Show Reference: "Integrating robotics and neuroscience: brains for robots, bodies for brains"

Integrating robotics and neuroscience: brains for robots, bodies for brains Advanced Robotics, Vol. 21, No. 10. (1 January 2007), pp. 1115-1129, doi:10.1163/156855307781389428 by Michele Rucci, Daniel Bullock, Fabrizio Santini
    abstract = {Researchers in robotics and artificial intelligence have often looked at biology as a source of inspiration for solving their problems. From the opposite perspective, neuroscientists have recently turned their attention to the use of robotic systems as a way to quantitatively test and analyze theories that would otherwise remain at a speculative stage. Computational models of neurons and networks of neurons are often activated with simplified artificial patterns that bear little resemblance to natural stimuli. The use of robotic systems has the advantage of introducing phenotypic and environmental constraints similar to those that brains of animals have to face during development and in everyday life. Consideration of these constraints is particularly important in light of modern brain theories, which emphasize the importance of closing the perception/action loop between the agent and the environment. To provide concrete examples of the use of robotic systems in neuroscience, this paper reviews our work in the areas of sensory perception and motor learning. The interdisciplinary approach followed by this research establishes a direct link between natural sciences and engineering. This research can lead to the understanding of basic biological problems while producing robust and flexible systems that operate in the real world.},
    author = {Rucci, Michele and Bullock, Daniel and Santini, Fabrizio},
    day = {1},
    doi = {10.1163/156855307781389428},
    issn = {0169-1864},
    journal = {Advanced Robotics},
    keywords = {neororobotics, philosophical, robotics},
    month = jan,
    number = {10},
    pages = {1115--1129},
    posted-at = {2012-11-08 17:12:40},
    priority = {2},
    publisher = {Taylor \& Francis},
    title = {Integrating robotics and neuroscience: brains for robots, bodies for brains},
    url = {},
    volume = {21},
    year = {2007}

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Through simulations of neurons (and neuron ensembles), numbers of neurons can be monitored over time scales which both are not possible in vivo.

According to Rucci et al., neuroscientists can use robots to quantitatively test and analyze their theories.

The degree to which neuroscientists can draw conclusions from computational models depends on biological accuracy.

If input to biologically plausible models is too dissimilar to natural input, then that can lead to non-natural behavior of the model.

Sensory noise in robotic experiments validates a model's robustness. It is always realistic (but not necessarily natural).

Because of the distance between the focal point of the lens and the point of rotation of the biological eye, depth information can be inferred from shifts of objects' projections on the retina during eye movements.

Rucci et al. model multi-sensory integration in the barn owl OT using leaky integrator firing-rate neurons and reinforcement learning.

Rucci et al. test their model of multi-sensory integration in the barn owl OT in a robot.

Rucci et al. suggest that high saliency in the center of the visual field can act as a reward signal for pre-saccadic neural activation.