Application and optimization of neural field dynamics for driver assistance

Edelbrunner, H.; Handmann, Uwe; Igel, Christian; Leefken, Iris; Seelen, Werner von

doi:10.1109/itsc.2001.948674

Cited by 8 publications

(2 citation statements)

References 7 publications

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“…They are equivalent to continuous recurrent neural networks, in which, neurons are laterally coupled through an interaction kernel and receive external inputs. This concept has also shown great robustness in mobile robot navigation [35][36][37][38][39][40].…”

Section: Behavior Generationmentioning

confidence: 99%

A neural framework for adaptive robot control

Oubbati

Palm

2009

Neural Comput & Applic

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This paper investigates how dynamics in recurrent neural networks can be used to solve some specific mobile robot problems such as motion control and behavior generation. We have designed an adaptive motion control approach based on a novel recurrent neural network, called Echo state networks. The advantage is that no knowledge about the dynamic model is required, and no synaptic weight changing is needed in presence of time varying parameters in the robot. To generate the robot behavior over time, we adopted a biologically inspired approach called neural fields. Due to its dynamical properties, a neural field produces only one localized peak that indicates the optimum movement direction, which navigates a mobile robot to its goal in an unknown environment without any collisions with static or moving obstacles.

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Section: Behavior Generationmentioning

confidence: 99%

A neural framework for adaptive robot control

Oubbati

Palm

2009

Neural Comput & Applic

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“…Dynamic neural fields have been applied to a wide variety of tasks and brain mechanisms: memory [Johnson et al 2009;Vitay et al 2005], visual perception [Wilimzig et al 2006;Zibner et al 2010], visual attention [Vitay et al 2005;Fix et al 2011;Vazquez et al 2011;, video tracking ], classification of motion patterns [Cerda and Girau 2013], object recognition [Faubel and Schöner 2008], multimodal perception ], driving assistance [Edelbrunner et al 2001], sensorimotor control [Erlhagen and Schner 2002], cognitive robotics [Erlhagen and Bicho 2006;Bicho et al 2010], and so on. Several applications require embedded real-time processing; the parallel structure and regularity of DNF is often mentioned to anticipate efficient hardware implementations.…”

Section: Introductionmentioning

confidence: 99%

Randomly Spiking Dynamic Neural Fields

Vangel

Torres-Huitzil

Girau

2015

J. Emerg. Technol. Comput. Syst.

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Bio-inspired neural computation attracts a lot of attention as a possible solution for the future challenges in designing computational resources. Dynamic neural fields (DNF) provide cortically inspired models of neural populations to which computation can be applied for a wide variety of tasks, such as perception and sensorimotor control. DNFs are often derived from continuous neural field theory (CNFT). In spite of the parallel structure and regularity of CNFT models, few studies of hardware implementations have been carried out targeting embedded real-time processing. In this article, a hardware-friendly model adapted from the CNFT is introduced, namely the RSDNF model (randomly spiking dynamic neural fields). Thanks to their simplified 2D structure, RSDNFs achieve scalable parallel implementations on digital hardware while maintaining the behavioral properties of CNFT models. Spike-based computations within neurons in the field are introduced to reduce interneuron connection bandwidth. Additionally, local stochastic spike propagation ensures inhibition and excitation broadcast without a fully connected network. The behavioral soundness and robustness of the model in the presence of noise and distracters is fully validated through software and hardware. A field programmable gate array (FPGA) implementation shows how the RSDNF model ensures a level of density and scalability out of reach for previous hardware implementations of dynamic neural field models.

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