From Wheels to Wings with Evolutionary Spiking Circuits

Floreano, Dario; Zufferey, Jean-Christophe; Nicoud, Jean-Daniel

doi:10.1007/978-3-540-24580-3_3

Cited by 14 publications

(13 citation statements)

References 25 publications

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“…SNN-based systems also develop increasingly in the area of robotics, where fast processing is a key issue [104,163,44,43,126,50], from wheels to wings, or legged locomotion. The special abilities of SNNs for fast computing transient temporal patterns make them on first line for designing efficient systems in the area of autonomous robotics.…”

Section: Pattern Recognition With Snnsmentioning

confidence: 99%

Computing with Spiking Neuron Networks

Paugam-Moisy

Bohté

2012

Handbook of Natural Computing

209

114

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Spiking Neuron Networks (SNNs) are often referred to as the 3 rd generation of neural networks. Highly inspired from natural computing in the brain and recent advances in neurosciences, they derive their strength and interest from an accurate modeling of synaptic interactions between neurons, taking into account the time of spike firing. SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. Based on dynamic event-driven processing, they open up new horizons for developing models with an exponential capacity of memorizing and a strong ability to fast adaptation. Today, the main challenge is to discover efficient learning rules that might take advantage of the specific features of SNNs while keeping the nice properties (general-purpose, easy-to-use, available simulators, etc.) of traditional connectionist models. This chapter relates the history of the "spiking neuron" in Section 1 and summarizes the most currently-in-use models of neurons and synaptic plasticity in Section 2. The computational power of SNNs is addressed in Section 3 and the problem of learning in networks of spiking neurons is tackled in Section 4, with insights into the tracks currently explored for solving it. Finally, Section 5 discusses application domains, implementation issues and proposes several simulation frameworks.

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Section: Pattern Recognition With Snnsmentioning

confidence: 99%

Computing with Spiking Neuron Networks

Paugam-Moisy

Bohté

2012

Handbook of Natural Computing

209

114

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“…On the other hand, an object that approaches in non-collision course, or a translating object, would generate response in only a part of them. Simple steering and collision avoidance mechanisms based on fly EMDs have also been investigated in [24] using robot platforms. However system integration of these approaches into real cars is still in its earlier stages.…”

Section: The Underlying Biological Modelsmentioning

confidence: 99%

Insect-vision inspired collision warning vision processor for automobiles

Liñán‐Cembrano

Carranza

Rind

et al. 2008

IEEE Circuits Syst. Mag.

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FeatureDigital Object Identifier 10. 1109 /MCAS.2008 Vision is expected to play important roles for car safety enhancement. Imaging systems can be used to enlarging the vision field of the driver. For instance capturing and displaying views of hidden areas around the car which the driver can analyze for safer decision-making. Vision systems go a step further. They can autonomously analyze the visual information, identify dangerous situations and prompt the delivery of warning signals. For instance in case of road lane departure, if an overtaking car is in the blind spot, if an object is approaching within collision course, etc. Processing capabilities are also needed for applications viewing the car interior such as "intelligent airbag systems" that base deployment decisions on passenger features.On-line processing of visual information for car safety involves multiple sensors and views, huge amount of data per view and large frame rates. The associated computational load may be prohibitive for conventional processing architectures. Dedicated systems with embedded local processing capabilities may be needed to confront the challenges. This paper describes a dedicated sensory-processing architecture for collision warning which is inspired by insect vision. Particularly, the paper relies on the exploitation of the knowledge about the behavior of Locusta Migratoria to develop dedicated chips and systems which are integrated into model cars as well as into a commercial car (Volvo XC90) and tested to deliver collision warnings in real traffic scenarios.Authorized licensed use limited to: Newcastle University. Downloaded on March 16,2010 at 10:30:53 EDT from IEEE Xplore. Restrictions apply.

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“…Many researchers have considered a bio-inspired control system in order to control a robot [5], [6], [7], [8]. Sometimes the animal not only inspires the control strategy for the robot, but also its kinematics and functionalities.…”

Section: The Neural Controller Architecturementioning

confidence: 99%

A BioInspired Neural Controller For a Mobile Robot

Folgheraiter

Gini

Nava

et al. 2006

2006 IEEE International Conference on Robotics and Biomimetics

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Abstract-This paper focuses on the study of a bio-inspired neural controller used to govern a mobile robot. The network's architecture is based on the understanding that neurophysiologists have obtained on the nervous system of some simple animals, like arthropods or invertebrates. The neuronal model mimics the behavior of the natural cells present in the animal, and elaborates the continuous signals coming from the robot's sensors. The output generated by the controller, after scaling, commands the wheel rotation and therefore the robot's linear and angular velocity. The mobile robot, thanks to the controller, presents different behaviors, like reaching a sonorous source, avoiding obstacles and finding the recharge stations. In the network architecture different modules, charged of different functionality, are regulated and coordinated using an inhibition mechanism. In order to test the control strategy and the neural architecture, we implemented the system in Matlab and finally in hardware using a dedicated dual processor board equipped with an ARM7TDMI micro-controller. Results show that the neural controller can govern the robot efficiently with performances comparable with those described about the animal.

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From Wheels to Wings with Evolutionary Spiking Circuits

Cited by 14 publications

References 25 publications

Computing with Spiking Neuron Networks

Computing with Spiking Neuron Networks

Insect-vision inspired collision warning vision processor for automobiles

A BioInspired Neural Controller For a Mobile Robot

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