- Department of Materials Science and Engineering, Stanford University, Stanford, CA 94305, USA
- Corresponding author. (A.S.); (Y.v.d.B); (P.G.)
| | - Yoeri van de Burgt
- Microsystems, Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, Netherlands
- Corresponding author. (A.S.); (Y.v.d.B); (P.G.)
| | - Paschalis Gkoupidenis
- Max Planck Institute for Polymer Research, Mainz, Germany
- Corresponding author. (A.S.); (Y.v.d.B); (P.G.)
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8
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Pimentel JM, Moioli RC, de Araujo MFP, Ranieri CM, Romero RAF, Broz F, Vargas PA. Neuro4PD: An Initial Neurorobotics Model of Parkinson's Disease. Front Neurorobot 2021; 15:640449. [PMID: 34276331 PMCID: PMC8283825 DOI: 10.3389/fnbot.2021.640449] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 05/31/2021] [Indexed: 02/05/2023] Open
Abstract
In this work, we present the first steps toward the creation of a new neurorobotics model of Parkinson's Disease (PD) that embeds, for the first time in a real robot, a well-established computational model of PD. PD mostly affects the modulation of movement in humans. The number of people suffering from this neurodegenerative disease is set to double in the next 15 years and there is still no cure. With the new model we were capable to further explore the dynamics of the disease using a humanoid robot. Results show that the embedded model under both conditions, healthy and parkinsonian, was capable of performing a simple behavioural task with different levels of motor disturbance. We believe that this neurorobotics model is a stepping stone to the development of more sophisticated models that could eventually test and inform new PD therapies and help to reduce and replace animals in research.
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Affiliation(s)
- Jhielson M. Pimentel
- Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, United Kingdom
| | - Renan C. Moioli
- Bioinformatics Multidisciplinary Environment, Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Brazil
| | | | | | | | - Frank Broz
- Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, United Kingdom
| | - Patricia A. Vargas
- Edinburgh Centre for Robotics, Heriot-Watt University, Edinburgh, United Kingdom
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9
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Zahra O, Tolu S, Navarro-Alarcon D. Differential mapping spiking neural network for sensor-based robot control. BIOINSPIRATION & BIOMIMETICS 2021; 16:036008. [PMID: 33706302 DOI: 10.1088/1748-3190/abedce] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 03/11/2021] [Indexed: 06/12/2023]
Abstract
In this work, a spiking neural network (SNN) is proposed for approximating differential sensorimotor maps of robotic systems. The computed model is used as a local Jacobian-like projection that relates changes in sensor space to changes in motor space. The SNN consists of an input (sensory) layer and an output (motor) layer connected through plastic synapses, with inter-inhibitory connections at the output layer. Spiking neurons are modeled as Izhikevich neurons with a synaptic learning rule based on spike timing-dependent plasticity. Feedback data from proprioceptive and exteroceptive sensors are encoded and fed into the input layer through a motor babbling process. A guideline for tuning the network parameters is proposed and applied along with the particle swarm optimization technique. Our proposed control architecture takes advantage of biologically plausible tools of an SNN to achieve the target reaching task while minimizing deviations from the desired path, and consequently minimizing the execution time. Thanks to the chosen architecture and optimization of the parameters, the number of neurons and the amount of data required for training are considerably low. The SNN is capable of handling noisy sensor readings to guide the robot movements in real-time. Experimental results are presented to validate the control methodology with a vision-guided robot.
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Affiliation(s)
- Omar Zahra
- The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China
| | | | - David Navarro-Alarcon
- The Hong Kong Polytechnic University, Hong Kong Special Administrative Region of China
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10
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Davies S, Lucas A, Ricolfe-Viala C, Di Nuovo A. A Database for Learning Numbers by Visual Finger Recognition in Developmental Neuro-Robotics. Front Neurorobot 2021; 15:619504. [PMID: 33737873 PMCID: PMC7960766 DOI: 10.3389/fnbot.2021.619504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 02/01/2021] [Indexed: 11/13/2022] Open
Abstract
Numerical cognition is a fundamental component of human intelligence that has not been fully understood yet. Indeed, it is a subject of research in many disciplines, e.g., neuroscience, education, cognitive and developmental psychology, philosophy of mathematics, linguistics. In Artificial Intelligence, aspects of numerical cognition have been modelled through neural networks to replicate and analytically study children behaviours. However, artificial models need to incorporate realistic sensory-motor information from the body to fully mimic the children's learning behaviours, e.g., the use of fingers to learn and manipulate numbers. To this end, this article presents a database of images, focused on number representation with fingers using both human and robot hands, which can constitute the base for building new realistic models of numerical cognition in humanoid robots, enabling a grounded learning approach in developmental autonomous agents. The article provides a benchmark analysis of the datasets in the database that are used to train, validate, and test five state-of-the art deep neural networks, which are compared for classification accuracy together with an analysis of the computational requirements of each network. The discussion highlights the trade-off between speed and precision in the detection, which is required for realistic applications in robotics.
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Affiliation(s)
- Sergio Davies
- Department of Computing, Sheffield Hallam University, Sheffield, United Kingdom
| | - Alexandr Lucas
- Department of Computing, Sheffield Hallam University, Sheffield, United Kingdom.,Department of Computer Science, The University of Sheffield, Sheffield, United Kingdom
| | - Carlos Ricolfe-Viala
- Instituto de Automàtica e Informàtica Industrial, Universitat Politecnica de Valencia, Valencia, Spain
| | - Alessandro Di Nuovo
- Department of Computing, Sheffield Hallam University, Sheffield, United Kingdom
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11
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Yu S, Wu J, Xu H, Sun R, Sun L. Robustness Improvement of Visual Templates Matching Based on Frequency-Tuned Model in RatSLAM. Front Neurorobot 2020; 14:568091. [PMID: 33101002 PMCID: PMC7546858 DOI: 10.3389/fnbot.2020.568091] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 08/18/2020] [Indexed: 11/14/2022] Open
Abstract
This paper describes an improved brain-inspired simultaneous localization and mapping (RatSLAM) that extracts visual features from saliency maps using a frequency-tuned (FT) model. In the traditional RatSLAM algorithm, the visual template feature is organized as a one-dimensional vector whose values only depend on pixel intensity; therefore, this feature is susceptible to changes in illumination intensity. In contrast to this approach, which directly generates visual templates from raw RGB images, we propose an FT model that converts RGB images into saliency maps to obtain visual templates. The visual templates extracted from the saliency maps contain more of the feature information contained within the original images. Our experimental results demonstrate that the accuracy of loop closure detection was improved, as measured by the number of loop closures detected by our method compared with the traditional RatSLAM system. We additionally verified that the proposed FT model-based visual templates improve the robustness of familiar visual scene identification by RatSLAM.
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Affiliation(s)
- Shumei Yu
- School of Mechanical and Electrical Engineering, Soochow University, Suzhou, China
| | - Junyi Wu
- School of Mechanical and Electrical Engineering, Soochow University, Suzhou, China
| | - Haidong Xu
- School of Mechanical and Electrical Engineering, Soochow University, Suzhou, China
| | - Rongchuan Sun
- School of Mechanical and Electrical Engineering, Soochow University, Suzhou, China
| | - Lining Sun
- School of Mechanical and Electrical Engineering, Soochow University, Suzhou, China
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12
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Zou Z, Zhao R, Wu Y, Yang Z, Tian L, Wu S, Wang G, Yu Y, Zhao Q, Chen M, Pei J, Chen F, Zhang Y, Song S, Zhao M, Shi L. A hybrid and scalable brain-inspired robotic platform. Sci Rep 2020; 10:18160. [PMID: 33097742 PMCID: PMC7584638 DOI: 10.1038/s41598-020-73366-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 08/31/2020] [Indexed: 01/04/2023] Open
Abstract
Recent years have witnessed tremendous progress of intelligent robots brought about by mimicking human intelligence. However, current robots are still far from being able to handle multiple tasks in a dynamic environment as efficiently as humans. To cope with complexity and variability, further progress toward scalability and adaptability are essential for intelligent robots. Here, we report a brain-inspired robotic platform implemented by an unmanned bicycle that exhibits scalability of network scale, quantity and diversity to handle the changing needs of different scenarios. The platform adopts rich coding schemes and a trainable and scalable neural state machine, enabling flexible cooperation of hybrid networks. In addition, an embedded system is developed using a cross-paradigm neuromorphic chip to facilitate the implementation of diverse neural networks in spike or non-spike form. The platform achieved various real-time tasks concurrently in different real-world scenarios, providing a new pathway to enhance robots' intelligence.
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Affiliation(s)
- Zhe Zou
- Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, and Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Rong Zhao
- Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, and Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Yujie Wu
- Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, and Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Zheyu Yang
- Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, and Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Lei Tian
- Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, and Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Shuang Wu
- Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, and Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Guanrui Wang
- Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, and Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Yongchao Yu
- Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Qi Zhao
- Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, and Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Mingwang Chen
- Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, and Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Jing Pei
- Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, and Department of Precision Instrument, Tsinghua University, Beijing, 100084, China
| | - Feng Chen
- Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Youhui Zhang
- Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China
| | - Sen Song
- Department of Biomedical Engineering, Tsinghua University, Beijing, 100084, China
| | - Mingguo Zhao
- Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, and Department of Precision Instrument, Tsinghua University, Beijing, 100084, China.
- Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Luping Shi
- Center for Brain-Inspired Computing Research (CBICR), Beijing Innovation Center for Future Chip, Optical Memory National Engineering Research Center, and Department of Precision Instrument, Tsinghua University, Beijing, 100084, China.
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13
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Chen K, Hwu T, Kashyap HJ, Krichmar JL, Stewart K, Xing J, Zou X. Neurorobots as a Means Toward Neuroethology and Explainable AI. Front Neurorobot 2020; 14:570308. [PMID: 33192435 PMCID: PMC7604467 DOI: 10.3389/fnbot.2020.570308] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Accepted: 08/25/2020] [Indexed: 12/18/2022] Open
Abstract
Understanding why deep neural networks and machine learning algorithms act as they do is a difficult endeavor. Neuroscientists are faced with similar problems. One way biologists address this issue is by closely observing behavior while recording neurons or manipulating brain circuits. This has been called neuroethology. In a similar way, neurorobotics can be used to explain how neural network activity leads to behavior. In real world settings, neurorobots have been shown to perform behaviors analogous to animals. Moreover, a neuroroboticist has total control over the network, and by analyzing different neural groups or studying the effect of network perturbations (e.g., simulated lesions), they may be able to explain how the robot's behavior arises from artificial brain activity. In this paper, we review neurorobot experiments by focusing on how the robot's behavior leads to a qualitative and quantitative explanation of neural activity, and vice versa, that is, how neural activity leads to behavior. We suggest that using neurorobots as a form of computational neuroethology can be a powerful methodology for understanding neuroscience, as well as for artificial intelligence and machine learning.
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Affiliation(s)
- Kexin Chen
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States
| | - Tiffany Hwu
- HRL Laboratories (formerly Hughes Research Laboratory), LLC, Malibu, CA, United States
| | - Hirak J Kashyap
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
| | - Jeffrey L Krichmar
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States.,Department of Computer Science, University of California, Irvine, Irvine, CA, United States
| | - Kenneth Stewart
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
| | - Jinwei Xing
- Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States
| | - Xinyun Zou
- Department of Computer Science, University of California, Irvine, Irvine, CA, United States
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14
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Cyr A, Thériault F, Chartier S. Revisiting the XOR problem: a neurorobotic implementation. Neural Comput Appl 2020. [DOI: 10.1007/s00521-019-04522-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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15
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Harris CA, Guerri L, Mircic S, Reining Z, Amorim M, Jović Ð, Wallace W, DeBoer J, Gage GJ. Neurorobotics Workshop for High School Students Promotes Competence and Confidence in Computational Neuroscience. Front Neurorobot 2020; 14:6. [PMID: 32116636 PMCID: PMC7033397 DOI: 10.3389/fnbot.2020.00006] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 01/27/2020] [Indexed: 11/25/2022] Open
Abstract
Understanding the brain is a fascinating challenge, captivating the scientific community and the public alike. The lack of effective treatment for most brain disorders makes the training of the next generation of neuroscientists, engineers and physicians a key concern. Over the past decade there has been a growing effort to introduce neuroscience in primary and secondary schools, however, hands-on laboratories have been limited to anatomical or electrophysiological activities. Modern neuroscience research labs are increasingly using computational tools to model circuits of the brain to understand information processing. Here we introduce the use of neurorobots – robots controlled by computer models of biological brains – as an introduction to computational neuroscience in the classroom. Neurorobotics has enormous potential as an education technology because it combines multiple activities with clear educational benefits including neuroscience, active learning, and robotics. We describe a 1-week introductory neurorobot workshop that teaches high school students how to use neurorobots to investigate key concepts in neuroscience, including spiking neural networks, synaptic plasticity, and adaptive action selection. Our do-it-yourself (DIY) neurorobot uses wheels, a camera, a speaker, and a distance sensor to interact with its environment, and can be built from generic parts costing about $170 in under 4 h. Our Neurorobot App visualizes the neurorobot’s visual input and brain activity in real-time, and enables students to design new brains and deliver dopamine-like reward signals to reinforce chosen behaviors. We ran the neurorobot workshop at two high schools (n = 295 students total) and found significant improvement in students’ understanding of key neuroscience concepts and in students’ confidence in neuroscience, as assessed by a pre/post workshop survey. Here we provide DIY hardware assembly instructions, discuss our open-source Neurorobot App and demonstrate how to teach the Neurorobot Workshop. By doing this we hope to accelerate research in educational neurorobotics and promote the use of neurorobots to teach computational neuroscience in high school.
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Affiliation(s)
| | - Lucia Guerri
- Laboratory of Neurogenetics, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD, United States
| | | | | | | | - Ðorđe Jović
- Backyard Brains, Inc., Ann Arbor, MI, United States
| | | | - Jennifer DeBoer
- School of Engineering Education, Purdue University, West Lafayette, IN, United States
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Iyengar RS, Pithapuram MV, Singh AK, Raghavan M. Curated Model Development Using NEUROiD: A Web-Based NEUROmotor Integration and Design Platform. Front Neuroinform 2019; 13:56. [PMID: 31440153 PMCID: PMC6693358 DOI: 10.3389/fninf.2019.00056] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Accepted: 07/11/2019] [Indexed: 11/24/2022] Open
Abstract
Decades of research on neuromotor circuits and systems has provided valuable information on neuronal control of movement. Computational models of several elements of the neuromotor system have been developed at various scales, from sub-cellular to system. While several small models abound, their structured integration is the key to building larger and more biologically realistic models which can predict the behavior of the system in different scenarios. This effort calls for integration of elements across neuroscience and musculoskeletal biomechanics. There is also a need for development of methods and tools for structured integration that yield larger in silico models demonstrating a set of desired system responses. We take a small step in this direction with the NEUROmotor integration and Design (NEUROiD) platform. NEUROiD helps integrate results from motor systems anatomy, physiology, and biomechanics into an integrated neuromotor system model. Simulation and visualization of the model across multiple scales is supported. Standard electrophysiological operations such as slicing, current injection, recording of membrane potential, and local field potential are part of NEUROiD. The platform allows traceability of model parameters to primary literature. We illustrate the power and utility of NEUROiD by building a simple ankle model and its controlling neural circuitry by curating a set of published components. NEUROiD allows researchers to utilize remote high-performance computers for simulation, while controlling the model using a web browser.
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Affiliation(s)
- Raghu Sesha Iyengar
- Spine Labs, Department of Biomedical Engineering, Indian Institute of Technology, Hyderabad, India
| | - Madhav Vinodh Pithapuram
- Spine Labs, Department of Biomedical Engineering, Indian Institute of Technology, Hyderabad, India
| | - Avinash Kumar Singh
- Spine Labs, Department of Biomedical Engineering, Indian Institute of Technology, Hyderabad, India
| | - Mohan Raghavan
- Spine Labs, Department of Biomedical Engineering, Indian Institute of Technology, Hyderabad, India
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Spatial Concept Learning: A Spiking Neural Network Implementation in Virtual and Physical Robots. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2019; 2019:8361369. [PMID: 31065256 PMCID: PMC6466944 DOI: 10.1155/2019/8361369] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Accepted: 03/11/2019] [Indexed: 11/18/2022]
Abstract
This paper proposes an artificial spiking neural network (SNN) sustaining the cognitive abstract process of spatial concept learning, embedded in virtual and real robots. Based on an operant conditioning procedure, the robots learn the relationship of horizontal/vertical and left/right visual stimuli, regardless of their specific pattern composition or their location on the images. Tests with novel patterns and locations were successfully completed after the acquisition learning phase. Results show that the SNN can adapt its behavior in real time when the rewarding rule changes.
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Cyr A, Thériault F, Ross M, Berberian N, Chartier S. Spiking Neurons Integrating Visual Stimuli Orientation and Direction Selectivity in a Robotic Context. Front Neurorobot 2018; 12:75. [PMID: 30524261 PMCID: PMC6256284 DOI: 10.3389/fnbot.2018.00075] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Accepted: 10/31/2018] [Indexed: 11/13/2022] Open
Abstract
Visual motion detection is essential for the survival of many species. The phenomenon includes several spatial properties, not fully understood at the level of a neural circuit. This paper proposes a computational model of a visual motion detector that integrates direction and orientation selectivity features. A recent experiment in the Drosophila model highlights that stimulus orientation influences the neural response of direction cells. However, this interaction and the significance at the behavioral level are currently unknown. As such, another objective of this article is to study the effect of merging these two visual processes when contextualized in a neuro-robotic model and an operant conditioning procedure. In this work, the learning task was solved using an artificial spiking neural network, acting as the brain controller for virtual and physical robots, showing a behavior modulation from the integration of both visual processes.
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Affiliation(s)
- André Cyr
- Conec Laboratory, School of Psychology, Ottawa University, Ottawa, ON, Canada
| | - Frédéric Thériault
- Department of Computer Science, Cégep du Vieux Montréal, Montreal, QC, Canada
| | - Matthew Ross
- Conec Laboratory, School of Psychology, Ottawa University, Ottawa, ON, Canada
| | - Nareg Berberian
- Conec Laboratory, School of Psychology, Ottawa University, Ottawa, ON, Canada
| | - Sylvain Chartier
- Conec Laboratory, School of Psychology, Ottawa University, Ottawa, ON, Canada
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