Show Reference: "Adaptation of orienting behavior: from the barn owl to a robotic system"

Adaptation of orienting behavior: from the barn owl to a robotic system Robotics and Automation, IEEE Transactions on, Vol. 15, No. 1. (February 1999), pp. 96-110, doi:10.1109/70.744606 by Michele Rucci, Gerald M. Edelman, Jonathan Wray
@article{rucci-et-al-1999,
    abstract = {Autonomous robotic systems need to adjust their sensorimotor coordinations so as to maintain good performance in the presence of changes in their sensory and motor characteristics. Biological systems are able to adapt to large variations in their physical and functional properties. In the last decade, the adjustment of orienting behavior has been carefully investigated in the barn owl, a nocturnal predator with highly developed auditory capabilities. We have previously proposed that the development and maintenance of the barn owl's accurate orienting behavior can be explained through a process of learning based on the saliency of sensorimotor events. In this paper we consider the application of a detailed computer model of the principal neural structures involved in the process of spatial localization in the barn owl to the control of the orienting behavior of a robotic system, in the presence of auditory and visual stimulation. The system is composed of a robotic head equipped with two lateral microphones and a camera. We show that the model produces accurate orienting behavior toward both auditory and visual targets and is able to quickly recover good performance after alterations of the sensory inputs and motor outputs. The results illustrate that an architecture specifically designed to account for biological phenomena can produce flexible and robust motor control of a robotic system operating in the real world},
    author = {Rucci, Michele and Edelman, Gerald M. and Wray, Jonathan},
    citeulike-article-id = {416793},
    citeulike-linkout-0 = {http://dx.doi.org/10.1109/70.744606},
    citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=744606},
    doi = {10.1109/70.744606},
    institution = {Neurosciences Inst., San Diego, CA, USA},
    issn = {1042-296X},
    journal = {Robotics and Automation, IEEE Transactions on},
    keywords = {auditory, biorobotic, localization, model, neurorobotics, sc, visual},
    month = feb,
    number = {1},
    pages = {96--110},
    posted-at = {2014-12-04 15:44:29},
    priority = {2},
    publisher = {IEEE},
    title = {Adaptation of orienting behavior: from the barn owl to a robotic system},
    url = {http://dx.doi.org/10.1109/70.744606},
    volume = {15},
    year = {1999}
}

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Rucci et al. present a robotic system based on their neural model of audiovisual localization.

The model of natural multisensory integration and localization is based on the leaky integrate-and-fire neuron model.

Rucci et al. explain audio-visual map registration and learning of orienting responses to audio-visual stimuli by what they call value-dependent learning: After each motor response, a modulatory system evaluated whether that response was good, bringing the target into the center of the visual field of the system, or bad. The learning rule used by the system was such that it strengthened connections between neurons from the different neural subpopulations of the network if they were highly correlated whenever the modulatory response was strong, and weakened otherwise.

Rucci et al.'s system comprises artificial neural populations modeling MSO (aka. the nucleus laminaris), the central nucleus of the inferior colliculus (ICc), the external nucleus of the inferior colliculus (ICx), the retina, and the superior colliculus (SC, aka. the optic tectum). The population modeling the SC is split into a sensory and a motor subpopulation.

In Rucci et al.'s system, the MSO is modeled by computing Fourier transforms for each of the auditory signals. The activity of the MSO neurons is then determined by their individual preferred frequency and ITD and computed directly from the Fourier-transformed data.

In Rucci et al.'s model, neural weights are updated between neural populations modeling

  • ICC and ICx
  • sensory and motor SC.

Rucci et al. claim a mean localization error of 1.54°±1.01° (± presumably meaning standard error) for auditory localization of white-noise stimuli at a direction between $[-60°,60°]$ from their system.