1
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Balci F, Ben Hamed S, Boraud T, Bouret S, Brochier T, Brun C, Cohen JY, Coutureau E, Deffains M, Doyère V, Gregoriou GG, Heimel JA, Kilavik BE, Lee D, Leuthardt EC, Mainen ZF, Mathis M, Monosov IE, Naudé J, Orsborn AL, Padoa-Schioppa C, Procyk E, Sabatini B, Sallet J, Sandi C, Schall JD, Soltani A, Svoboda K, Wilson CRE, Zimmermann J. A response to claims of emergent intelligence and sentience in a dish. Neuron 2023; 111:604-605. [PMID: 36863319 DOI: 10.1016/j.neuron.2023.02.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 01/11/2023] [Accepted: 02/06/2023] [Indexed: 03/04/2023]
Affiliation(s)
- Fuat Balci
- University of Manitoba, Department of Biological Sciences, 50 Sifton Rd, Winnipeg, MB R3T 2M5, Canada
| | - Suliann Ben Hamed
- Institut des Sciences Cognitives Marc Jeannerod, UMR5229 CNRS Université de Lyon, 67 Boulevard Pinel, 69675 Bron Cedex, France
| | - Thomas Boraud
- University Bordeaux, CNRS, IMN, UMR 5293, 33000 Bordeaux, France; CHU de Bordeaux, IMN Clinique, 33000 Bordeaux, France
| | - Sébastien Bouret
- Institut du Cerveau (ICM), INSERM UMRS 1127, CNRS UMR 7225, Pitié-Salpêtrière Hospital, Paris, France
| | - Thomas Brochier
- Institut de Neurosciences de la Timone (INT), UMR 7289, CNRS, Aix-Marseille Université, Marseille 13005, France
| | - Cédric Brun
- University Bordeaux, CNRS, IMN, UMR 5293, 33000 Bordeaux, France; University Bordeaux Montaigne, Philosophy Department, 33607 Pessac, France
| | | | | | - Marc Deffains
- University Bordeaux, CNRS, IMN, UMR 5293, 33000 Bordeaux, France
| | - Valérie Doyère
- Université Paris-Saclay, CNRS, Institut des Neurosciences Paris-Saclay, UMR9197, 91400 Saclay, France
| | - Georgia G Gregoriou
- University of Crete, Faculty of Medicine and Foundation for Research and Technology Hellas, Institute of Applied and Computational Mathematics, Heraklion 70013, Greece
| | | | - Bjørg Elisabeth Kilavik
- Institut de Neurosciences de la Timone (INT), UMR 7289, CNRS, Aix-Marseille Université, Marseille 13005, France
| | - Daeyeol Lee
- Zanvyl Krieger Mind/Brain Institute, Departments of Psychological and Brain Sciences and Neuroscience, Johns Hopkins University, Baltimore, MD, USA
| | - Eric C Leuthardt
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Mackenzie Mathis
- École Polytechnique Fédérale de Lausanne (EPFL), Brain Mind Institute, Lausanne, Switzerland
| | - Ilya E Monosov
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA.
| | - Jérémie Naudé
- IGF, University of Montpellier, CNRS, INSERM, Montpellier, France
| | - Amy L Orsborn
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA
| | - Camillo Padoa-Schioppa
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
| | - Emmanuel Procyk
- University Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron, France.
| | - Bernardo Sabatini
- HHMI, Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Jérôme Sallet
- University Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron, France
| | - Carmen Sandi
- École Polytechnique Fédérale de Lausanne (EPFL), Brain Mind Institute, Lausanne, Switzerland
| | - Jeffrey D Schall
- Visual Neurophysiology Centre, York University, Toronto, ON, Canada
| | - Alireza Soltani
- Department of Psychological and Brain Sciences, Dartmouth University, Hanover, NH, USA
| | - Karel Svoboda
- Allen Institute for Neural Dynamics, Seattle, WA 98109, USA
| | - Charles R E Wilson
- University Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron, France
| | - Jan Zimmermann
- University of Minnesota, Department of Neuroscience, Center for Magnetic Resonance Research, Minneapolis, MN, USA
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2
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Chen Z, Liang Q, Wei Z, Chen X, Shi Q, Yu Z, Sun T. An Overview of In Vitro Biological Neural Networks for Robot Intelligence. CYBORG AND BIONIC SYSTEMS 2023; 4:0001. [PMID: 37040493 PMCID: PMC10076061 DOI: 10.34133/cbsystems.0001] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 10/17/2022] [Indexed: 01/12/2023] Open
Abstract
In vitro biological neural networks (BNNs) interconnected with robots, so-called BNN-based neurorobotic systems, can interact with the external world, so that they can present some preliminary intelligent behaviors, including learning, memory, robot control, etc. This work aims to provide a comprehensive overview of the intelligent behaviors presented by the BNN-based neurorobotic systems, with a particular focus on those related to robot intelligence. In this work, we first introduce the necessary biological background to understand the 2 characteristics of the BNNs: nonlinear computing capacity and network plasticity. Then, we describe the typical architecture of the BNN-based neurorobotic systems and outline the mainstream techniques to realize such an architecture from 2 aspects: from robots to BNNs and from BNNs to robots. Next, we separate the intelligent behaviors into 2 parts according to whether they rely solely on the computing capacity (computing capacity-dependent) or depend also on the network plasticity (network plasticity-dependent), which are then expounded respectively, with a focus on those related to the realization of robot intelligence. Finally, the development trends and challenges of the BNN-based neurorobotic systems are discussed.
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Affiliation(s)
- Zhe Chen
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, Beijing 10081, China
- Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China
| | - Qian Liang
- Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, Beijing 10081, China
- Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Zihou Wei
- Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, Beijing 10081, China
- Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Xie Chen
- Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, Beijing 10081, China
- Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Qing Shi
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
- Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, Beijing 10081, China
- Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Zhiqiang Yu
- Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, Beijing 10081, China
- Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
| | - Tao Sun
- Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, Beijing 10081, China
- Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China
- School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
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3
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Levin M. Technological Approach to Mind Everywhere: An Experimentally-Grounded Framework for Understanding Diverse Bodies and Minds. Front Syst Neurosci 2022; 16:768201. [PMID: 35401131 PMCID: PMC8988303 DOI: 10.3389/fnsys.2022.768201] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 01/24/2022] [Indexed: 12/11/2022] Open
Abstract
Synthetic biology and bioengineering provide the opportunity to create novel embodied cognitive systems (otherwise known as minds) in a very wide variety of chimeric architectures combining evolved and designed material and software. These advances are disrupting familiar concepts in the philosophy of mind, and require new ways of thinking about and comparing truly diverse intelligences, whose composition and origin are not like any of the available natural model species. In this Perspective, I introduce TAME-Technological Approach to Mind Everywhere-a framework for understanding and manipulating cognition in unconventional substrates. TAME formalizes a non-binary (continuous), empirically-based approach to strongly embodied agency. TAME provides a natural way to think about animal sentience as an instance of collective intelligence of cell groups, arising from dynamics that manifest in similar ways in numerous other substrates. When applied to regenerating/developmental systems, TAME suggests a perspective on morphogenesis as an example of basal cognition. The deep symmetry between problem-solving in anatomical, physiological, transcriptional, and 3D (traditional behavioral) spaces drives specific hypotheses by which cognitive capacities can increase during evolution. An important medium exploited by evolution for joining active subunits into greater agents is developmental bioelectricity, implemented by pre-neural use of ion channels and gap junctions to scale up cell-level feedback loops into anatomical homeostasis. This architecture of multi-scale competency of biological systems has important implications for plasticity of bodies and minds, greatly potentiating evolvability. Considering classical and recent data from the perspectives of computational science, evolutionary biology, and basal cognition, reveals a rich research program with many implications for cognitive science, evolutionary biology, regenerative medicine, and artificial intelligence.
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Affiliation(s)
- Michael Levin
- Allen Discovery Center at Tufts University, Medford, MA, United States
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Cambridge, MA, United States
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4
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Shriver AJ, John TM. Neuroethics and Animals: Report and Recommendations From the University of Pennsylvania Animal Research Neuroethics Workshop. ILAR J 2021; 60:424-433. [PMID: 34370840 PMCID: PMC8767460 DOI: 10.1093/ilar/ilab024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Accepted: 05/20/2021] [Indexed: 11/30/2022] Open
Abstract
Growing awareness of the ethical implications of neuroscience in the early years of the 21st century led to the emergence of the new academic field of “neuroethics,” which studies the ethical implications of developments in the neurosciences. However, despite the acceleration and evolution of neuroscience research on nonhuman animals, the unique ethical issues connected with neuroscience research involving nonhuman animals remain underdiscussed. This is a significant oversight given the central place of animal models in neuroscience. To respond to these concerns, the Center for Neuroscience and Society and the Center for the Interaction of Animals and Society at the University of Pennsylvania hosted a workshop on the “Neuroethics of Animal Research” in Philadelphia, Pennsylvania. At the workshop, expert speakers and attendees discussed ethical issues arising from neuroscience research involving nonhuman animals, including the use of animal models in the study of pain and psychiatric conditions, animal brain-machine interfaces, animal–animal chimeras, cerebral organoids, and the relevance of neuroscience to debates about personhood. This paper highlights important emerging ethical issues based on the discussions at the workshop. This paper includes recommendations for research in the United States from the authors based on the discussions at the workshop, loosely following the format of the 2 Gray Matters reports on neuroethics published by the Presidential Commission for the Study of Bioethical Issues.
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Affiliation(s)
- Adam J Shriver
- W. Maurice Young Centre for Applied Ethics, University of British Columbia, Vancouver, CA.,Center for Neuroscience & Society, University of Pennsylvania, Philadelphia, PA
| | - Tyler M John
- Department of Philosophy, Rutgers University, New Brunswick, New Jersey, USA
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5
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Zeng Y, Ferdous ZI, Zhang W, Xu M, Yu A, Patel D, Post V, Guo X, Berdichevsky Y, Yan Z. Understanding the Impact of Neural Variations and Random Connections on Inference. Front Comput Neurosci 2021; 15:612937. [PMID: 34163343 PMCID: PMC8215547 DOI: 10.3389/fncom.2021.612937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 04/19/2021] [Indexed: 11/19/2022] Open
Abstract
Recent research suggests that in vitro neural networks created from dissociated neurons may be used for computing and performing machine learning tasks. To develop a better artificial intelligent system, a hybrid bio-silicon computer is worth exploring, but its performance is still inferior to that of a silicon-based computer. One reason may be that a living neural network has many intrinsic properties, such as random network connectivity, high network sparsity, and large neural and synaptic variability. These properties may lead to new design considerations, and existing algorithms need to be adjusted for living neural network implementation. This work investigates the impact of neural variations and random connections on inference with learning algorithms. A two-layer hybrid bio-silicon platform is constructed and a five-step design method is proposed for the fast development of living neural network algorithms. Neural variations and dynamics are verified by fitting model parameters with biological experimental results. Random connections are generated under different connection probabilities to vary network sparsity. A multi-layer perceptron algorithm is tested with biological constraints on the MNIST dataset. The results show that a reasonable inference accuracy can be achieved despite the presence of neural variations and random network connections. A new adaptive pre-processing technique is proposed to ensure good learning accuracy with different living neural network sparsity.
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Affiliation(s)
- Yuan Zeng
- Electrical and Computer Engineering Department, Lehigh University, Bethlehem, PA, United States
| | - Zubayer Ibne Ferdous
- Electrical and Computer Engineering Department, Lehigh University, Bethlehem, PA, United States
| | - Weixiang Zhang
- Electrical and Computer Engineering Department, Beihang University, Beijing, China
| | - Mufan Xu
- Electrical and Computer Engineering Department, Beihang University, Beijing, China
| | - Anlan Yu
- Electrical and Computer Engineering Department, Lehigh University, Bethlehem, PA, United States
| | - Drew Patel
- Electrical and Computer Engineering Department, Lehigh University, Bethlehem, PA, United States
| | - Valentin Post
- Electrical and Computer Engineering Department, Lehigh University, Bethlehem, PA, United States
| | - Xiaochen Guo
- Electrical and Computer Engineering Department, Lehigh University, Bethlehem, PA, United States
| | - Yevgeny Berdichevsky
- Electrical and Computer Engineering Department, Lehigh University, Bethlehem, PA, United States.,Bioengineering Department, Lehigh University, Bethlehem, PA, United States
| | - Zhiyuan Yan
- Electrical and Computer Engineering Department, Lehigh University, Bethlehem, PA, United States
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6
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Bongard J, Levin M. Living Things Are Not (20th Century) Machines: Updating Mechanism Metaphors in Light of the Modern Science of Machine Behavior. Front Ecol Evol 2021. [DOI: 10.3389/fevo.2021.650726] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
One of the most useful metaphors for driving scientific and engineering progress has been that of the “machine.” Much controversy exists about the applicability of this concept in the life sciences. Advances in molecular biology have revealed numerous design principles that can be harnessed to understand cells from an engineering perspective, and build novel devices to rationally exploit the laws of chemistry, physics, and computation. At the same time, organicists point to the many unique features of life, especially at larger scales of organization, which have resisted decomposition analysis and artificial implementation. Here, we argue that much of this debate has focused on inessential aspects of machines – classical properties which have been surpassed by advances in modern Machine Behavior and no longer apply. This emerging multidisciplinary field, at the interface of artificial life, machine learning, and synthetic bioengineering, is highlighting the inadequacy of existing definitions. Key terms such as machine, robot, program, software, evolved, designed, etc., need to be revised in light of technological and theoretical advances that have moved past the dated philosophical conceptions that have limited our understanding of both evolved and designed systems. Moving beyond contingent aspects of historical and current machines will enable conceptual tools that embrace inevitable advances in synthetic and hybrid bioengineering and computer science, toward a framework that identifies essential distinctions between fundamental concepts of devices and living agents. Progress in both theory and practical applications requires the establishment of a novel conception of “machines as they could be,” based on the profound lessons of biology at all scales. We sketch a perspective that acknowledges the remarkable, unique aspects of life to help re-define key terms, and identify deep, essential features of concepts for a future in which sharp boundaries between evolved and designed systems will not exist.
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7
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Life, death, and self: Fundamental questions of primitive cognition viewed through the lens of body plasticity and synthetic organisms. Biochem Biophys Res Commun 2020; 564:114-133. [PMID: 33162026 DOI: 10.1016/j.bbrc.2020.10.077] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2020] [Revised: 10/25/2020] [Accepted: 10/28/2020] [Indexed: 12/16/2022]
Abstract
Central to the study of cognition is being able to specify the Subject that is making decisions and owning memories and preferences. However, all real cognitive agents are made of parts (such as brains made of cells). The integration of many active subunits into a coherent Self appearing at a larger scale of organization is one of the fundamental questions of evolutionary cognitive science. Typical biological model systems, whether basal or advanced, have a static anatomical structure which obscures important aspects of the mind-body relationship. Recent advances in bioengineering now make it possible to assemble, disassemble, and recombine biological structures at the cell, organ, and whole organism levels. Regenerative biology and controlled chimerism reveal that studies of cognition in intact, "standard", evolved animal bodies are just a narrow slice of a much bigger and as-yet largely unexplored reality: the incredible plasticity of dynamic morphogenesis of biological forms that house and support diverse types of cognition. The ability to produce living organisms in novel configurations makes clear that traditional concepts, such as body, organism, genetic lineage, death, and memory are not as well-defined as commonly thought, and need considerable revision to account for the possible spectrum of living entities. Here, I review fascinating examples of experimental biology illustrating that the boundaries demarcating somatic and cognitive Selves are fluid, providing an opportunity to sharpen inquiries about how evolution exploits physical forces for multi-scale cognition. Developmental (pre-neural) bioelectricity contributes a novel perspective on how the dynamic control of growth and form of the body evolved into sophisticated cognitive capabilities. Most importantly, the development of functional biobots - synthetic living machines with behavioral capacity - provides a roadmap for greatly expanding our understanding of the origin and capacities of cognition in all of its possible material implementations, especially those that emerge de novo, with no lengthy evolutionary history of matching behavioral programs to bodyplan. Viewing fundamental questions through the lens of new, constructed living forms will have diverse impacts, not only in basic evolutionary biology and cognitive science, but also in regenerative medicine of the brain and in artificial intelligence.
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8
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George R, Chiappalone M, Giugliano M, Levi T, Vassanelli S, Partzsch J, Mayr C. Plasticity and Adaptation in Neuromorphic Biohybrid Systems. iScience 2020; 23:101589. [PMID: 33083749 PMCID: PMC7554028 DOI: 10.1016/j.isci.2020.101589] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Neuromorphic systems take inspiration from the principles of biological information processing to form hardware platforms that enable the large-scale implementation of neural networks. The recent years have seen both advances in the theoretical aspects of spiking neural networks for their use in classification and control tasks and a progress in electrophysiological methods that is pushing the frontiers of intelligent neural interfacing and signal processing technologies. At the forefront of these new technologies, artificial and biological neural networks are tightly coupled, offering a novel "biohybrid" experimental framework for engineers and neurophysiologists. Indeed, biohybrid systems can constitute a new class of neuroprostheses opening important perspectives in the treatment of neurological disorders. Moreover, the use of biologically plausible learning rules allows forming an overall fault-tolerant system of co-developing subsystems. To identify opportunities and challenges in neuromorphic biohybrid systems, we discuss the field from the perspectives of neurobiology, computational neuroscience, and neuromorphic engineering.
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Affiliation(s)
- Richard George
- Department of Electrical Engineering and Information Technology, Technical University of Dresden, Dresden, Germany
| | | | - Michele Giugliano
- Neuroscience Area, International School of Advanced Studies, Trieste, Italy
| | - Timothée Levi
- Laboratoire de l’Intégration du Matéeriau au Systéme, University of Bordeaux, Bordeaux, France
- LIMMS/CNRS, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
| | - Stefano Vassanelli
- Department of Biomedical Sciences and Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Johannes Partzsch
- Department of Electrical Engineering and Information Technology, Technical University of Dresden, Dresden, Germany
| | - Christian Mayr
- Department of Electrical Engineering and Information Technology, Technical University of Dresden, Dresden, Germany
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9
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Allegra Mascaro AL, Falotico E, Petkoski S, Pasquini M, Vannucci L, Tort-Colet N, Conti E, Resta F, Spalletti C, Ramalingasetty ST, von Arnim A, Formento E, Angelidis E, Blixhavn CH, Leergaard TB, Caleo M, Destexhe A, Ijspeert A, Micera S, Laschi C, Jirsa V, Gewaltig MO, Pavone FS. Experimental and Computational Study on Motor Control and Recovery After Stroke: Toward a Constructive Loop Between Experimental and Virtual Embodied Neuroscience. Front Syst Neurosci 2020; 14:31. [PMID: 32733210 PMCID: PMC7359878 DOI: 10.3389/fnsys.2020.00031] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 05/08/2020] [Indexed: 01/22/2023] Open
Abstract
Being able to replicate real experiments with computational simulations is a unique opportunity to refine and validate models with experimental data and redesign the experiments based on simulations. However, since it is technically demanding to model all components of an experiment, traditional approaches to modeling reduce the experimental setups as much as possible. In this study, our goal is to replicate all the relevant features of an experiment on motor control and motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous integration of new experimental data into a computational modeling framework. First, results show that we could reproduce experimental object displacement with high accuracy via the simulated embodiment in the virtual world by feeding a spinal cord model with experimental registration of the cortical activity. Second, by using computational models of multiple granularities, our preliminary results show the possibility of simulating several features of the brain after stroke, from the local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies are proposed to merge the two pipelines. We further suggest that additional models could be integrated into the framework thanks to the versatility of the proposed approach, thus allowing many researchers to achieve continuously improved experimental design.
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Affiliation(s)
- Anna Letizia Allegra Mascaro
- Neuroscience Institute, National Research Council, Pisa, Italy.,European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy
| | - Egidio Falotico
- Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Spase Petkoski
- Aix-Marseille Université, Inserm, INS UMR_S 1106, Marseille, France
| | - Maria Pasquini
- Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Lorenzo Vannucci
- Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Núria Tort-Colet
- Paris-Saclay University, Institute of Neuroscience, CNRS, Gif-sur-Yvette, France
| | - Emilia Conti
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy.,Department of Physics and Astronomy, University of Florence, Florence, Italy
| | - Francesco Resta
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy.,Department of Physics and Astronomy, University of Florence, Florence, Italy
| | | | | | | | - Emanuele Formento
- Bertarelli Foundation Chair in Translational NeuroEngineering, Institute of Bioengineering, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Emmanouil Angelidis
- Fortiss GmbH, Munich, Germany.,Chair of Robotics, Artificial Intelligence and Embedded Systems, Department of Informatics, Technical University of Munich, Munich, Germany
| | | | | | - Matteo Caleo
- Neuroscience Institute, National Research Council, Pisa, Italy.,Department of Biomedical Sciences, University of Padua, Padua, Italy
| | - Alain Destexhe
- Paris-Saclay University, Institute of Neuroscience, CNRS, Gif-sur-Yvette, France
| | - Auke Ijspeert
- Biorobotics Laboratory, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Silvestro Micera
- Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy.,Bertarelli Foundation Chair in Translational NeuroEngineering, Institute of Bioengineering, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Cecilia Laschi
- Department of Excellence in Robotics & AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Viktor Jirsa
- Aix-Marseille Université, Inserm, INS UMR_S 1106, Marseille, France
| | - Marc-Oliver Gewaltig
- Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Francesco S Pavone
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, Italy.,Department of Physics and Astronomy, University of Florence, Florence, Italy
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10
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Lobov SA, Mikhaylov AN, Shamshin M, Makarov VA, Kazantsev VB. Spatial Properties of STDP in a Self-Learning Spiking Neural Network Enable Controlling a Mobile Robot. Front Neurosci 2020; 14:88. [PMID: 32174804 PMCID: PMC7054464 DOI: 10.3389/fnins.2020.00088] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Accepted: 01/22/2020] [Indexed: 11/13/2022] Open
Abstract
Development of spiking neural networks (SNNs) controlling mobile robots is one of the modern challenges in computational neuroscience and artificial intelligence. Such networks, being replicas of biological ones, are expected to have a higher computational potential than traditional artificial neural networks (ANNs). The critical problem is in the design of robust learning algorithms aimed at building a “living computer” based on SNNs. Here, we propose a simple SNN equipped with a Hebbian rule in the form of spike-timing-dependent plasticity (STDP). The SNN implements associative learning by exploiting the spatial properties of STDP. We show that a LEGO robot controlled by the SNN can exhibit classical and operant conditioning. Competition of spike-conducting pathways in the SNN plays a fundamental role in establishing associations of neural connections. It replaces the irrelevant associations by new ones in response to a change in stimuli. Thus, the robot gets the ability to relearn when the environment changes. The proposed SNN and the stimulation protocol can be further enhanced and tested in developing neuronal cultures, and also admit the use of memristive devices for hardware implementation.
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Affiliation(s)
- Sergey A Lobov
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.,Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia
| | - Alexey N Mikhaylov
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Maxim Shamshin
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Valeri A Makarov
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.,Instituto de Matemática Interdisciplinar, Facultad de Ciencias Matemáticas, Universidad Complutense de Madrid, Madrid, Spain
| | - Victor B Kazantsev
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.,Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia
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11
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Keren H, Partzsch J, Marom S, Mayr CG. A Biohybrid Setup for Coupling Biological and Neuromorphic Neural Networks. Front Neurosci 2019; 13:432. [PMID: 31133779 PMCID: PMC6517490 DOI: 10.3389/fnins.2019.00432] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Accepted: 04/15/2019] [Indexed: 12/30/2022] Open
Abstract
Developing technologies for coupling neural activity and artificial neural components, is key for advancing neural interfaces and neuroprosthetics. We present a biohybrid experimental setting, where the activity of a biological neural network is coupled to a biomimetic hardware network. The implementation of the hardware network (denoted NeuroSoC) exhibits complex dynamics with a multiplicity of time-scales, emulating 2880 neurons and 12.7 M synapses, designed on a VLSI chip. This network is coupled to a neural network in vitro, where the activities of both the biological and the hardware networks can be recorded, processed, and integrated bidirectionally in real-time. This experimental setup enables an adjustable and well-monitored coupling, while providing access to key functional features of neural networks. We demonstrate the feasibility to functionally couple the two networks and to implement control circuits to modify the biohybrid activity. Overall, we provide an experimental model for neuromorphic-neural interfaces, hopefully to advance the capability to interface with neural activity, and with its irregularities in pathology.
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Affiliation(s)
- Hanna Keren
- Department of Physiology, Biophysics and Systems Biology, Ruth and Bruce Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, Israel
- Network Biology Research Laboratory, Faculty of Electrical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
- Institute of Circuits and Systems, Faculty of Electrical and Computer Engineering, School of Engineering Sciences, Dresden University of Technology, Dresden, Germany
| | - Johannes Partzsch
- Institute of Circuits and Systems, Faculty of Electrical and Computer Engineering, School of Engineering Sciences, Dresden University of Technology, Dresden, Germany
| | - Shimon Marom
- Department of Physiology, Biophysics and Systems Biology, Ruth and Bruce Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, Israel
- Network Biology Research Laboratory, Faculty of Electrical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - Christian G Mayr
- Institute of Circuits and Systems, Faculty of Electrical and Computer Engineering, School of Engineering Sciences, Dresden University of Technology, Dresden, Germany
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12
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Closed-Loop Systems and In Vitro Neuronal Cultures: Overview and Applications. ADVANCES IN NEUROBIOLOGY 2019; 22:351-387. [DOI: 10.1007/978-3-030-11135-9_15] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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13
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14
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Ando N, Kanzaki R. Using insects to drive mobile robots - hybrid robots bridge the gap between biological and artificial systems. ARTHROPOD STRUCTURE & DEVELOPMENT 2017; 46:723-735. [PMID: 28254451 DOI: 10.1016/j.asd.2017.02.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Revised: 02/21/2017] [Accepted: 02/21/2017] [Indexed: 06/06/2023]
Abstract
The use of mobile robots is an effective method of validating sensory-motor models of animals in a real environment. The well-identified insect sensory-motor systems have been the major targets for modeling. Furthermore, mobile robots implemented with such insect models attract engineers who aim to avail advantages from organisms. However, directly comparing the robots with real insects is still difficult, even if we successfully model the biological systems, because of the physical differences between them. We developed a hybrid robot to bridge the gap. This hybrid robot is an insect-controlled robot, in which a tethered male silkmoth (Bombyx mori) drives the robot in order to localize an odor source. This robot has the following three advantages: 1) from a biomimetic perspective, the robot enables us to evaluate the potential performance of future insect-mimetic robots; 2) from a biological perspective, the robot enables us to manipulate the closed-loop of an onboard insect for further understanding of its sensory-motor system; and 3) the robot enables comparison with insect models as a reference biological system. In this paper, we review the recent works regarding insect-controlled robots and discuss the significance for both engineering and biology.
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Affiliation(s)
- Noriyasu Ando
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan.
| | - Ryohei Kanzaki
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan
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15
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Chiolerio A, Chiappalone M, Ariano P, Bocchini S. Coupling Resistive Switching Devices with Neurons: State of the Art and Perspectives. Front Neurosci 2017; 11:70. [PMID: 28261048 PMCID: PMC5309244 DOI: 10.3389/fnins.2017.00070] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Accepted: 01/31/2017] [Indexed: 11/13/2022] Open
Abstract
Here we provide the state-of-the-art of bioelectronic interfacing between biological neuronal systems and artificial components, focusing the attention on the potentiality offered by intrinsically neuromorphic synthetic devices based on Resistive Switching (RS). Neuromorphic engineering is outside the scopes of this Perspective. Instead, our focus is on those materials and devices featuring genuine physical effects that could be sought as non-linearity, plasticity, excitation, and extinction which could be directly and more naturally coupled with living biological systems. In view of important applications, such as prosthetics and future life augmentation, a cybernetic parallelism is traced, between biological and artificial systems. We will discuss how such intrinsic features could reduce the complexity of conditioning networks for a more natural direct connection between biological and synthetic worlds. Putting together living systems with RS devices could represent a feasible though innovative perspective for the future of bionics.
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Affiliation(s)
- Alessandro Chiolerio
- Center for Sustainable Future Technologies, Istituto Italiano di Tecnologia Torino, Italy
| | - Michela Chiappalone
- Neuroscience and Brain Technologies Department, Istituto Italiano di Tecnologia Genova, Italy
| | - Paolo Ariano
- Center for Sustainable Future Technologies, Istituto Italiano di Tecnologia Torino, Italy
| | - Sergio Bocchini
- Center for Sustainable Future Technologies, Istituto Italiano di Tecnologia Torino, Italy
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16
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Panzeri S, Safaai H, De Feo V, Vato A. Implications of the Dependence of Neuronal Activity on Neural Network States for the Design of Brain-Machine Interfaces. Front Neurosci 2016; 10:165. [PMID: 27147955 PMCID: PMC4837323 DOI: 10.3389/fnins.2016.00165] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2016] [Accepted: 04/01/2016] [Indexed: 01/07/2023] Open
Abstract
Brain-machine interfaces (BMIs) can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stimulating electrodes, because trial-to-trial variability of neural activity partly arises from intrinsic factors (collectively known as the network state) that include ongoing spontaneous activity and neuromodulation, and so is shared among neurons. Here we review recent progress in characterizing the state dependence of neural responses, and in particular of how neural responses depend on endogenous slow fluctuations of network excitability. We then elaborate on how this knowledge may be used to increase the amount of information that BMIs exchange with brain. Knowledge of network state can be used to fine-tune the stimulation pattern that should reliably elicit a target neural response used to encode information in the brain, and to discount part of the trial-by-trial variability of neural responses, so that they can be decoded more accurately.
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Affiliation(s)
- Stefano Panzeri
- Neural Computation Laboratory, Istituto Italiano di Tecnologia Rovereto, Italy
| | - Houman Safaai
- Neural Computation Laboratory, Istituto Italiano di Tecnologia Rovereto, Italy
| | - Vito De Feo
- Neural Computation Laboratory, Istituto Italiano di Tecnologia Rovereto, Italy
| | - Alessandro Vato
- Neural Computation Laboratory, Istituto Italiano di Tecnologia Rovereto, Italy
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17
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Greenwald E, Masters MR, Thakor NV. Erratum to: Implantable neurotechnologies: bidirectional neural interfaces--applications and VLSI circuit implementations. Med Biol Eng Comput 2016; 54:19-22. [PMID: 26924780 PMCID: PMC4955539 DOI: 10.1007/s11517-016-1452-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Elliot Greenwald
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
| | - Matthew R Masters
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Nitish V Thakor
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Singapore Institute for Neurotechnology (SINAPSE), National University of Singapore, Singapore, Singapore.
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18
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Greenwald E, Masters MR, Thakor NV. Implantable neurotechnologies: bidirectional neural interfaces--applications and VLSI circuit implementations. Med Biol Eng Comput 2016; 54:1-17. [PMID: 26753776 PMCID: PMC4839984 DOI: 10.1007/s11517-015-1429-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2015] [Accepted: 12/10/2015] [Indexed: 12/20/2022]
Abstract
A bidirectional neural interface is a device that transfers information into and out of the nervous system. This class of devices has potential to improve treatment and therapy in several patient populations. Progress in very large-scale integration has advanced the design of complex integrated circuits. System-on-chip devices are capable of recording neural electrical activity and altering natural activity with electrical stimulation. Often, these devices include wireless powering and telemetry functions. This review presents the state of the art of bidirectional circuits as applied to neuroprosthetic, neurorepair, and neurotherapeutic systems.
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Affiliation(s)
- Elliot Greenwald
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
| | - Matthew R Masters
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Nitish V Thakor
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Singapore Institute for Neurotechnology (SINAPSE), National University of Singapore, Singapore, Singapore.
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19
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Tessadori J, Chiappalone M. Closed-loop neuro-robotic experiments to test computational properties of neuronal networks. J Vis Exp 2015. [PMID: 25867052 PMCID: PMC4401171 DOI: 10.3791/52341] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
Abstract
Information coding in the Central Nervous System (CNS) remains unexplored. There is mounting evidence that, even at a very low level, the representation of a given stimulus might be dependent on context and history. If this is actually the case, bi-directional interactions between the brain (or if need be a reduced model of it) and sensory-motor system can shed a light on how encoding and decoding of information is performed. Here an experimental system is introduced and described in which the activity of a neuronal element (i.e., a network of neurons extracted from embryonic mammalian hippocampi) is given context and used to control the movement of an artificial agent, while environmental information is fed back to the culture as a sequence of electrical stimuli. This architecture allows a quick selection of diverse encoding, decoding, and learning algorithms to test different hypotheses on the computational properties of neuronal networks.
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Affiliation(s)
- Jacopo Tessadori
- Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia
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20
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Montebelli A, Lowe R, Ziemke T. Toward metabolic robotics: insights from modeling embodied cognition in a biomechatronic symbiont. ARTIFICIAL LIFE 2013; 19:299-315. [PMID: 23834595 DOI: 10.1162/artl_a_00114] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
We present a novel example of a biomechatronic hybrid system. The living component of the system, embedded within microbial fuel cells, relies on the availability of food and water in order to produce electrical energy. The latter is essential to the operations of the mechatronic component, responsible for finding and collecting food and water, and for the execution of work. In simulation, we explore the behavioral and cognitive consequences of this symbiotic relation. In particular we highlight the importance of the integration of sensorimotor and metabolic signals within an evolutionary perspective, in order to create sound cognitive living technology.
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21
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Newman JP, Zeller-Townson R, Fong MF, Arcot Desai S, Gross RE, Potter SM. Closed-Loop, Multichannel Experimentation Using the Open-Source NeuroRighter Electrophysiology Platform. Front Neural Circuits 2013; 6:98. [PMID: 23346047 PMCID: PMC3548271 DOI: 10.3389/fncir.2012.00098] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2012] [Accepted: 11/18/2012] [Indexed: 11/25/2022] Open
Abstract
Single neuron feedback control techniques, such as voltage clamp and dynamic clamp, have enabled numerous advances in our understanding of ion channels, electrochemical signaling, and neural dynamics. Although commercially available multichannel recording and stimulation systems are commonly used for studying neural processing at the network level, they provide little native support for real-time feedback. We developed the open-source NeuroRighter multichannel electrophysiology hardware and software platform for closed-loop multichannel control with a focus on accessibility and low cost. NeuroRighter allows 64 channels of stimulation and recording for around US $10,000, along with the ability to integrate with other software and hardware. Here, we present substantial enhancements to the NeuroRighter platform, including a redesigned desktop application, a new stimulation subsystem allowing arbitrary stimulation patterns, low-latency data servers for accessing data streams, and a new application programming interface (API) for creating closed-loop protocols that can be inserted into NeuroRighter as plugin programs. This greatly simplifies the design of sophisticated real-time experiments without sacrificing the power and speed of a compiled programming language. Here we present a detailed description of NeuroRighter as a stand-alone application, its plugin API, and an extensive set of case studies that highlight the system’s abilities for conducting closed-loop, multichannel interfacing experiments.
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Affiliation(s)
- Jonathan P Newman
- Laboratory for Neuroengineering, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine Atlanta, GA, USA
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22
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De Rossi D, Pieroni M. Grand Challenges in Bionics. Front Bioeng Biotechnol 2013; 1:3. [PMID: 25023011 PMCID: PMC4090898 DOI: 10.3389/fbioe.2013.00003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2013] [Accepted: 06/05/2013] [Indexed: 12/03/2022] Open
Affiliation(s)
- Danilo De Rossi
- Research Center "E. Piaggio", University of Pisa Pisa, Italy
| | - Michael Pieroni
- Research Center "E. Piaggio", University of Pisa Pisa, Italy
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23
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Vato A, Semprini M, Maggiolini E, Szymanski FD, Fadiga L, Panzeri S, Mussa-Ivaldi FA. Shaping the dynamics of a bidirectional neural interface. PLoS Comput Biol 2012; 8:e1002578. [PMID: 22829754 PMCID: PMC3400597 DOI: 10.1371/journal.pcbi.1002578] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2012] [Accepted: 05/09/2012] [Indexed: 11/18/2022] Open
Abstract
Progress in decoding neural signals has enabled the development of interfaces that translate cortical brain activities into commands for operating robotic arms and other devices. The electrical stimulation of sensory areas provides a means to create artificial sensory information about the state of a device. Taken together, neural activity recording and microstimulation techniques allow us to embed a portion of the central nervous system within a closed-loop system, whose behavior emerges from the combined dynamical properties of its neural and artificial components. In this study we asked if it is possible to concurrently regulate this bidirectional brain-machine interaction so as to shape a desired dynamical behavior of the combined system. To this end, we followed a well-known biological pathway. In vertebrates, the communications between brain and limb mechanics are mediated by the spinal cord, which combines brain instructions with sensory information and organizes coordinated patterns of muscle forces driving the limbs along dynamically stable trajectories. We report the creation and testing of the first neural interface that emulates this sensory-motor interaction. The interface organizes a bidirectional communication between sensory and motor areas of the brain of anaesthetized rats and an external dynamical object with programmable properties. The system includes (a) a motor interface decoding signals from a motor cortical area, and (b) a sensory interface encoding the state of the external object into electrical stimuli to a somatosensory area. The interactions between brain activities and the state of the external object generate a family of trajectories converging upon a selected equilibrium point from arbitrary starting locations. Thus, the bidirectional interface establishes the possibility to specify not only a particular movement trajectory but an entire family of motions, which includes the prescribed reactions to unexpected perturbations.
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Affiliation(s)
- Alessandro Vato
- Department of Robotics, Brain and Cognitive Sciences, Istituto Italiano di Tecnologia, Genova, Italy
| | - Marianna Semprini
- Department of Robotics, Brain and Cognitive Sciences, Istituto Italiano di Tecnologia, Genova, Italy
| | - Emma Maggiolini
- Department of Robotics, Brain and Cognitive Sciences, Istituto Italiano di Tecnologia, Genova, Italy
| | - Francois D. Szymanski
- Department of Robotics, Brain and Cognitive Sciences, Istituto Italiano di Tecnologia, Genova, Italy
| | - Luciano Fadiga
- Department of Robotics, Brain and Cognitive Sciences, Istituto Italiano di Tecnologia, Genova, Italy
- Department of Human Physiology, University of Ferrara, Ferrara, Italy
| | - Stefano Panzeri
- Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom
| | - Ferdinando A. Mussa-Ivaldi
- Department of Physiology, Northwestern University, Chicago, Illinois, United States of America
- Department of Biomedical Engineering, Northwestern University, Evanston, Illinois, United States of America
- Sensory Motor Performance Program, Rehabilitation Institute of Chicago, Chicago, Illinois, United States of America
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Embodied artificial evolution: Artificial evolutionary systems in the 21st Century. EVOLUTIONARY INTELLIGENCE 2012; 5:261-272. [PMID: 23144668 PMCID: PMC3490067 DOI: 10.1007/s12065-012-0071-x] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2011] [Revised: 02/17/2012] [Accepted: 03/22/2012] [Indexed: 11/01/2022]
Abstract
Evolution is one of the major omnipresent powers in the universe that has been studied for about two centuries. Recent scientific and technical developments make it possible to make the transition from passively understanding to actively using evolutionary processes. Today this is possible in Evolutionary Computing, where human experimenters can design and manipulate all components of evolutionary processes in digital spaces. We argue that in the near future it will be possible to implement artificial evolutionary processes outside such imaginary spaces and make them physically embodied. In other words, we envision the "Evolution of Things", rather than just the evolution of digital objects, leading to a new field of Embodied Artificial Evolution (EAE). The main objective of this paper is to present a unifying vision in order to aid the development of this high potential research area. To this end, we introduce the notion of EAE, discuss a few examples and applications, and elaborate on the expected benefits as well as the grand challenges this developing field will have to address.
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25
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Rolston JD, Gross RE, Potter SM. Closed-loop, open-source electrophysiology. Front Neurosci 2010; 4. [PMID: 20859448 PMCID: PMC2940414 DOI: 10.3389/fnins.2010.00031] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2010] [Accepted: 05/11/2010] [Indexed: 11/25/2022] Open
Abstract
Multiple extracellular microelectrodes (multi-electrode arrays, or MEAs) effectively record rapidly varying neural signals, and can also be used for electrical stimulation. Multi-electrode recording can serve as artificial output (efferents) from a neural system, while complex spatially and temporally targeted stimulation can serve as artificial input (afferents) to the neuronal network. Multi-unit or local field potential (LFP) recordings can not only be used to control real world artifacts, such as prostheses, computers or robots, but can also trigger or alter subsequent stimulation. Real-time feedback stimulation may serve to modulate or normalize aberrant neural activity, to induce plasticity, or to serve as artificial sensory input. Despite promising closed-loop applications, commercial electrophysiology systems do not yet take advantage of the bidirectional capabilities of multi-electrodes, especially for use in freely moving animals. We addressed this lack of tools for closing the loop with NeuroRighter, an open-source system including recording hardware, stimulation hardware, and control software with a graphical user interface. The integrated system is capable of multi-electrode recording and simultaneous patterned microstimulation (triggered by recordings) with minimal stimulation artifact. The potential applications of closed-loop systems as research tools and clinical treatments are broad; we provide one example where epileptic activity recorded by a multi-electrode probe is used to trigger targeted stimulation, via that probe, to freely moving rodents.
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Affiliation(s)
- John D Rolston
- Laboratory for Neuroengineering, Georgia Institute of Technology and Emory University School of Medicine Atlanta, GA, USA
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26
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Connecting neurons to a mobile robot: an in vitro bidirectional neural interface. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2010:12725. [PMID: 18350128 PMCID: PMC2266971 DOI: 10.1155/2007/12725] [Citation(s) in RCA: 68] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2006] [Revised: 04/04/2007] [Accepted: 06/18/2007] [Indexed: 11/17/2022]
Abstract
One of the key properties of intelligent behaviors is the capability to learn and adapt to changing environmental conditions. These features are the result of the continuous and intense interaction of the brain with the external world, mediated by the body. For this reason “embodiment” represents an innovative and very suitable experimental paradigm when studying the neural processes underlying learning new behaviors and adapting to unpredicted situations. To this purpose, we developed a novel bidirectional neural interface. We interconnected in vitro neurons, extracted from rat embryos and plated on a microelectrode array (MEA), to external devices, thus allowing real-time closed-loop interaction. The novelty of this experimental approach entails the necessity to explore different computational schemes and experimental hypotheses. In this paper, we present an open, scalable architecture, which allows fast prototyping of different modules and where coding and decoding schemes and different experimental configurations can be tested. This hybrid system can be used for studying the computational properties and information coding in biological neuronal networks with far-reaching implications for the future development of advanced neuroprostheses.
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27
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Clark KB. Bose–Einstein condensates form in heuristics learned by ciliates deciding to signal ‘social’ commitments. Biosystems 2010; 99:167-78. [DOI: 10.1016/j.biosystems.2009.10.010] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2009] [Revised: 10/22/2009] [Accepted: 10/27/2009] [Indexed: 10/20/2022]
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Kelso JAS, de Guzman GC, Reveley C, Tognoli E. Virtual Partner Interaction (VPI): exploring novel behaviors via coordination dynamics. PLoS One 2009; 4:e5749. [PMID: 19492044 PMCID: PMC2685001 DOI: 10.1371/journal.pone.0005749] [Citation(s) in RCA: 80] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2009] [Accepted: 04/27/2009] [Indexed: 11/19/2022] Open
Abstract
Inspired by the dynamic clamp of cellular neuroscience, this paper introduces VPI -- Virtual Partner Interaction -- a coupled dynamical system for studying real time interaction between a human and a machine. In this proof of concept study, human subjects coordinate hand movements with a virtual partner, an avatar of a hand whose movements are driven by a computerized version of the Haken-Kelso-Bunz (HKB) equations that have been shown to govern basic forms of human coordination. As a surrogate system for human social coordination, VPI allows one to examine regions of the parameter space not typically explored during live interactions. A number of novel behaviors never previously observed are uncovered and accounted for. Having its basis in an empirically derived theory of human coordination, VPI offers a principled approach to human-machine interaction and opens up new ways to understand how humans interact with human-like machines including identification of underlying neural mechanisms.
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Affiliation(s)
- J A Scott Kelso
- Human Brain and Behavior Laboratory, Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL, USA.
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29
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Pizzi R, Rossetti D, Cino G, Marino D, A.L.Vescovi, Baer W. A cultured human neural network operates a robotic actuator. Biosystems 2009; 95:137-44. [DOI: 10.1016/j.biosystems.2008.09.006] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2007] [Revised: 09/21/2008] [Accepted: 09/24/2008] [Indexed: 11/25/2022]
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30
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Di Pino G, Seidl T, Benvenuto A, Sergi F, Campolo D, Accoto D, Maria Rossini P, Guglielmelli E. Interfacing insect brain for space applications. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2009; 86:39-47. [PMID: 19607989 DOI: 10.1016/s0074-7742(09)86003-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Insects exhibit remarkable navigation capabilities that current control architectures are still far from successfully mimic and reproduce. In this chapter, we present the results of a study on conceptualizing insect/machine hybrid controllers for improving autonomy of exploratory vehicles. First, the different principally possible levels of interfacing between insect and machine are examined followed by a review of current approaches towards hybridity and enabling technologies. Based on the insights of this activity, we propose a double hybrid control architecture which hinges around the concept of "insect-in-a-cockpit." It integrates both biological/artificial (insect/robot) modules and deliberative/reactive behavior. The basic assumption is that "low-level" tasks are managed by the robot, while the "insect intelligence" is exploited whenever high-level problem solving and decision making is required. Both neural and natural interfacing have been considered to achieve robustness and redundancy of exchanged information.
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Affiliation(s)
- Giovanni Di Pino
- Biomedical Robotics and Biomicrosystems Laboratory, Università Campus Biomedico di Roma, Roma, Italy
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31
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Serruya MD, Kahana MJ. Techniques and devices to restore cognition. Behav Brain Res 2008; 192:149-65. [PMID: 18539345 DOI: 10.1016/j.bbr.2008.04.007] [Citation(s) in RCA: 31] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2007] [Revised: 03/03/2008] [Accepted: 04/09/2008] [Indexed: 12/18/2022]
Abstract
Executive planning, the ability to direct and sustain attention, language and several types of memory may be compromised by conditions such as stroke, traumatic brain injury, cancer, autism, cerebral palsy and Alzheimer's disease. No medical devices are currently available to help restore these cognitive functions. Recent findings about the neurophysiology of these conditions in humans coupled with progress in engineering devices to treat refractory neurological conditions imply that the time has arrived to consider the design and evaluation of a new class of devices. Like their neuromotor counterparts, neurocognitive prostheses might sense or modulate neural function in a non-invasive manner or by means of implanted electrodes. In order to paint a vision for future device development, it is essential to first review what can be achieved using behavioral and external modulatory techniques. While non-invasive approaches might strengthen a patient's remaining intact cognitive abilities, neurocognitive prosthetics comprised of direct brain-computer interfaces could in theory physically reconstitute and augment the substrate of cognition itself.
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Affiliation(s)
- Mijail Demian Serruya
- Department of Neurology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Gates 3 HUP, Philadelphia, PA 19104-4283, United States.
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Chao ZC, Bakkum DJ, Potter SM. Shaping embodied neural networks for adaptive goal-directed behavior. PLoS Comput Biol 2008; 4:e1000042. [PMID: 18369432 PMCID: PMC2265558 DOI: 10.1371/journal.pcbi.1000042] [Citation(s) in RCA: 43] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2007] [Accepted: 02/20/2008] [Indexed: 11/18/2022] Open
Abstract
The acts of learning and memory are thought to emerge from the modifications of synaptic connections between neurons, as guided by sensory feedback during behavior. However, much is unknown about how such synaptic processes can sculpt and are sculpted by neuronal population dynamics and an interaction with the environment. Here, we embodied a simulated network, inspired by dissociated cortical neuronal cultures, with an artificial animal (an animat) through a sensory-motor loop consisting of structured stimuli, detailed activity metrics incorporating spatial information, and an adaptive training algorithm that takes advantage of spike timing dependent plasticity. By using our design, we demonstrated that the network was capable of learning associations between multiple sensory inputs and motor outputs, and the animat was able to adapt to a new sensory mapping to restore its goal behavior: move toward and stay within a user-defined area. We further showed that successful learning required proper selections of stimuli to encode sensory inputs and a variety of training stimuli with adaptive selection contingent on the animat's behavior. We also found that an individual network had the flexibility to achieve different multi-task goals, and the same goal behavior could be exhibited with different sets of network synaptic strengths. While lacking the characteristic layered structure of in vivo cortical tissue, the biologically inspired simulated networks could tune their activity in behaviorally relevant manners, demonstrating that leaky integrate-and-fire neural networks have an innate ability to process information. This closed-loop hybrid system is a useful tool to study the network properties intermediating synaptic plasticity and behavioral adaptation. The training algorithm provides a stepping stone towards designing future control systems, whether with artificial neural networks or biological animats themselves. The ability of a brain to learn has been studied at various levels. However, a large gap exists between behavioral studies of learning and memory and studies of cellular plasticity. In particular, much remains unknown about how cellular plasticity scales to affect network population dynamics. In previous studies, we have addressed this by growing mammalian brain cells in culture and creating a long-term, two-way interface between a cultured network and a robot or an artificial animal. Behavior and learning could now be observed in concert with the detailed and long-term electrophysiology. In this work, we used modeling/simulation of living cortical cultures to investigate the network's capability to learn goal-directed behavior. A biologically inspired simulated network was used to determine an effective closed-loop training algorithm, and the system successfully exhibited multi-task goal-directed adaptive behavior. The results suggest that even though lacking the characteristic layered structure of a brain, the network still could be functionally shaped and showed meaningful behavior. Knowledge gained from working with such closed-loop systems could influence the design of future artificial neural networks, more effective neuroprosthetics, and even the use of living networks themselves as a biologically based control system.
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Affiliation(s)
- Zenas C. Chao
- Laboratory for Neuroengineering, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Douglas J. Bakkum
- Laboratory for Neuroengineering, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, Georgia, United States of America
| | - Steve M. Potter
- Laboratory for Neuroengineering, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, Georgia, United States of America
- * E-mail:
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Whittington RH, Kovacs GTA. A Discrete-Time Control Algorithm Applied to Closed-Loop Pacing of HL-1 Cardiomyocytes. IEEE Trans Biomed Eng 2008; 55:21-30. [DOI: 10.1109/tbme.2007.910641] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Dennis RG, Dow DE. Excitability of skeletal muscle during development, denervation, and tissue culture. ACTA ACUST UNITED AC 2007; 13:2395-404. [PMID: 17867927 DOI: 10.1089/ten.2006.0367] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
A quantitative understanding of the bulk excitability of skeletal muscle tissues is important for the design of muscle tissue bioreactor systems, implantable muscle stimulators, and other systems where electrical pulses are employed to elicit contractions in muscle tissue both in vitro and in vivo. The purpose of the present study is to systematically compare the excitability of mammalian (rat) skeletal muscle under a range of conditions (including neonatal development, denervation, and chronic in vivo stimulation of denervated muscle) and of self-organized muscle tissue constructs engineered in vitro from both primary cells and cell lines. Excitability is represented by rheobase (R(50), units = V/mm) and chronaxie (C(50), units = microseconds) values, with lower values for each indicating greater excitability. Adult skeletal muscle is the most excitable (R(50) ~ 0.29, C(50) ~ 100); chronically denervated whole muscles (R(50) ~ 2.54, C(50) ~ 690) and muscle engineered in vitro from cell lines (C2C12 + 10T1/2) (R(50) ~ 1.93, C(50) ~ 416) have exceptionally low excitability; muscle engineered in vitro from primary myocytes (R(50) ~ 0.99, C(50) ~ 496) has excitability similar to that of day 14 neonatal rat muscle (R(50) ~ 0.65, C(50) ~ 435); stimulated-denervated muscles retain excellent excitability when chronically electrically stimulated (R(50) ~ 0.40, C(50) ~ 100); and neonatal rat muscle excitability improves during the first 6 weeks of development, steadily approaching that of adult muscle.
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Affiliation(s)
- Robert G Dennis
- Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7575, USA.
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Bakkum DJ, Gamblen PM, Ben-Ary G, Chao ZC, Potter SM. MEART: The Semi-Living Artist. Front Neurorobot 2007; 1:5. [PMID: 18958276 PMCID: PMC2533587 DOI: 10.3389/neuro.12.005.2007] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2007] [Accepted: 10/15/2007] [Indexed: 11/19/2022] Open
Abstract
Here, we and others describe an unusual neurorobotic project, a merging of art and science called MEART, the semi-living artist. We built a pneumatically actuated robotic arm to create drawings, as controlled by a living network of neurons from rat cortex grown on a multi-electrode array (MEA). Such embodied cultured networks formed a real-time closed-loop system which could now behave and receive electrical stimulation as feedback on its behavior. We used MEART and simulated embodiments, or animats, to study the network mechanisms that produce adaptive, goal-directed behavior. This approach to neural interfacing will help instruct the design of other hybrid neural-robotic systems we call hybrots. The interfacing technologies and algorithms developed have potential applications in responsive deep brain stimulation systems and for motor prosthetics using sensory components. In a broader context, MEART educates the public about neuroscience, neural interfaces, and robotics. It has paved the way for critical discussions on the future of bio-art and of biotechnology.
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Affiliation(s)
- Douglas J. Bakkum
- Laboratory for Neuroengineering, School of Mechanical Engineering, Georgia Institute of TechnologyUSA
| | - Philip M. Gamblen
- SymbioticA, School of Anatomy and Human Biology, University of Western AustraliaAustralia
| | - Guy Ben-Ary
- SymbioticA, School of Anatomy and Human Biology, University of Western AustraliaAustralia
| | - Zenas C. Chao
- Laboratory for Neuroengineering, School of Mechanical Engineering, Georgia Institute of TechnologyUSA
| | - Steve M. Potter
- Laboratory for Neuroengineering, School of Mechanical Engineering, Georgia Institute of TechnologyUSA
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Warwick K, Gasson MN, Spiers AJ. Therapeutic potential of computer to cerebral cortex implantable devices. ACTA NEUROCHIRURGICA. SUPPLEMENT 2007; 97:529-35. [PMID: 17691344 DOI: 10.1007/978-3-211-33081-4_61] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
In this article, an overview of some of the latest developments in the field of cerebral cortex to computer interfacing (CCCI) is given. This is posed in the more general context of Brain-Computer Interfaces in order to assess advantages and disadvantages. The emphasis is clearly placed on practical studies that have been undertaken and reported on, as opposed to those speculated, simulated or proposed as future projects. Related areas are discussed briefly only in the context of their contribution to the studies being undertaken. The area of focus is notably the use of invasive implant technology, where a connection is made directly with the cerebral cortex and/or nervous system. Tests and experimentation which do not involve human subjects are invariably carried out a priori to indicate the eventual possibilities before human subjects are themselves involved. Some of the more pertinent animal studies from this area are discussed. The paper goes on to describe human experimentation, in which neural implants have linked the human nervous system bidirectionally with technology and the internet. A view is taken as to the prospects for the future for CCCI, in terms of its broad therapeutic role.
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Affiliation(s)
- K Warwick
- Department of Cybernetics, University of Reading, Reading, UK.
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Bashashati A, Fatourechi M, Ward RK, Birch GE. A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. J Neural Eng 2007; 4:R32-57. [PMID: 17409474 DOI: 10.1088/1741-2560/4/2/r03] [Citation(s) in RCA: 279] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Brain-computer interfaces (BCIs) aim at providing a non-muscular channel for sending commands to the external world using the electroencephalographic activity or other electrophysiological measures of the brain function. An essential factor in the successful operation of BCI systems is the methods used to process the brain signals. In the BCI literature, however, there is no comprehensive review of the signal processing techniques used. This work presents the first such comprehensive survey of all BCI designs using electrical signal recordings published prior to January 2006. Detailed results from this survey are presented and discussed. The following key research questions are addressed: (1) what are the key signal processing components of a BCI, (2) what signal processing algorithms have been used in BCIs and (3) which signal processing techniques have received more attention?
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Affiliation(s)
- Ali Bashashati
- Department of Electrical and Computer Engineering, The University of British Columbia, 2356 Main Mall, Vancouver, V6T 1Z4, Canada.
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Pizzi R, Cino G, Gelain F, Rossetti D, Vescovi A. Learning in human neural networks on microelectrode arrays. Biosystems 2007; 88:1-15. [PMID: 16843590 DOI: 10.1016/j.biosystems.2006.03.012] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2005] [Revised: 02/10/2006] [Accepted: 03/03/2006] [Indexed: 11/23/2022]
Abstract
This paper describes experiments involving the growth of human neural networks of stem cells on a MEA (microelectrode array) support. The microelectrode arrays (MEAs) are constituted by a glass support in which a set of tungsten electrodes are inserted. The artificial neural network (ANN) paradigm was used by stimulating the neurons in parallel with digital patterns distributed on eight channels, then by analyzing a parallel multichannel output. In particular, the microelectrodes were connected following two different architectures, one inspired by the Kohonen's SOM, the other by the Hopfield network. The output signals have been analyzed in order to evaluate the possibility of organized reactions by the natural neurons.f The results show that the network of human neurons reacts selectively to the subministered digital signals, i.e., it produces similar output signals referred to identical or similar patterns, and clearly differentiates the outputs coming from different stimulations. Analyses performed with a special artificial neural network called ITSOM show the possibility to codify the neural responses to different patterns, thus to interpret the signals coming from the network of biological neurons, assigning a code to each output. It is straightforward to verify that identical codes are generated by the neural reactions to similar patterns. Further experiments are to be designed that improve the hybrid neural networks' capabilities and to test the possibility of utilizing the organized answers of the neurons in several ways.
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Affiliation(s)
- R Pizzi
- Department of Information Technologies, University of Milan, via Bramante 65, 26013 Crema (CR), Italy.
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Cozzi L, D'Angelo P, Sanguineti V. Encoding of time-varying stimuli in populations of cultured neurons. BIOLOGICAL CYBERNETICS 2006; 94:335-49. [PMID: 16479398 DOI: 10.1007/s00422-006-0051-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2005] [Accepted: 12/21/2005] [Indexed: 05/06/2023]
Abstract
We wondered whether random populations of dissociated cultured cortical neurons, despite of their lack of structure and/or regional specialization, are capable of modulating their neural activity as the effect of a time-varying stimulation - a simulated 'sensory' afference. More specifically, we used localized low-frequency, non-periodic trains of stimuli to simulate sensory afferences, and asked how much information about the original trains of stimuli could be extracted from the neural activity recorded at the different sites. Furthermore, motivated by the results of studies performed both in vivo and in vitro on different preparations, which suggested that isolated spikes and bursts may play different roles in coding time-varying signals, we explored the amount of such 'sensory' information that could be associated to these different firing modes. Finally, we asked whether and how such 'sensory' information is transferred from the sites of stimulation (i.e., the 'sensory' areas), to the other regions of the neural populations. To do this we applied stimulus reconstruction techniques and information theoretic concepts that are typically used to investigate neural coding in sensory systems.Our main results are that (1) slow variations of the rate of stimulation are coded into isolated spikes and in the time of occurrence of bursts (but not in the bursts' temporal structure); (2) increasing the rate of stimulation has the effect of increasing the proportion of isolated spikes in the average evoked response and their importance in coding for the stimuli; and, (3) the ability to recover the time course of the pattern of stimulation is strongly related to the degree of functional connectivity between stimulation and recording sites. These observations parallel similar findings in intact nervous systems regarding the complementary roles of bursts and tonic spikes in encoding sensory information.Our results also have interesting implications in the field of neuro-robotic interfaces. In fact, the ability of populations of neurons to code information is a prerequisite for obtaining hybrid systems, in which neuronal populations are used to control external devices.
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Affiliation(s)
- Laura Cozzi
- Department of Informatics, Systems and Telematics, University of Genova, Via Opera Pia 13, 16145, Genova, Italy.
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Machine Experiments and Theoretical Modelling: from Cybernetic Methodology to Neuro-Robotics. Minds Mach (Dordr) 2005. [DOI: 10.1007/s11023-005-2924-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Cozzi L, D’Angelo P, Chiappalone M, Ide A, Novellino A, Martinoia S, Sanguineti V. Coding and decoding of information in a bi-directional neural interface. Neurocomputing 2005. [DOI: 10.1016/j.neucom.2004.10.075] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Martinoia S, Sanguineti V, Cozzi L, Berdondini L, van Pelt J, Tomas J, Le Masson G, Davide F. Towards an embodied in vitro electrophysiology: the NeuroBIT project. Neurocomputing 2004. [DOI: 10.1016/j.neucom.2004.01.167] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Kositsky M, Karniel A, Alford S, Fleming KM, Mussa-Ivaldi FA. Dynamical dimension of a hybrid neurorobotic system. IEEE Trans Neural Syst Rehabil Eng 2003; 11:155-9. [PMID: 12899261 DOI: 10.1109/tnsre.2003.814444] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The goal of this work is to understand how neural tissue can be programed to execute predetermined functions. We developed a research tool that includes the brainstem of a lamprey and a two-wheeled robot interconnected in a closed loop. We report here the development of a framework for studying the dynamics of the neural tissue based on the interaction of this tissue with the robot.
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Affiliation(s)
- Michael Kositsky
- Department of Physiology, Northwestern University Medical School, Chicago, IL 60611, USA.
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