Retina Color-Opponency Based Pursuit Implemented Through Spiking Neural Networks in the Neurorobotics Platform

Ambrosano, Alessandro; Vannucci, Lorenzo; Albanese, Ugo; Kirtay, Murat; Falotico, Egidio; Martínez‐Cañada, Pablo; Hinkel, Georg; Kaiser, Jacques; Ulbrich, Stefan; Levi, Paul; Morillas, Christian A.; Knoll, Alois; Gewaltig, Marc‐Oliver; Laschi, Cecilia

doi:10.1007/978-3-319-42417-0_2

Cited by 8 publications

(8 citation statements)

References 21 publications

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“…Then, we describe how the models interact with each other. The visual system is composed of the segmentation model of Francis et al (2017), a retina model inspired by Ambrosano et al (2016), and a saliency model, which is a simplified version of the model introduced by Kroner et al (2019). These specific parts of human vision were chosen because the segmentation model already explains many features of visual crowding (Figure 4) and because retinal processing, as well as saliency computation, are potential sources of anisotropy for the segmentation output.…”

Section: Methodsmentioning

confidence: 99%

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Running Large-Scale Simulations on the Neurorobotics Platform to Understand Vision – The Case of Visual Crowding

et al. 2019

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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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Section: Methodsmentioning

confidence: 99%

“…Previous work has integrated a retina model as part of a neurorobotic experiment in the NRP (Ambrosano et al, 2016) by using the COREM (Computational Retina Modeling framework; Martínez-Cañada et al, 2015, 2016). COREM is a set of building blocks that are often used to describe the behavior of the retina at different levels of detail.…”

Section: Methodsmentioning

confidence: 99%

Running Large-Scale Simulations on the Neurorobotics Platform to Understand Vision – The Case of Visual Crowding

et al. 2019

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“…Initially, solving simulated control tasks was done by manually setting network weights, e.g., in Lewis et al ( 2000 ) and Ambrosano et al ( 2016 ). However, this approach is limited to solving simple behavioral tasks such as wall following (Wang et al, 2009 ) or lane following (Kaiser et al, 2016 ), it is usually only feasible for very small network architectures with few weights.…”

Section: Learning and Robotics Applicationsmentioning

confidence: 99%

A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks

et al. 2018

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Biological intelligence processes information using impulses or spikes, which makes those living creatures able to perceive and act in the real world exceptionally well and outperform state-of-the-art robots in almost every aspect of life. To make up the deficit, emerging hardware technologies and software knowledge in the fields of neuroscience, electronics, and computer science have made it possible to design biologically realistic robots controlled by spiking neural networks (SNNs), inspired by the mechanism of brains. However, a comprehensive review on controlling robots based on SNNs is still missing. In this paper, we survey the developments of the past decade in the field of spiking neural networks for control tasks, with particular focus on the fast emerging robotics-related applications. We first highlight the primary impetuses of SNN-based robotics tasks in terms of speed, energy efficiency, and computation capabilities. We then classify those SNN-based robotic applications according to different learning rules and explicate those learning rules with their corresponding robotic applications. We also briefly present some existing platforms that offer an interaction between SNNs and robotics simulations for exploration and exploitation. Finally, we conclude our survey with a forecast of future challenges and some associated potential research topics in terms of controlling robots based on SNNs.

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“…As a first step in this direction, a model of the retina was included in the NRP as a robot to neuron transfer function (Ambrosano et al, 2016). …”

Section: Use Cases For the Neurorobotics Platformmentioning

confidence: 99%

Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform

et al. 2017

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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).1 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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Retina Color-Opponency Based Pursuit Implemented Through Spiking Neural Networks in the Neurorobotics Platform

Cited by 8 publications

References 21 publications

Running Large-Scale Simulations on the Neurorobotics Platform to Understand Vision – The Case of Visual Crowding

Running Large-Scale Simulations on the Neurorobotics Platform to Understand Vision – The Case of Visual Crowding

A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks

Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform

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