Intelligent Database Systems Lab Presenter: YAN-SHOU SIE Authors: RAMIN PASHAIE, STUDENT MEMBER, IEEE, AND NABIL H. FARHAT, LIFE FELLOW, IEEE 2009. TNN.

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Presentation transcript:

Intelligent Database Systems Lab Presenter: YAN-SHOU SIE Authors: RAMIN PASHAIE, STUDENT MEMBER, IEEE, AND NABIL H. FARHAT, LIFE FELLOW, IEEE TNN Self-Organization in a Parametrically Coupled Logistic Map Network: A Model for Information Processing in the Visual Cortex

Intelligent Database Systems Lab Outlines Motivation Objectives Methodology Experiments Conclusions Comments

Intelligent Database Systems Lab Motivation In brain anatomy, the cerebral cortex is the outermost layer of the cerebrum and part of brain that is the center of unsupervised learning and the seat of higher level brain functions including perception, cognition, and learning of both static and dynamic sensory information. Such as a visual system of mammals is a unsupervised learning.

Intelligent Database Systems Lab Objectives Study of the dynamics of the cortical patches in this model is the main focus. Propose a new model seeking to emulate the way the visual cortex processes information and interacts with subcortical areas to produce higher level brain functions is described.

Intelligent Database Systems Lab ARCHITECTURE OF THE NETWORK OF PARAMETRICALLY COUPLED COMPLEX PROCESSING ELEMENTS (PCLMN) – Netlets: Computational Units of Cortex Methodology

Intelligent Database Systems Lab DYNAMICS OF THE NETWORK Methodology

Intelligent Database Systems Lab Methodology −Stimulation −Iterations Updates

Intelligent Database Systems Lab – Adaptation Methodology

Intelligent Database Systems Lab Experiments Stimulation of a PCLMN With Face Images

Intelligent Database Systems Lab Experiments Stimulation of a Cortical Patch With Images of Nature

Intelligent Database Systems Lab Experiments

Intelligent Database Systems Lab Experiments

Intelligent Database Systems Lab Experiments

Intelligent Database Systems Lab Conclusions The model processing elements are simple have rich dynamics that includes ﬁxed-point attractors as well as periodic and chaotic behavior. In next step, processing elements were coupled parametrically to form PCLMN. The new model, produces the sparse codes and computational maps considerably faster than other models reported previously. This may also be used for further study of a model of this kind to produce other cortical maps.

Intelligent Database Systems Lab Comments Advantages -Can helpful to research visual system of mammals and unsupervised learning. Applications - unsupervised learning,etc.