1
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Liénard JF, Aubin L, Cos I, Girard B. Estimation of the transmission delays in the basal ganglia of the macaque monkey and subsequent predictions about oscillatory activity under dopamine depletion. Eur J Neurosci 2024; 59:1657-1680. [PMID: 38414108 DOI: 10.1111/ejn.16271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 12/31/2023] [Accepted: 01/21/2024] [Indexed: 02/29/2024]
Abstract
The timescales of the dynamics of a system depend on the combination of the timescales of its components and of its transmission delays between components. Here, we combine experimental stimulation data from 10 studies in macaque monkeys that reveal the timing of excitatory and inhibitory events in the basal ganglia circuit, to estimate its set of transmission delays. In doing so, we reveal possible inconsistencies in the existing data, calling for replications, and we propose two possible sets of transmission delays. We then integrate these delays in a model of the primate basal ganglia that does not rely on direct and indirect pathways' segregation and show that extrastriatal dopaminergic depletion in the external part of the globus pallidus and in the subthalamic nucleus is sufficient to generate β-band oscillations (in the high part, 20-35 Hz, of the band). More specifically, we show that D2 and D5 dopamine receptors in these nuclei play opposing roles in the emergence of these oscillations, thereby explaining how completely deactivating D5 receptors in the subthalamic nucleus can, paradoxically, cancel oscillations.
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Affiliation(s)
- Jean F Liénard
- Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique (ISIR), Paris, France
| | - Lise Aubin
- Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique (ISIR), Paris, France
| | - Ignasi Cos
- Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique (ISIR), Paris, France
- Facultat de Matemàtiques i Informàtica, Universitat de Barcelona, Barcelona, Spain
- Serra-Hunter Fellow Program, Barcelona, Spain
| | - Benoît Girard
- Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique (ISIR), Paris, France
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2
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Kuniyoshi Y, Kuriyama R, Omura S, Gutierrez CE, Sun Z, Feldotto B, Albanese U, Knoll AC, Yamada T, Hirayama T, Morin FO, Igarashi J, Doya K, Yamazaki T. Embodied bidirectional simulation of a spiking cortico-basal ganglia-cerebellar-thalamic brain model and a mouse musculoskeletal body model distributed across computers including the supercomputer Fugaku. Front Neurorobot 2023; 17:1269848. [PMID: 37867618 PMCID: PMC10585105 DOI: 10.3389/fnbot.2023.1269848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 09/12/2023] [Indexed: 10/24/2023] Open
Abstract
Embodied simulation with a digital brain model and a realistic musculoskeletal body model provides a means to understand animal behavior and behavioral change. Such simulation can be too large and complex to conduct on a single computer, and so distributed simulation across multiple computers over the Internet is necessary. In this study, we report our joint effort on developing a spiking brain model and a mouse body model, connecting over the Internet, and conducting bidirectional simulation while synchronizing them. Specifically, the brain model consisted of multiple regions including secondary motor cortex, primary motor and somatosensory cortices, basal ganglia, cerebellum and thalamus, whereas the mouse body model, provided by the Neurorobotics Platform of the Human Brain Project, had a movable forelimb with three joints and six antagonistic muscles to act in a virtual environment. Those were simulated in a distributed manner across multiple computers including the supercomputer Fugaku, which is the flagship supercomputer in Japan, while communicating via Robot Operating System (ROS). To incorporate models written in C/C++ in the distributed simulation, we developed a C++ version of the rosbridge library from scratch, which has been released under an open source license. These results provide necessary tools for distributed embodied simulation, and demonstrate its possibility and usefulness toward understanding animal behavior and behavioral change.
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Affiliation(s)
- Yusuke Kuniyoshi
- Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan
| | - Rin Kuriyama
- Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan
| | - Shu Omura
- Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan
| | - Carlos Enrique Gutierrez
- Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
| | - Zhe Sun
- Image Processing Research Team, Center for Advanced Photonics, RIKEN, Saitama, Japan
- Computational Engineering Applications Unit, Head Office for Information Systems and Cybersecurity, RIKEN, Saitama, Japan
| | - Benedikt Feldotto
- Robotics, Artificial Intelligence and Real-Time Systems, Faculty of Informatics, Technical University of Munich, Munich, Germany
| | - Ugo Albanese
- Department of Excellence in Robotics and AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Alois C. Knoll
- Robotics, Artificial Intelligence and Real-Time Systems, Faculty of Informatics, Technical University of Munich, Munich, Germany
| | - Taiki Yamada
- Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan
| | - Tomoya Hirayama
- Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan
| | - Fabrice O. Morin
- Robotics, Artificial Intelligence and Real-Time Systems, Faculty of Informatics, Technical University of Munich, Munich, Germany
| | - Jun Igarashi
- Computational Engineering Applications Unit, Head Office for Information Systems and Cybersecurity, RIKEN, Saitama, Japan
- Center for Computational Science, RIKEN, Hyogo, Japan
| | - Kenji Doya
- Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
| | - Tadashi Yamazaki
- Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan
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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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Gutierrez CE, Skibbe H, Musset H, Doya K. A Spiking Neural Network Builder for Systematic Data-to-Model Workflow. Front Neuroinform 2022; 16:855765. [PMID: 35909884 PMCID: PMC9326306 DOI: 10.3389/fninf.2022.855765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 05/30/2022] [Indexed: 11/16/2022] Open
Abstract
In building biological neural network models, it is crucial to efficiently convert diverse anatomical and physiological data into parameters of neurons and synapses and to systematically estimate unknown parameters in reference to experimental observations. Web-based tools for systematic model building can improve the transparency and reproducibility of computational models and can facilitate collaborative model building, validation, and evolution. Here, we present a framework to support collaborative data-driven development of spiking neural network (SNN) models based on the Entity-Relationship (ER) data description commonly used in large-scale business software development. We organize all data attributes, including species, brain regions, neuron types, projections, neuron models, and references as tables and relations within a database management system (DBMS) and provide GUI interfaces for data registration and visualization. This allows a robust “business-oriented” data representation that supports collaborative model building and traceability of source information for every detail of a model. We tested this data-to-model framework in cortical and striatal network models by successfully combining data from papers with existing neuron and synapse models and by generating NEST simulation codes for various network sizes. Our framework also helps to check data integrity and consistency and data comparisons across species. The framework enables the modeling of any region of the brain and is being deployed to support the integration of anatomical and physiological datasets from the brain/MINDS project for systematic SNN modeling of the marmoset brain.
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Affiliation(s)
- Carlos Enrique Gutierrez
- Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
- *Correspondence: Carlos Enrique Gutierrez
| | - Henrik Skibbe
- Brain Image Analysis Unit, RIKEN Center for Brain Science, Wako, Japan
| | - Hugo Musset
- Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
| | - Kenji Doya
- Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
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5
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Feldotto B, Eppler JM, Jimenez-Romero C, Bignamini C, Gutierrez CE, Albanese U, Retamino E, Vorobev V, Zolfaghari V, Upton A, Sun Z, Yamaura H, Heidarinejad M, Klijn W, Morrison A, Cruz F, McMurtrie C, Knoll AC, Igarashi J, Yamazaki T, Doya K, Morin FO. Deploying and Optimizing Embodied Simulations of Large-Scale Spiking Neural Networks on HPC Infrastructure. Front Neuroinform 2022; 16:884180. [PMID: 35662903 PMCID: PMC9160925 DOI: 10.3389/fninf.2022.884180] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 04/19/2022] [Indexed: 12/20/2022] Open
Abstract
Simulating the brain-body-environment trinity in closed loop is an attractive proposal to investigate how perception, motor activity and interactions with the environment shape brain activity, and vice versa. The relevance of this embodied approach, however, hinges entirely on the modeled complexity of the various simulated phenomena. In this article, we introduce a software framework that is capable of simulating large-scale, biologically realistic networks of spiking neurons embodied in a biomechanically accurate musculoskeletal system that interacts with a physically realistic virtual environment. We deploy this framework on the high performance computing resources of the EBRAINS research infrastructure and we investigate the scaling performance by distributing computation across an increasing number of interconnected compute nodes. Our architecture is based on requested compute nodes as well as persistent virtual machines; this provides a high-performance simulation environment that is accessible to multi-domain users without expert knowledge, with a view to enable users to instantiate and control simulations at custom scale via a web-based graphical user interface. Our simulation environment, entirely open source, is based on the Neurorobotics Platform developed in the context of the Human Brain Project, and the NEST simulator. We characterize the capabilities of our parallelized architecture for large-scale embodied brain simulations through two benchmark experiments, by investigating the effects of scaling compute resources on performance defined in terms of experiment runtime, brain instantiation and simulation time. The first benchmark is based on a large-scale balanced network, while the second one is a multi-region embodied brain simulation consisting of more than a million neurons and a billion synapses. Both benchmarks clearly show how scaling compute resources improves the aforementioned performance metrics in a near-linear fashion. The second benchmark in particular is indicative of both the potential and limitations of a highly distributed simulation in terms of a trade-off between computation speed and resource cost. Our simulation architecture is being prepared to be accessible for everyone as an EBRAINS service, thereby offering a community-wide tool with a unique workflow that should provide momentum to the investigation of closed-loop embodiment within the computational neuroscience community.
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Affiliation(s)
- Benedikt Feldotto
- Robotics, Artificial Intelligence and Real-Time Systems, Faculty of Informatics, Technical University of Munich, Munich, Germany
- *Correspondence: Benedikt Feldotto
| | - Jochen Martin Eppler
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Cristian Jimenez-Romero
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
| | | | - Carlos Enrique Gutierrez
- Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
| | - Ugo Albanese
- Department of Excellence in Robotics and AI, The BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy
| | - Eloy Retamino
- Department of Computer Architecture and Technology, Research Centre for Information and Communication Technologies, University of Granada, Granada, Spain
| | - Viktor Vorobev
- Robotics, Artificial Intelligence and Real-Time Systems, Faculty of Informatics, Technical University of Munich, Munich, Germany
| | - Vahid Zolfaghari
- Robotics, Artificial Intelligence and Real-Time Systems, Faculty of Informatics, Technical University of Munich, Munich, Germany
| | - Alex Upton
- Swiss National Supercomputing Centre (CSCS), ETH Zurich, Lugano, Switzerland
| | - Zhe Sun
- Image Processing Research Team, Center for Advanced Photonics, RIKEN, Wako, Japan
- Computational Engineering Applications Unit, Head Office for Information Systems and Cybersecurity, RIKEN, Wako, Japan
| | - Hiroshi Yamaura
- Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan
| | - Morteza Heidarinejad
- Computational Engineering Applications Unit, Head Office for Information Systems and Cybersecurity, RIKEN, Wako, Japan
| | - Wouter Klijn
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Abigail Morrison
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
- Jülich Research Centre, Institute of Neuroscience and Medicine (INM-6), Institute for Advanced Simulation (IAS-6), JARA BRAIN Institute I, Jülich, Germany
- Computer Science 3-Software Engineering, RWTH Aachen University, Aachen, Germany
| | - Felipe Cruz
- Swiss National Supercomputing Centre (CSCS), ETH Zurich, Lugano, Switzerland
| | - Colin McMurtrie
- Swiss National Supercomputing Centre (CSCS), ETH Zurich, Lugano, Switzerland
| | - Alois C. Knoll
- Robotics, Artificial Intelligence and Real-Time Systems, Faculty of Informatics, Technical University of Munich, Munich, Germany
| | - Jun Igarashi
- Computational Engineering Applications Unit, Head Office for Information Systems and Cybersecurity, RIKEN, Wako, Japan
- Center for Computational Science, RIKEN, Kobe, Japan
| | - Tadashi Yamazaki
- Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan
| | - Kenji Doya
- Neural Computation Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
| | - Fabrice O. Morin
- Robotics, Artificial Intelligence and Real-Time Systems, Faculty of Informatics, Technical University of Munich, Munich, Germany
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6
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Humphries MD, Gurney K. Making decisions in the dark basement of the brain: A look back at the GPR model of action selection and the basal ganglia. BIOLOGICAL CYBERNETICS 2021; 115:323-329. [PMID: 34272969 DOI: 10.1007/s00422-021-00887-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 07/06/2021] [Indexed: 06/13/2023]
Abstract
How does your brain decide what you will do next? Over the past few decades compelling evidence has emerged that the basal ganglia, a collection of nuclei in the fore- and mid-brain of all vertebrates, are vital to action selection. Gurney, Prescott, and Redgrave published an influential computational account of this idea in Biological Cybernetics in 2001. Here we take a look back at this pair of papers, outlining the "GPR" model contained therein, the context of that model's development, and the influence it has had over the past twenty years. Tracing its lineage into models and theories still emerging now, we are encouraged that the GPR model is that rare thing, a computational model of a brain circuit whose advances were directly built on by others.
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7
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Girard B, Lienard J, Gutierrez CE, Delord B, Doya K. A biologically constrained spiking neural network model of the primate basal ganglia with overlapping pathways exhibits action selection. Eur J Neurosci 2020; 53:2254-2277. [PMID: 32564449 PMCID: PMC8246891 DOI: 10.1111/ejn.14869] [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] [Received: 01/28/2020] [Revised: 05/19/2020] [Accepted: 06/08/2020] [Indexed: 12/19/2022]
Abstract
Action selection has been hypothesized to be a key function of the basal ganglia, yet the nuclei involved, their interactions and the importance of the direct/indirect pathway segregation in such process remain debated. Here, we design a spiking computational model of the monkey basal ganglia derived from a previously published population model, initially parameterized to reproduce electrophysiological activity at rest and to embody as much quantitative anatomical data as possible. As a particular feature, both models exhibit the strong overlap between the direct and indirect pathways that has been documented in non-human primates. Here, we first show how the translation from a population to an individual neuron model was achieved, with the addition of a minimal number of parameters. We then show that our model performs action selection, even though it was built without any assumption on the activity carried out during behaviour. We investigate the mechanisms of this selection through circuit disruptions and found an instrumental role of the off-centre/on-surround structure of the MSN-STN-GPi circuit, as well as of the MSN-MSN and FSI-MSN projections. This validates their potency in enabling selection. We finally study the pervasive centromedian and parafascicular thalamic inputs that reach all basal ganglia nuclei and whose influence is therefore difficult to anticipate. Our model predicts that these inputs modulate the responsiveness of action selection, making them a candidate for the regulation of the speed-accuracy trade-off during decision-making.
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Affiliation(s)
- Benoît Girard
- Institut des Systèmes Intelligent et de Robotique (ISIR), Sorbonne Université, CNRS, Paris, France
| | - Jean Lienard
- Neural Computation Unit, Okinawa Institute of Science and Technology, Kunigami-gun, Japan
| | | | - Bruno Delord
- Institut des Systèmes Intelligent et de Robotique (ISIR), Sorbonne Université, CNRS, Paris, France
| | - Kenji Doya
- Neural Computation Unit, Okinawa Institute of Science and Technology, Kunigami-gun, Japan
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8
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Humphries MD, Obeso JA, Dreyer JK. Insights into Parkinson's disease from computational models of the basal ganglia. J Neurol Neurosurg Psychiatry 2018; 89:1181-1188. [PMID: 29666208 PMCID: PMC6124639 DOI: 10.1136/jnnp-2017-315922] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 03/20/2018] [Accepted: 03/21/2018] [Indexed: 12/28/2022]
Abstract
Movement disorders arise from the complex interplay of multiple changes to neural circuits. Successful treatments for these disorders could interact with these complex changes in myriad ways, and as a consequence their mechanisms of action and their amelioration of symptoms are incompletely understood. Using Parkinson's disease as a case study, we review here how computational models are a crucial tool for taming this complexity, across causative mechanisms, consequent neural dynamics and treatments. For mechanisms, we review models that capture the effects of losing dopamine on basal ganglia function; for dynamics, we discuss models that have transformed our understanding of how beta-band (15-30 Hz) oscillations arise in the parkinsonian basal ganglia. For treatments, we touch on the breadth of computational modelling work trying to understand the therapeutic actions of deep brain stimulation. Collectively, models from across all levels of description are providing a compelling account of the causes, symptoms and treatments for Parkinson's disease.
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Affiliation(s)
- Mark D Humphries
- Faculty of Biology, Medicine, and Health, University of Manchester, Manchester, UK.,School of Psychology, University of Nottingham, Nottingham, UK
| | - Jose Angel Obeso
- HM-CINAC, Hospital Puerta del Sur, Mostoles, CEU-San Pablo University, Madrid, Spain
| | - Jakob Kisbye Dreyer
- Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark.,Department of Bioinformatics, H Lundbeck A/S, Valby, Denmark
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9
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Caiola M, Holmes MH. Model and Analysis for the Onset of Parkinsonian Firing Patterns in a Simplified Basal Ganglia. Int J Neural Syst 2018; 29:1850021. [PMID: 29886807 DOI: 10.1142/s0129065718500211] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Parkinson's disease (PD) is a degenerative neurological disease that disrupts the movement cycle in the basal ganglia. As the disease progresses, dopamine depletion leads to changes to how the basal ganglia functions as well as the appearance of abnormal beta oscillations. There is much debate on just exactly how these connection strengths change and just how the oscillations emerge. One leading hypothesis claims that the oscillations develop in the globus pallidus external, subthalamic nucleus, and globus pallidus internal loop. We introduce a mathematical model that calculates the average firing rates of this loop while still accounting for the larger closed loop of the entire basal ganglia system. This model is constructed such that physiologically realistic results can be obtained while not sacrificing the use of analytic methods. Because of this, it is possible to determine how the change in the connection strengths can drive the necessary changes in firing rates seen in recordings and account for the generation of trademark beta oscillations of PD without relying on highly specific time delays, stochastic approaches, or numerical approximations. Additionally, we find that the entire cortico-basal ganglia-thalamo-cortical loop is essential for abnormal oscillations to originate.
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Affiliation(s)
- Michael Caiola
- 1 Department of Mathematical Sciences, Rensselaer Polytechnic Institute, 110, 8th Street, Troy, New York 12180, USA
| | - Mark H Holmes
- 1 Department of Mathematical Sciences, Rensselaer Polytechnic Institute, 110, 8th Street, Troy, New York 12180, USA
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10
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Salimi-Badr A, Ebadzadeh MM, Darlot C. A system-level mathematical model of Basal Ganglia motor-circuit for kinematic planning of arm movements. Comput Biol Med 2018; 92:78-89. [PMID: 29156412 DOI: 10.1016/j.compbiomed.2017.11.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2017] [Revised: 11/06/2017] [Accepted: 11/06/2017] [Indexed: 01/01/2023]
Abstract
In this paper, a novel system-level mathematical model of the Basal Ganglia (BG) for kinematic planning, is proposed. An arm composed of several segments presents a geometric redundancy. Thus, selecting one trajectory among an infinite number of possible ones requires overcoming redundancy, according to some kinds of optimization. Solving this optimization is assumed to be the function of BG in planning. In the proposed model, first, a mathematical solution of kinematic planning is proposed for movements of a redundant arm in a plane, based on minimizing energy consumption. Next, the function of each part in the model is interpreted as a possible role of a nucleus of BG. Since the kinematic variables are considered as vectors, the proposed model is presented based on the vector calculus. This vector model predicts different neuronal populations in BG which is in accordance with some recent experimental studies. According to the proposed model, the function of the direct pathway is to calculate the necessary rotation of each joint, and the function of the indirect pathway is to control each joint rotation considering the movement of the other joints. In the proposed model, the local feedback loop between Subthalamic Nucleus and Globus Pallidus externus is interpreted as a local memory to store the previous amounts of movements of the other joints, which are utilized by the indirect pathway. In this model, activities of dopaminergic neurons would encode, at short-term, the error between the desired and actual positions of the end-effector. The short-term modulating effect of dopamine on Striatum is also modeled as cross product. The model is simulated to generate the commands of a redundant manipulator. The performance of the model is studied for different reaching movements between 8 points in a plane. Finally, some symptoms of Parkinson's disease such as bradykinesia and akinesia are simulated by modifying the model parameters, inspired by the dopamine depletion.
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Affiliation(s)
- Armin Salimi-Badr
- Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran; INSERM-U1093 Cognition, Action, et Plasticité Sensorimotrice, Université de Bourgogne, Dijon, France
| | - Mohammad Mehdi Ebadzadeh
- Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran.
| | - Christian Darlot
- INSERM-U1093 Cognition, Action, et Plasticité Sensorimotrice, Université de Bourgogne, Dijon, France
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11
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A possible correlation between the basal ganglia motor function and the inverse kinematics calculation. J Comput Neurosci 2017; 43:295-318. [DOI: 10.1007/s10827-017-0665-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2017] [Revised: 09/24/2017] [Accepted: 10/02/2017] [Indexed: 10/18/2022]
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12
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Viejo G, Girard B, Procyk E, Khamassi M. Adaptive coordination of working-memory and reinforcement learning in non-human primates performing a trial-and-error problem solving task. Behav Brain Res 2017; 355:76-89. [PMID: 29061387 DOI: 10.1016/j.bbr.2017.09.030] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2017] [Revised: 08/08/2017] [Accepted: 09/18/2017] [Indexed: 10/18/2022]
Abstract
Accumulating evidence suggest that human behavior in trial-and-error learning tasks based on decisions between discrete actions may involve a combination of reinforcement learning (RL) and working-memory (WM). While the understanding of brain activity at stake in this type of tasks often involve the comparison with non-human primate neurophysiological results, it is not clear whether monkeys use similar combined RL and WM processes to solve these tasks. Here we analyzed the behavior of five monkeys with computational models combining RL and WM. Our model-based analysis approach enables to not only fit trial-by-trial choices but also transient slowdowns in reaction times, indicative of WM use. We found that the behavior of the five monkeys was better explained in terms of a combination of RL and WM despite inter-individual differences. The same coordination dynamics we used in a previous study in humans best explained the behavior of some monkeys while the behavior of others showed the opposite pattern, revealing a possible different dynamics of WM process. We further analyzed different variants of the tested models to open a discussion on how the long pretraining in these tasks may have favored particular coordination dynamics between RL and WM. This points towards either inter-species differences or protocol differences which could be further tested in humans.
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Affiliation(s)
- Guillaume Viejo
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, Institute of Intelligent Systems and Robotics (ISIR), F-75005 Paris, France; Montreal Neurological Institute and Hospital, 3801 University Street, Montreal, Quebec, Canada.
| | - Benoît Girard
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, Institute of Intelligent Systems and Robotics (ISIR), F-75005 Paris, France
| | - Emmanuel Procyk
- University of Lyon, Université Claude Bernard Lyon 1, INSERM, Stem Cell and Brain Research Institute, U1208 Lyon, France
| | - Mehdi Khamassi
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, Institute of Intelligent Systems and Robotics (ISIR), F-75005 Paris, France
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Blenkinsop A, Anderson S, Gurney K. Frequency and function in the basal ganglia: the origins of beta and gamma band activity. J Physiol 2017; 595:4525-4548. [PMID: 28334424 DOI: 10.1113/jp273760] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2016] [Accepted: 03/02/2017] [Indexed: 01/28/2023] Open
Abstract
KEY POINTS Neuronal oscillations in the basal ganglia have been observed to correlate with behaviours, although the causal mechanisms and functional significance of these oscillations remain unknown. We present a novel computational model of the healthy basal ganglia, constrained by single unit recordings from non-human primates. When the model is run using inputs that might be expected during performance of a motor task, the network shows emergent phenomena: it functions as a selection mechanism and shows spectral properties that match those seen in vivo. Beta frequency oscillations are shown to require pallido-striatal feedback, and occur with behaviourally relevant cortical input. Gamma oscillations arise in the subthalamic-globus pallidus feedback loop, and occur during movement. The model provides a coherent framework for the study of spectral, temporal and functional analyses of the basal ganglia and lays the foundation for an integrated approach to study basal ganglia pathologies such as Parkinson's disease in silico. ABSTRACT Neural oscillations in the basal ganglia (BG) are well studied yet remain poorly understood. Behavioural correlates of spectral activity are well described, yet a quantitative hypothesis linking time domain dynamics and spectral properties to BG function has been lacking. We show, for the first time, that a unified description is possible by interpreting previously ignored structure in data describing globus pallidus interna responses to cortical stimulation. These data were used to expose a pair of distinctive neuronal responses to the stimulation. This observation formed the basis for a new mathematical model of the BG, quantitatively fitted to the data, which describes the dynamics in the data, and is validated against other stimulus protocol experiments. A key new result is that when the model is run using inputs hypothesised to occur during the performance of a motor task, beta and gamma frequency oscillations emerge naturally during static-force and movement, respectively, consistent with experimental local field potentials. This new model predicts that the pallido-striatum connection has a key role in the generation of beta band activity, and that the gamma band activity associated with motor task performance has its origins in the pallido-subthalamic feedback loop. The network's functionality as a selection mechanism also occurs as an emergent property, and closer fits to the data gave better selection properties. The model provides a coherent framework for the study of spectral, temporal and functional analyses of the BG and therefore lays the foundation for an integrated approach to study BG pathologies such as Parkinson's disease in silico.
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Affiliation(s)
| | - Sean Anderson
- Automatic Control & Systems Engineering, University of Sheffield, Sheffield, S1 3JD, UK
| | - Kevin Gurney
- Department of Psychology, University of Sheffield, Sheffield, S10 2TP, UK
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14
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Abstract
Unidirectional connections from the cortex to the matrix of the corpus striatum initiate the cortico-basal ganglia (BG)-thalamocortical loop, thought to be important in momentary action selection and in longer-term fine tuning of behavioural repertoire; a discrete set of striatal compartments, striosomes, has the complementary role of registering or anticipating reward that shapes corticostriatal plasticity. Re-entrant signals traversing the cortico-BG loop impact predominantly frontal cortices, conveyed through topographically ordered output channels; by contrast, striatal input signals originate from a far broader span of cortex, and are far more divergent in their termination. The term 'disclosed loop' is introduced to describe this organisation: a closed circuit that is open to outside influence at the initial stage of cortical input. The closed circuit component of corticostriatal afferents is newly dubbed 'operative', as it is proposed to establish the bid for action selection on the part of an incipient cortical action plan; the broader set of converging corticostriatal afferents is described as contextual. A corollary of this proposal is that every unit of the striatal volume, including the long, C-shaped tail of the caudate nucleus, should receive a mandatory component of operative input, and hence include at least one area of BG-recipient cortex amongst the sources of its corticostriatal afferents. Individual operative afferents contact twin classes of GABAergic striatal projection neuron (SPN), distinguished by their neurochemical character, and onward circuitry. This is the basis of the classic direct and indirect pathway model of the cortico-BG loop. Each pathway utilises a serial chain of inhibition, with two such links, or three, providing positive and negative feedback, respectively. Operative co-activation of direct and indirect SPNs is, therefore, pictured to simultaneously promote action, and to restrain it. The balance of this rival activity is determined by the contextual inputs, which summarise the external and internal sensory environment, and the state of ongoing behavioural priorities. Notably, the distributed sources of contextual convergence upon a striatal locus mirror the transcortical network harnessed by the origin of the operative input to that locus, thereby capturing a similar set of contingencies relevant to determining action. The disclosed loop formulation of corticostriatal and subsequent BG loop circuitry, as advanced here, refines the operating rationale of the classic model and allows the integration of more recent anatomical and physiological data, some of which can appear at variance with the classic model. Equally, it provides a lucid functional context for continuing cellular studies of SPN biophysics and mechanisms of synaptic plasticity.
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15
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Thorson IL, Liénard J, David SV. The Essential Complexity of Auditory Receptive Fields. PLoS Comput Biol 2015; 11:e1004628. [PMID: 26683490 PMCID: PMC4684325 DOI: 10.1371/journal.pcbi.1004628] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2015] [Accepted: 10/26/2015] [Indexed: 12/05/2022] Open
Abstract
Encoding properties of sensory neurons are commonly modeled using linear finite impulse response (FIR) filters. For the auditory system, the FIR filter is instantiated in the spectro-temporal receptive field (STRF), often in the framework of the generalized linear model. Despite widespread use of the FIR STRF, numerous formulations for linear filters are possible that require many fewer parameters, potentially permitting more efficient and accurate model estimates. To explore these alternative STRF architectures, we recorded single-unit neural activity from auditory cortex of awake ferrets during presentation of natural sound stimuli. We compared performance of > 1000 linear STRF architectures, evaluating their ability to predict neural responses to a novel natural stimulus. Many were able to outperform the FIR filter. Two basic constraints on the architecture lead to the improved performance: (1) factorization of the STRF matrix into a small number of spectral and temporal filters and (2) low-dimensional parameterization of the factorized filters. The best parameterized model was able to outperform the full FIR filter in both primary and secondary auditory cortex, despite requiring fewer than 30 parameters, about 10% of the number required by the FIR filter. After accounting for noise from finite data sampling, these STRFs were able to explain an average of 40% of A1 response variance. The simpler models permitted more straightforward interpretation of sensory tuning properties. They also showed greater benefit from incorporating nonlinear terms, such as short term plasticity, that provide theoretical advances over the linear model. Architectures that minimize parameter count while maintaining maximum predictive power provide insight into the essential degrees of freedom governing auditory cortical function. They also maximize statistical power available for characterizing additional nonlinear properties that limit current auditory models. Understanding how the brain solves sensory problems can provide useful insight for the development of automated systems such as speech recognizers and image classifiers. Recent developments in nonlinear regression and machine learning have produced powerful algorithms for characterizing the input-output relationship of complex systems. However, the complexity of sensory neural systems, combined with practical limitations on experimental data, make it difficult to apply arbitrarily complex analyses to neural data. In this study we pushed analysis in the opposite direction, toward simpler models. We asked how simple a model can be while still capturing the essential sensory properties of neurons in auditory cortex. We found that substantially simpler formulations of the widely-used spectro-temporal receptive field are able to perform as well as the best current models. These simpler formulations define new basis sets that can be incorporated into state-of-the-art machine learning algorithms for a more exhaustive exploration of sensory processing.
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Affiliation(s)
- Ivar L. Thorson
- Oregon Hearing Research Center, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Jean Liénard
- Department of Mathematics, Washington State University, Vancouver, Washington, United States of America
| | - Stephen V. David
- Oregon Hearing Research Center, Oregon Health & Science University, Portland, Oregon, United States of America
- * E-mail:
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16
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Viejo G, Khamassi M, Brovelli A, Girard B. Modeling choice and reaction time during arbitrary visuomotor learning through the coordination of adaptive working memory and reinforcement learning. Front Behav Neurosci 2015; 9:225. [PMID: 26379518 PMCID: PMC4549628 DOI: 10.3389/fnbeh.2015.00225] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2015] [Accepted: 08/10/2015] [Indexed: 11/18/2022] Open
Abstract
Current learning theory provides a comprehensive description of how humans and other animals learn, and places behavioral flexibility and automaticity at heart of adaptive behaviors. However, the computations supporting the interactions between goal-directed and habitual decision-making systems are still poorly understood. Previous functional magnetic resonance imaging (fMRI) results suggest that the brain hosts complementary computations that may differentially support goal-directed and habitual processes in the form of a dynamical interplay rather than a serial recruitment of strategies. To better elucidate the computations underlying flexible behavior, we develop a dual-system computational model that can predict both performance (i.e., participants' choices) and modulations in reaction times during learning of a stimulus–response association task. The habitual system is modeled with a simple Q-Learning algorithm (QL). For the goal-directed system, we propose a new Bayesian Working Memory (BWM) model that searches for information in the history of previous trials in order to minimize Shannon entropy. We propose a model for QL and BWM coordination such that the expensive memory manipulation is under control of, among others, the level of convergence of the habitual learning. We test the ability of QL or BWM alone to explain human behavior, and compare them with the performance of model combinations, to highlight the need for such combinations to explain behavior. Two of the tested combination models are derived from the literature, and the latter being our new proposal. In conclusion, all subjects were better explained by model combinations, and the majority of them are explained by our new coordination proposal.
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Affiliation(s)
- Guillaume Viejo
- Sorbonne Université, Université Pierre et Marie Curie, Univ Paris 06, UMR 7222, Institut des Systèmes Intelligents et de Robotique Paris, France ; Centre National de la Recherche Scientifique, UMR 7222, ISIR Paris, France
| | - Mehdi Khamassi
- Sorbonne Université, Université Pierre et Marie Curie, Univ Paris 06, UMR 7222, Institut des Systèmes Intelligents et de Robotique Paris, France ; Centre National de la Recherche Scientifique, UMR 7222, ISIR Paris, France
| | - Andrea Brovelli
- Institut de Neurosciences de la Timone, UMR 7289, Centre National de la Recherche Scientifique - Aix Marseille Université Marseille, France
| | - Benoît Girard
- Sorbonne Université, Université Pierre et Marie Curie, Univ Paris 06, UMR 7222, Institut des Systèmes Intelligents et de Robotique Paris, France ; Centre National de la Recherche Scientifique, UMR 7222, ISIR Paris, France
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17
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Humphries MD. Slaves to the rhythm: coupling of the subthalamic nucleus-globus pallidus network in Parkinsonian oscillations. J Physiol 2014; 592:1427-8. [PMID: 24692460 DOI: 10.1113/jphysiol.2014.270736] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
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