Optimisation of Structures for Earthquake Loads by a Self-Organizing Neural System

2009 ◽  
Author(s):  
E. Salajegheh ◽  
S. Gholizadeh
Keyword(s):  
Neural System ◽  
Earthquake Loads ◽  
Self Organizing

scholarly journals Brain-Inspired Self-Organization with Cellular Neuromorphic Computing for Multimodal Unsupervised Learning

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1605 ◽  
Author(s):  
Lyes Khacef ◽  
Laurent Rodriguez ◽  
Benoît Miramond
Keyword(s):  
Unsupervised Learning ◽  
Cortical Plasticity ◽  
Internal Representation ◽  
Neural System ◽  
Self Organization ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Dynamic Vision ◽  
Neuromorphic Architecture ◽  
Self Organizing

Cortical plasticity is one of the main features that enable our ability to learn and adapt in our environment. Indeed, the cerebral cortex self-organizes itself through structural and synaptic plasticity mechanisms that are very likely at the basis of an extremely interesting characteristic of the human brain development: the multimodal association. In spite of the diversity of the sensory modalities, like sight, sound and touch, the brain arrives at the same concepts (convergence). Moreover, biological observations show that one modality can activate the internal representation of another modality when both are correlated (divergence). In this work, we propose the Reentrant Self-Organizing Map (ReSOM), a brain-inspired neural system based on the reentry theory using Self-Organizing Maps and Hebbian-like learning. We propose and compare different computational methods for unsupervised learning and inference, then quantify the gain of the ReSOM in a multimodal classification task. The divergence mechanism is used to label one modality based on the other, while the convergence mechanism is used to improve the overall accuracy of the system. We perform our experiments on a constructed written/spoken digits database and a Dynamic Vision Sensor (DVS)/EletroMyoGraphy (EMG) hand gestures database. The proposed model is implemented on a cellular neuromorphic architecture that enables distributed computing with local connectivity. We show the gain of the so-called hardware plasticity induced by the ReSOM, where the system’s topology is not fixed by the user but learned along the system’s experience through self-organization.

scholarly journals Model of Visual Neural System with Self-Organizing Cells

1984 ◽  
Vol 20 (1) ◽  
pp. 56-63 ◽  
Author(s):  
Takashi OMORI ◽  
Kaoru NAKANO
Keyword(s):  
Neural System ◽  
Self Organizing

scholarly journals A self-organizing neural system for learning to recognize textured scenes

1999 ◽  
Vol 39 (7) ◽  
pp. 1385-1406 ◽  
Author(s):  
Stephen Grossberg ◽  
James R. Williamson
Keyword(s):  
Neural System ◽  
Self Organizing

Identification of complex shapes using a self organizing neural system

2000 ◽  
Vol 11 (4) ◽  
pp. 921-934 ◽  
Author(s):  
T. Sabisch ◽  
A. Ferguson ◽  
H. Bolouri
Keyword(s):  
Neural System ◽  
Complex Shapes ◽  
Self Organizing

Predicting spatial activity patterns in a neural map from a probabilistic atlas of 3-D reconstructed neurons

1995 ◽  
Vol 53 ◽  
pp. 812-813
Author(s):  
G. Jacobs ◽  
F. Theunissen
Keyword(s):  
Activity Patterns ◽  
Three Dimensional ◽  
Sensory System ◽  
Neural System ◽  
Probabilistic Atlas ◽  
Uniform Sample ◽  
Dimensional Reconstruction ◽  
The Time Domain ◽  
And Function ◽  
Visualization Techniques

In order to understand how the algorithms underlying neural computation are implemented within any neural system, it is necessary to understand details of the anatomy, physiology and global organization of the neurons from which the system is constructed. Information is represented in neural systems by patterns of activity that vary in both their spatial extent and in the time domain. One of the great challenges to microscopists is to devise methods for imaging these patterns of activity and to correlate them with the underlying neuroanatomy and physiology. We have addressed this problem by using a combination of three dimensional reconstruction techniques, quantitative analysis and computer visualization techniques to build a probabilistic atlas of a neural map in an insect sensory system. The principal goal of this study was to derive a quantitative representation of the map, based on a uniform sample of afferents that was of sufficient size to allow statistically meaningful analyses of the relationships between structure and function.

Review of Pattern Recognition by Self-Organizing Neural Networks.

1995 ◽  
Vol 40 (11) ◽  
pp. 1110-1110
Author(s):  
Stephen James Thomas
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Self Organizing
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