5 research outputs found
Deploying and Optimizing Embodied Simulations of Large-Scale Spiking Neural Networks on HPC Infrastructure
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 virtualmachines;
this provides a high-performance simulation environment that is accessible to multidomain
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 largescale
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.European Union’s Horizon
2020 Framework Programme 785907 945539European Union’s Horizon
2020 800858MEXT (hp200139, hp210169) MEXT KAKENHI grant
no. 17H06310
Deploying and Optimizing Embodied Simulations of Large-Scale Spiking Neural Networks on HPC Infrastructure
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.journal articl
Human-robot collaborative scene mapping from relational descriptions
In this article we propose a method for cooperatively building a scene map between a human and a robot by using a spatial relational model employed by the robot to interpret human descriptions of the scene. The description will consist in a set of spatial relations between the objects in the scene. The scene map will contain the position of these objects. For this end we propose a model based on the generation of scalar fields of applicability for each of the available relations. The method can be summarized as follows. In first place a person will come into the room and describe the scene to the robot, including in the description semantic information about the objects which the robot can't get from its sensors. From the description the robot will form the scene mental map. In second place the robot will sense the scene with a 2D range laser building the scene sensed map. The objects positions in the mental map will be used to guide the sensing process. In a third step the robot will fuse the two maps, linking the semantic information about the described objects to the corresponding sensed ones. The resulting map is called the scene enriched map.Peer Reviewe
Human-robot collaborative scene mapping from relational descriptions
In this article we propose a method for cooperatively building a scene map between a human and a robot by using a spatial relational model employed by the robot to interpret human descriptions of the scene. The description will consist in a set of spatial relations between the objects in the scene. The scene map will contain the position of these objects. For this end we propose a model based on the generation of scalar fields of applicability for each of the available relations. The method can be summarized as follows. In first place a person will come into the room and describe the scene to the robot, including in the description semantic information about the objects which the robot can't get from its sensors. From the description the robot will form the scene mental map. In second place the robot will sense the scene with a 2D range laser building the scene sensed map. The objects positions in the mental map will be used to guide the sensing process. In a third step the robot will fuse the two maps, linking the semantic information about the described objects to the corresponding sensed ones. The resulting map is called the scene enriched map.Peer Reviewe
Deploying and Optimizing Embodied Simulations of Large-Scale Spiking Neural Networks on HPC Infrastructure
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