Decision Making Logic of Visual Brain
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
The Neurophysiological Bases of Cognitive Computation Using Rough Set Theory
Transactions on Rough Sets IX
Rough Set Theory of Shape Perception
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Rough set theory of pattern classification in the brain
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Interactions between rough parts in object perception
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Brain-like approximate reasoning
VoCS'08 Proceedings of the 2008 international conference on Visions of Computer Science: BCS International Academic Conference
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There exists a common view that the brain acts like a Turing machine: The machine reads information from an infinite tape (sensory data) and, on the basis of the machine's state and information from the tape, an action (decision) is made. The main problem with this model lies in how to synchronize a large number of tapes in an adaptive way so that the machine is able to accomplish tasks such as object classification. We propose that such mechanisms exist already in the eye. A popular view is that the retina, typically associated with high gain and adaptation for light processing, is actually performing local preprocessing by means of its center-surround receptive field. We would like to show another property of the retina: The ability to integrate many independent processes. We believe that this integration is implemented by synchronization of neuronal oscillations. In this paper, we present a model of the retina consisting of a series of coupled oscillators which can synchronize on several scales. Synchronization is an analog process which is converted into a digital spike train in the output of the retina. We have developed a hardware implementation of this model, which enables us to carry out rapid simulation of multineuron oscillatory dynamics. We show that the properties of the spike trains in our model are similar to those found in vivo in the cat retina