Attractors Neural Networks
Mostrando 1-6 de 6 artigos, teses e dissertações.
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1. Identificação remota de sistemas operacionais utilizando análise de processos aleatórios e redes neurais artificiais
A new method to perform TCP/IP fingerprinting is proposed. TCP/IP fingerprinting is the process of identify a remote machine through a TCP/IP based computer network. This method has many applications related to network security. Both intrusion and defence procedures may use this process to achieve their objectives. There are many known methods that perform t
Publicado em: 2009
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2. Aspectos de mapas caóticos acoplados para processamento de informações / Aspects of chaotic coupled maps for information processing
A globally coupled map (GCM) model is a network of interconnected chaotic elements. In this work we investigate models based in the GCM and in the Hopfield network. Through modifications, like changing the local dynamics of the GCM (S-GCM) and adding self-feedback to the processing element of the Hopfield network, it s possible to use the models as an assoc�
Publicado em: 2002
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3. Recovery of memory properties of Neural Networks in attractors. / Propriedades de recuperação de memória em redes neurais atratoras.
Attractor neural networks are feedback neural networks with no pre-defined connection structure. These types of neural networks present a rich dissipative dynamics and, in general, are used as associative memory devices. Such devices have the capacity to retrieve a previously stored memory, even when exposed to partial or degraded information. To store a mem
Publicado em: 1997
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4. Stochastic gene expression as a many-body problem
Gene expression has a stochastic component because of the single-molecule nature of the gene and the small number of copies of individual DNA-binding proteins in the cell. We show how the statistics of such systems can be mapped onto quantum many-body problems. The dynamics of a single gene switch resembles the spin-boson model of a two-site polaron or an el
The National Academy of Sciences.
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5. Neural networks counting chimes.
It is shown that the ideas that led to neural networks capable of recalling associatively and asynchronously temporal sequences of patterns can be extended to produce a neural network that automatically counts the cardinal number in a sequence of identical external stimuli. The network is explicitly constructed, analyzed, and simulated. Such a network may ac
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6. Syntactic sequencing in Hebbian cell assemblies
Hebbian cell assemblies provide a theoretical framework for the modeling of cognitive processes that grounds them in the underlying physiological neural circuits. Recently we have presented an extension of cell assemblies by operational components which allows to model aspects of language, rules, and complex behaviour. In the present work we study the genera
Springer Netherlands.