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Hu K, Zhang Y, Ding F, Yang D, Yu Y, Yu Y, Wang Q, Baoyin H. Innate Orientating Behavior of a Multi-Legged Robot Driven by the Neural Circuits of C. elegans. Biomimetics (Basel) 2024; 9:314. [PMID: 38921194 DOI: 10.3390/biomimetics9060314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 05/08/2024] [Accepted: 05/15/2024] [Indexed: 06/27/2024] Open
Abstract
The objective of this research is to achieve biologically autonomous control by utilizing a whole-brain network model, drawing inspiration from biological neural networks to enhance the development of bionic intelligence. Here, we constructed a whole-brain neural network model of Caenorhabditis elegans (C. elegans), which characterizes the electrochemical processes at the level of the cellular synapses. The neural network simulation integrates computational programming and the visualization of the neurons and synapse connections of C. elegans, containing the specific controllable circuits and their dynamic characteristics. To illustrate the biological neural network (BNN)'s particular intelligent control capability, we introduced an innovative methodology for applying the BNN model to a 12-legged robot's movement control. Two methods were designed, one involving orientation control and the other involving locomotion generation, to demonstrate the intelligent control performance of the BNN. Both the simulation and experimental results indicate that the robot exhibits more autonomy and a more intelligent movement performance under BNN control. The systematic approach of employing the whole-brain BNN for robot control provides biomimetic research with a framework that has been substantiated by innovative methodologies and validated through the observed positive outcomes. This method is established as follows: (1) two integrated dynamic models of the C. elegans' whole-brain network and the robot moving dynamics are built, and all of the controllable circuits are discovered and verified; (2) real-time communication is achieved between the BNN model and the robot's dynamical model, both in the simulation and the experiments, including applicable encoding and decoding algorithms, facilitating their collaborative operation; (3) the designed mechanisms using the BNN model to control the robot are shown to be effective through numerical and experimental tests, focusing on 'foraging' behavior control and locomotion control.
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Affiliation(s)
- Kangxin Hu
- School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China
| | - Yu Zhang
- School of Aerospace Engineering, Tsinghua University, Beijing 100084, China
| | - Fei Ding
- School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China
| | - Dun Yang
- School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China
| | - Yang Yu
- School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China
| | - Ying Yu
- School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China
| | - Qingyun Wang
- School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China
| | - Hexi Baoyin
- School of Aerospace Engineering, Tsinghua University, Beijing 100084, China
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2
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Kusch L, Diaz-Pier S, Klijn W, Sontheimer K, Bernard C, Morrison A, Jirsa V. Multiscale co-simulation design pattern for neuroscience applications. Front Neuroinform 2024; 18:1156683. [PMID: 38410682 PMCID: PMC10895016 DOI: 10.3389/fninf.2024.1156683] [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: 02/01/2023] [Accepted: 01/19/2024] [Indexed: 02/28/2024] Open
Abstract
Integration of information across heterogeneous sources creates added scientific value. Interoperability of data, tools and models is, however, difficult to accomplish across spatial and temporal scales. Here we introduce the toolbox Parallel Co-Simulation, which enables the interoperation of simulators operating at different scales. We provide a software science co-design pattern and illustrate its functioning along a neuroscience example, in which individual regions of interest are simulated on the cellular level allowing us to study detailed mechanisms, while the remaining network is efficiently simulated on the population level. A workflow is illustrated for the use case of The Virtual Brain and NEST, in which the CA1 region of the cellular-level hippocampus of the mouse is embedded into a full brain network involving micro and macro electrode recordings. This new tool allows integrating knowledge across scales in the same simulation framework and validating them against multiscale experiments, thereby largely widening the explanatory power of computational models.
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Affiliation(s)
- Lionel Kusch
- Institut de Neurosciences des Systèmes (INS), UMR1106, Aix-Marseille Université, Marseilles, France
| | - Sandra Diaz-Pier
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Wouter Klijn
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Kim Sontheimer
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Christophe Bernard
- Institut de Neurosciences des Systèmes (INS), UMR1106, Aix-Marseille Université, Marseilles, France
| | - Abigail Morrison
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre (JSC), Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
- Forschungszentrum Jülich GmbH, IAS-6/INM-6, JARA, Jülich, Germany
- Computer Science 3 - Software Engineering, RWTH Aachen University, Aachen, Germany
| | - Viktor Jirsa
- Institut de Neurosciences des Systèmes (INS), UMR1106, Aix-Marseille Université, Marseilles, France
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3
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Ponce-Alvarez A, Deco G. The Hopf whole-brain model and its linear approximation. Sci Rep 2024; 14:2615. [PMID: 38297071 PMCID: PMC10831083 DOI: 10.1038/s41598-024-53105-0] [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: 09/16/2023] [Accepted: 01/27/2024] [Indexed: 02/02/2024] Open
Abstract
Whole-brain models have proven to be useful to understand the emergence of collective activity among neural populations or brain regions. These models combine connectivity matrices, or connectomes, with local node dynamics, noise, and, eventually, transmission delays. Multiple choices for the local dynamics have been proposed. Among them, nonlinear oscillators corresponding to a supercritical Hopf bifurcation have been used to link brain connectivity and collective phase and amplitude dynamics in different brain states. Here, we studied the linear fluctuations of this model to estimate its stationary statistics, i.e., the instantaneous and lagged covariances and the power spectral densities. This linear approximation-that holds in the case of heterogeneous parameters and time-delays-allows analytical estimation of the statistics and it can be used for fast parameter explorations to study changes in brain state, changes in brain activity due to alterations in structural connectivity, and modulations of parameter due to non-equilibrium dynamics.
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Affiliation(s)
- Adrián Ponce-Alvarez
- Departament de Matemàtiques, Universitat Politècnica de Catalunya, 08028, Barcelona, Spain.
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08005, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), 08010, Barcelona, Spain
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Salfenmoser L, Obermayer K. Nonlinear optimal control of a mean-field model of neural population dynamics. Front Comput Neurosci 2022; 16:931121. [PMID: 35990368 PMCID: PMC9382303 DOI: 10.3389/fncom.2022.931121] [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: 04/28/2022] [Accepted: 07/11/2022] [Indexed: 11/13/2022] Open
Abstract
We apply the framework of nonlinear optimal control to a biophysically realistic neural mass model, which consists of two mutually coupled populations of deterministic excitatory and inhibitory neurons. External control signals are realized by time-dependent inputs to both populations. Optimality is defined by two alternative cost functions that trade the deviation of the controlled variable from its target value against the “strength” of the control, which is quantified by the integrated 1- and 2-norms of the control signal. We focus on a bistable region in state space where one low- (“down state”) and one high-activity (“up state”) stable fixed points coexist. With methods of nonlinear optimal control, we search for the most cost-efficient control function to switch between both activity states. For a broad range of parameters, we find that cost-efficient control strategies consist of a pulse of finite duration to push the state variables only minimally into the basin of attraction of the target state. This strategy only breaks down once we impose time constraints that force the system to switch on a time scale comparable to the duration of the control pulse. Penalizing control strength via the integrated 1-norm (2-norm) yields control inputs targeting one or both populations. However, whether control inputs to the excitatory or the inhibitory population dominate, depends on the location in state space relative to the bifurcation lines. Our study highlights the applicability of nonlinear optimal control to understand neuronal processing under constraints better.
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Jajcay N, Cakan C, Obermayer K. Cross-Frequency Slow Oscillation–Spindle Coupling in a Biophysically Realistic Thalamocortical Neural Mass Model. Front Comput Neurosci 2022; 16:769860. [PMID: 35603132 PMCID: PMC9120371 DOI: 10.3389/fncom.2022.769860] [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: 09/02/2021] [Accepted: 03/28/2022] [Indexed: 11/13/2022] Open
Abstract
Sleep manifests itself by the spontaneous emergence of characteristic oscillatory rhythms, which often time-lock and are implicated in memory formation. Here, we analyze a neural mass model of the thalamocortical loop in which the cortical node can generate slow oscillations (approximately 1 Hz) while its thalamic component can generate fast sleep spindles of σ-band activity (12–15 Hz). We study the dynamics for different coupling strengths between the thalamic and cortical nodes, for different conductance values of the thalamic node's potassium leak and hyperpolarization-activated cation-nonselective currents, and for different parameter regimes of the cortical node. The latter are listed as follows: (1) a low activity (DOWN) state with noise-induced, transient excursions into a high activity (UP) state, (2) an adaptation induced slow oscillation limit cycle with alternating UP and DOWN states, and (3) a high activity (UP) state with noise-induced, transient excursions into the low activity (DOWN) state. During UP states, thalamic spindling is abolished or reduced. During DOWN states, the thalamic node generates sleep spindles, which in turn can cause DOWN to UP transitions in the cortical node. Consequently, this leads to spindle-induced UP state transitions in parameter regime (1), thalamic spindles induced in some but not all DOWN states in regime (2), and thalamic spindles following UP to DOWN transitions in regime (3). The spindle-induced σ-band activity in the cortical node, however, is typically the strongest during the UP state, which follows a DOWN state “window of opportunity” for spindling. When the cortical node is parametrized in regime (3), the model well explains the interactions between slow oscillations and sleep spindles observed experimentally during Non-Rapid Eye Movement sleep. The model is computationally efficient and can be integrated into large-scale modeling frameworks to study spatial aspects like sleep wave propagation.
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Affiliation(s)
- Nikola Jajcay
- Neural Information Processing Group, Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Department of Complex Systems, Institute of Computer Science, Czech Academy of Sciences, Prague, Czechia
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
- *Correspondence: Nikola Jajcay
| | - Caglar Cakan
- Neural Information Processing Group, Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Klaus Obermayer
- Neural Information Processing Group, Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
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Cakan C, Dimulescu C, Khakimova L, Obst D, Flöel A, Obermayer K. Spatiotemporal Patterns of Adaptation-Induced Slow Oscillations in a Whole-Brain Model of Slow-Wave Sleep. Front Comput Neurosci 2022; 15:800101. [PMID: 35095451 PMCID: PMC8790481 DOI: 10.3389/fncom.2021.800101] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 12/16/2021] [Indexed: 11/13/2022] Open
Abstract
During slow-wave sleep, the brain is in a self-organized regime in which slow oscillations (SOs) between up- and down-states travel across the cortex. While an isolated piece of cortex can produce SOs, the brain-wide propagation of these oscillations are thought to be mediated by the long-range axonal connections. We address the mechanism of how SOs emerge and recruit large parts of the brain using a whole-brain model constructed from empirical connectivity data in which SOs are induced independently in each brain area by a local adaptation mechanism. Using an evolutionary optimization approach, good fits to human resting-state fMRI data and sleep EEG data are found at values of the adaptation strength close to a bifurcation where the model produces a balance between local and global SOs with realistic spatiotemporal statistics. Local oscillations are more frequent, last shorter, and have a lower amplitude. Global oscillations spread as waves of silence across the undirected brain graph, traveling from anterior to posterior regions. These traveling waves are caused by heterogeneities in the brain network in which the connection strengths between brain areas determine which areas transition to a down-state first, and thus initiate traveling waves across the cortex. Our results demonstrate the utility of whole-brain models for explaining the origin of large-scale cortical oscillations and how they are shaped by the connectome.
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Affiliation(s)
- Caglar Cakan
- Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Cristiana Dimulescu
- Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Liliia Khakimova
- Department of Neurology, University Medicine, Greifswald, Germany
| | - Daniela Obst
- Department of Neurology, University Medicine, Greifswald, Germany
| | - Agnes Flöel
- Department of Neurology, University Medicine, Greifswald, Germany
- German Center for Neurodegenerative Diseases, Greifswald, Germany
| | - Klaus Obermayer
- Department of Software Engineering and Theoretical Computer Science, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
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Herbers P, Calvo I, Diaz-Pier S, Robles OD, Mata S, Toharia P, Pastor L, Peyser A, Morrison A, Klijn W. ConGen—A Simulator-Agnostic Visual Language for Definition and Generation of Connectivity in Large and Multiscale Neural Networks. Front Neuroinform 2022; 15:766697. [PMID: 35069166 PMCID: PMC8777257 DOI: 10.3389/fninf.2021.766697] [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: 08/29/2021] [Accepted: 11/30/2021] [Indexed: 11/21/2022] Open
Abstract
An open challenge on the road to unraveling the brain's multilevel organization is establishing techniques to research connectivity and dynamics at different scales in time and space, as well as the links between them. This work focuses on the design of a framework that facilitates the generation of multiscale connectivity in large neural networks using a symbolic visual language capable of representing the model at different structural levels—ConGen. This symbolic language allows researchers to create and visually analyze the generated networks independently of the simulator to be used, since the visual model is translated into a simulator-independent language. The simplicity of the front end visual representation, together with the simulator independence provided by the back end translation, combine into a framework to enhance collaboration among scientists with expertise at different scales of abstraction and from different fields. On the basis of two use cases, we introduce the features and possibilities of our proposed visual language and associated workflow. We demonstrate that ConGen enables the creation, editing, and visualization of multiscale biological neural networks and provides a whole workflow to produce simulation scripts from the visual representation of the model.
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Affiliation(s)
- Patrick Herbers
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre, Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Iago Calvo
- Department of Computer Science and Computer Architecture, Lenguajes y Sistemas Informáticos y Estadística e Investigación Operativa, Rey Juan Carlos University, Madrid, Spain
| | - Sandra Diaz-Pier
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre, Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
- *Correspondence: Wouter Klijn
| | - Oscar D. Robles
- Department of Computer Science and Computer Architecture, Lenguajes y Sistemas Informáticos y Estadística e Investigación Operativa, Rey Juan Carlos University, Madrid, Spain
- Center for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain
| | - Susana Mata
- Department of Computer Science and Computer Architecture, Lenguajes y Sistemas Informáticos y Estadística e Investigación Operativa, Rey Juan Carlos University, Madrid, Spain
- Center for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain
| | - Pablo Toharia
- Center for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain
- DATSI, ETSIINF, Universidad Politécnica de Madrid, Madrid, Spain
| | - Luis Pastor
- Department of Computer Science and Computer Architecture, Lenguajes y Sistemas Informáticos y Estadística e Investigación Operativa, Rey Juan Carlos University, Madrid, Spain
- Center for Computational Simulation, Universidad Politécnica de Madrid, Madrid, Spain
| | - Alexander Peyser
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre, Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Abigail Morrison
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre, Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
- Institute of Neuroscience and Medicine and Institute for Advanced Simulation and JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany
- Computer Science 3 - Software Engineering, RWTH Aachen University, Aachen, Germany
| | - Wouter Klijn
- Simulation and Data Lab Neuroscience, Jülich Supercomputing Centre, Institute for Advanced Simulation, JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
- Sandra Diaz-Pier
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