Many current neurophysiological, psychophysical, and psychological approaches to vision rest on the idea that when we see, the brain produces an internal representation of the world. The activation of this internal representation is assumed to give rise to the experience of seeing. The problem with this kind of approach is that it leaves unexplained how the existence of such a detailed internal representation might produce visual consciousness. An alternative proposal is made here. We propose that seeing is a way of acting. It is a particular way of exploring the environment. Activity in internal representations does not generate the experience of seeing. The outside world serves as its own, external, representation. The experience of seeing occurs when the organism masters what we call the governing laws of sensorimotor contingency. The advantage of this approach is that it provides a natural and principled way of accounting for visual consciousness, and for the differences in the perceived quality of sensory experience in the different sensory modalities. Several lines of empirical evidence are brought forward in support of the theory, in particular: evidence from experiments in sensorimotor adaptation, visual \"filling in,\" visual stability despite eye movements, change blindness, sensory substitution, and color perception.

A sensorimotor account of vision and visual consciousness-Authors' Response-Acting out our sensory experience

Supersizing the Mind: Embodiment, Action and Cognitive Extension.

High-gauge-factor stretchable strain sensors are developed by utilizing a new strategy of thickness-gradient films with high durability, and high uniaxial/isotropic stretchability based on the self-pinning effect of SWCNTs. The monitoring of detailed damping vibration modes driven by weak sound based on such sensors is demonstrated, making a solid step toward real applications.

Thickness-Gradient Films for High Gauge Factor Stretchable Strain Sensors.

Robot manipulator control using neural networks: A survey

The NEURON Book: Epilogue

This paper presents iSpike: a C++ library that interfaces between spiking neural network simulators and the iCub humanoid robot. It uses a biologically inspired approach to convert the robot's sensory information into spikes that are passed to the neural network simulator, and it decodes output spikes from the network into motor signals that are sent to control the robot. Applications of iSpike range from embodied models of the brain to the development of intelligent robots using biologically inspired spiking neural networks. iSpike is an open source library that is available for free download under the terms of the GPL.

/pdf/ispike-a-spiking-neural-interface-for-the-icub-robot-3vfd87y4ia.pdf

iSpike: a spiking neural interface for the iCub robot.

This paper describes the NeuroBot system, which uses a global workspace architecture, implemented in spiking neurons, to control an avatar within the Unreal Tournament 2004 (UT2004) computer game This system is designed to display humanlike behavior within UT2004, which provides a good environment for comparing human and embodied AI behavior without the cost and difficulty of full humanoid robots Using a biologically inspired approach, the architecture is loosely based on theories about the high-level control circuits in the brain, and it is the first neural implementation of a global workspace that has been embodied in a complex dynamic real-time environment NeuroBot's humanlike behavior was tested by competing in the 2011 BotPrize competition, in which human judges play UT2004 and rate the humanness of other avatars that are controlled by a human or a bot NeuroBot came a close second, achieving a humanness rating of 36%, while the most human human reached 67% We also developed a humanness metric that combines a number of statistical measures of an avatar's behavior into a single number In our experiments with this metric, NeuroBot was rated as 33% human, and the most human human achieved 73%

A Neurally Controlled Computer Game Avatar With Humanlike Behavior

http://www.davidgamez.eu/papers/Gamez10_InformationBasedPredictionsNeuralNetwork_PrePub.pdf

Information integration based predictions about the conscious states of a spiking neural network.

http://www.davidgamez.eu/papers/GamezAleksander11_AccuracyPerformancePhiLiveliness_PrePub.pdf

Accuracy and performance of the state-based Φ and liveliness measures of information integration

This paper describes a system that uses a global workspace architecture implemented in spiking neurons to control an avatar within the Unreal Tournament 2004 (UT2004) computer game. This system is designed to display human-like behaviour within UT2004, which provides a good environment for comparing human and embodied AI behaviour without the cost and difficulty of full humanoid robots. Using a biologically-inspired approach, the architecture is loosely based on theories about the high level control circuits in the brain, and it is the first neural implementation of a global workspace that is embodied in a dynamic real time environment. At its current stage of development the system can navigate through UT2004 and shoot opponents. We are currently completing the implementation and testing in preparation for the human-like bot competition at CIG 2011 in September.

/pdf/a-neuronal-global-workspace-for-human-like-control-of-a-4jmg1x3owx.pdf

David Gamez

Papers

iSpike: a spiking neural interface for the iCub robot.

A Neurally Controlled Computer Game Avatar With Humanlike Behavior

Information integration based predictions about the conscious states of a spiking neural network.

Accuracy and performance of the state-based Φ and liveliness measures of information integration

A neuronal global workspace for human-like control of a computer game character