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Bornet A, Kaiser J, Kroner A, Falotico E, Ambrosano A, Cantero K, Herzog MH, Francis G. Running Large-Scale Simulations on the Neurorobotics Platform to Understand Vision - The Case of Visual Crowding. Front Neurorobot 2019; 13:33. [PMID: 31191291 PMCID: PMC6549494 DOI: 10.3389/fnbot.2019.00033] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Accepted: 05/14/2019] [Indexed: 11/13/2022] Open
Abstract
Traditionally, human vision research has focused on specific paradigms and proposed models to explain very specific properties of visual perception. However, the complexity and scope of modern psychophysical paradigms undermine the success of this approach. For example, perception of an element strongly deteriorates when neighboring elements are presented in addition (visual crowding). As it was shown recently, the magnitude of deterioration depends not only on the directly neighboring elements but on almost all elements and their specific configuration. Hence, to fully explain human visual perception, one needs to take large parts of the visual field into account and combine all the aspects of vision that become relevant at such scale. These efforts require sophisticated and collaborative modeling. The Neurorobotics Platform (NRP) of the Human Brain Project offers a unique opportunity to connect models of all sorts of visual functions, even those developed by different research groups, into a coherently functioning system. Here, we describe how we used the NRP to connect and simulate a segmentation model, a retina model, and a saliency model to explain complex results about visual perception. The combination of models highlights the versatility of the NRP and provides novel explanations for inward-outward anisotropy in visual crowding.
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Affiliation(s)
- Alban Bornet
- Laboratory of Psychophysics, Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jacques Kaiser
- FZI Research Center for Information Technology, Karlsruhe, Germany
| | - Alexander Kroner
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Egidio Falotico
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy
| | | | | | - Michael H. Herzog
- Laboratory of Psychophysics, Brain Mind Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Gregory Francis
- Department of Psychological Sciences, Purdue University, West Lafayette, IN, United States
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Falotico E, Vannucci L, Ambrosano A, Albanese U, Ulbrich S, Vasquez Tieck JC, Hinkel G, Kaiser J, Peric I, Denninger O, Cauli N, Kirtay M, Roennau A, Klinker G, Von Arnim A, Guyot L, Peppicelli D, Martínez-Cañada P, Ros E, Maier P, Weber S, Huber M, Plecher D, Röhrbein F, Deser S, Roitberg A, van der Smagt P, Dillman R, Levi P, Laschi C, Knoll AC, Gewaltig MO. Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform. Front Neurorobot 2017; 11:2. [PMID: 28179882 PMCID: PMC5263131 DOI: 10.3389/fnbot.2017.00002] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2016] [Accepted: 01/04/2017] [Indexed: 11/13/2022] Open
Abstract
Combined efforts in the fields of neuroscience, computer science, and biology allowed to design biologically realistic models of the brain based on spiking neural networks. For a proper validation of these models, an embodiment in a dynamic and rich sensory environment, where the model is exposed to a realistic sensory-motor task, is needed. Due to the complexity of these brain models that, at the current stage, cannot deal with real-time constraints, it is not possible to embed them into a real-world task. Rather, the embodiment has to be simulated as well. While adequate tools exist to simulate either complex neural networks or robots and their environments, there is so far no tool that allows to easily establish a communication between brain and body models. The Neurorobotics Platform is a new web-based environment that aims to fill this gap by offering scientists and technology developers a software infrastructure allowing them to connect brain models to detailed simulations of robot bodies and environments and to use the resulting neurorobotic systems for in silico experimentation. In order to simplify the workflow and reduce the level of the required programming skills, the platform provides editors for the specification of experimental sequences and conditions, environments, robots, and brain-body connectors. In addition to that, a variety of existing robots and environments are provided. This work presents the architecture of the first release of the Neurorobotics Platform developed in subproject 10 "Neurorobotics" of the Human Brain Project (HBP). At the current state, the Neurorobotics Platform allows researchers to design and run basic experiments in neurorobotics using simulated robots and simulated environments linked to simplified versions of brain models. We illustrate the capabilities of the platform with three example experiments: a Braitenberg task implemented on a mobile robot, a sensory-motor learning task based on a robotic controller, and a visual tracking embedding a retina model on the iCub humanoid robot. These use-cases allow to assess the applicability of the Neurorobotics Platform for robotic tasks as well as in neuroscientific experiments.
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Affiliation(s)
- Egidio Falotico
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy
| | - Lorenzo Vannucci
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy
| | | | - Ugo Albanese
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy
| | - Stefan Ulbrich
- Department of Intelligent Systems and Production Engineering (ISPE – IDS/TKS), FZI Research Center for Information Technology, Karlsruhe, Germany
| | - Juan Camilo Vasquez Tieck
- Department of Intelligent Systems and Production Engineering (ISPE – IDS/TKS), FZI Research Center for Information Technology, Karlsruhe, Germany
| | - Georg Hinkel
- Department of Software Engineering (SE), FZI Research Center for Information Technology, Karlsruhe, Germany
| | - Jacques Kaiser
- Department of Intelligent Systems and Production Engineering (ISPE – IDS/TKS), FZI Research Center for Information Technology, Karlsruhe, Germany
| | - Igor Peric
- Department of Intelligent Systems and Production Engineering (ISPE – IDS/TKS), FZI Research Center for Information Technology, Karlsruhe, Germany
| | - Oliver Denninger
- Department of Software Engineering (SE), FZI Research Center for Information Technology, Karlsruhe, Germany
| | - Nino Cauli
- Computer and Robot Vision Laboratory, Instituto de Sistemas e Robotica, Instituto Superior Tecnico, Lisbon, Portugal
| | - Murat Kirtay
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy
| | - Arne Roennau
- Department of Intelligent Systems and Production Engineering (ISPE – IDS/TKS), FZI Research Center for Information Technology, Karlsruhe, Germany
| | - Gudrun Klinker
- Department of Informatics, Technical University of Munich, Garching, Germany
| | | | - Luc Guyot
- Blue Brain Project (BBP), École polytechnique fédérale de Lausanne (EPFL), Genève, Switzerland
| | - Daniel Peppicelli
- Blue Brain Project (BBP), École polytechnique fédérale de Lausanne (EPFL), Genève, Switzerland
| | - Pablo Martínez-Cañada
- Department of Computer Architecture and Technology, CITIC, University of Granada, Granada, Spain
| | - Eduardo Ros
- Department of Computer Architecture and Technology, CITIC, University of Granada, Granada, Spain
| | - Patrick Maier
- Department of Informatics, Technical University of Munich, Garching, Germany
| | - Sandro Weber
- Department of Informatics, Technical University of Munich, Garching, Germany
| | - Manuel Huber
- Department of Informatics, Technical University of Munich, Garching, Germany
| | - David Plecher
- Department of Informatics, Technical University of Munich, Garching, Germany
| | - Florian Röhrbein
- Department of Informatics, Technical University of Munich, Garching, Germany
| | - Stefan Deser
- Department of Informatics, Technical University of Munich, Garching, Germany
| | - Alina Roitberg
- Department of Informatics, Technical University of Munich, Garching, Germany
| | | | - Rüdiger Dillman
- Department of Intelligent Systems and Production Engineering (ISPE – IDS/TKS), FZI Research Center for Information Technology, Karlsruhe, Germany
| | - Paul Levi
- Department of Intelligent Systems and Production Engineering (ISPE – IDS/TKS), FZI Research Center for Information Technology, Karlsruhe, Germany
| | - Cecilia Laschi
- The BioRobotics Institute, Scuola Superiore Sant’Anna, Pontedera, Italy
| | - Alois C. Knoll
- Department of Informatics, Technical University of Munich, Garching, Germany
| | - Marc-Oliver Gewaltig
- Blue Brain Project (BBP), École polytechnique fédérale de Lausanne (EPFL), Genève, Switzerland
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Martínez-Cañada P, Morillas C, Pino B, Ros E, Pelayo F. A Computational Framework for Realistic Retina Modeling. Int J Neural Syst 2016; 26:1650030. [DOI: 10.1142/s0129065716500301] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Computational simulations of the retina have led to valuable insights about the biophysics of its neuronal activity and processing principles. A great number of retina models have been proposed to reproduce the behavioral diversity of the different visual processing pathways. While many of these models share common computational stages, previous efforts have been more focused on fitting specific retina functions rather than generalizing them beyond a particular model. Here, we define a set of computational retinal microcircuits that can be used as basic building blocks for the modeling of different retina mechanisms. To validate the hypothesis that similar processing structures may be repeatedly found in different retina functions, we implemented a series of retina models simply by combining these computational retinal microcircuits. Accuracy of the retina models for capturing neural behavior was assessed by fitting published electrophysiological recordings that characterize some of the best-known phenomena observed in the retina: adaptation to the mean light intensity and temporal contrast, and differential motion sensitivity. The retinal microcircuits are part of a new software platform for efficient computational retina modeling from single-cell to large-scale levels. It includes an interface with spiking neural networks that allows simulation of the spiking response of ganglion cells and integration with models of higher visual areas.
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Affiliation(s)
- Pablo Martínez-Cañada
- Department of Computer Architecture and Technology, CITIC-UGR, University of Granada, Spain
| | - Christian Morillas
- Department of Computer Architecture and Technology, CITIC-UGR, University of Granada, Spain
| | - Begoña Pino
- Department of Computer Architecture and Technology, CITIC-UGR, University of Granada, Spain
| | - Eduardo Ros
- Department of Computer Architecture and Technology, CITIC-UGR, University of Granada, Spain
| | - Francisco Pelayo
- Department of Computer Architecture and Technology, CITIC-UGR, University of Granada, Spain
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