Spontaneous origin of topological complexity in self-organizing neural networks
نویسندگان
چکیده
منابع مشابه
Spontaneous Origin of Topological Complexity in Self-Organizing Neural Networks
Attention is drawn to the possibility that self-organizing biological neural networks could spontaneously acquire the capability to carry out sophisticated computations. In particular, it is shown that the effective action governing the organization of feature detectors in neural networks which incorporate Kohonen-like self-organization can spontaneously lead to structures that are topologicall...
متن کاملSelf-Organizing Multilayered Neural Networks of Optimal Complexity
The principles of self-organizing the neural networks of optimal complexity is considered under the unrepresentative learning set. The method of self-organizing the multi-layered neural networks is offered and used to train the logical neural networks which were applied to the medical diagnostics.
متن کاملSpontaneous Origin of Topological Complexity in the Cerebral Cortex
Attention is drawn to the possibility of regarding the cerebral cortex as a physical system whose only excitations are topological. An attractive feature of such a hypothesis is that it is possible to understand how local dynamics could spontaneously give rise to a large scale organization of neurons and synapses that one might associate with sophisticated cognitive capabilities. It is suggeste...
متن کاملSelf-organizing Neural Tree Networks
Automatic pattern classification is a very important field of artificial intelligence. For these kind of tasks different techniques have been used. In this work a combination of decision trees and self-organizing neural networks is presented as an alternative to attack the problem. For the construction of these trees growth processes are applied. In these processes, the evaluation of classifi...
متن کاملSelf-organizing Neural Networks in Feature Extraction
Due to large datavolumes when remote sensing or other kind of images are used, there is need for methods to decrease the volume of data. Methods for decreasing the feature dimension, in other words number of channels, are called feature selection and feature extraction. In the feature selection, important channels are selected using some search technique and these channels are used for current ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Network: Computation in Neural Systems
سال: 1997
ISSN: 0954-898X,1361-6536
DOI: 10.1088/0954-898x/8/2/005