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Semantic neural network (SNN) is based on John von Neumann's neural network In contrast to the von Neumann network there are no limitations for topology of neurons for semantic networks. It leads to the impossibility of relative addressing of neurons as it was done by von Neumann. In this case an absolute readdressing should be used. Every neuron should have a unique identifier that would provide a direct access to another neuron. Of course, neurons interacting by axons-dendrites should have each other's identifiers. An absolute readdressing can be modulated by using neuron specificity as it was realized for biological neural networks. There’s no description for self-reflectiveness and self-modification abilities into the initial description of semantic networks (Z.V., Shuklin D.E., 2000 ). But in (D.E. 2004 ) a conclusion had been drawn about the necessity of introspection and self-modification abilities in the system. For maintenance of these abilities a concept of pointer to neuron is provided. Pointers represent virtual connections between neurons. In this model, bodies and signals transferring through the neurons connections represent a physical body, and virtual connections between neurons are representing an astral body. It is proposed to create models of artificial neuron networks on the basis of virtual machine supporting the opportunity for paranormal effects. SNN is generally used for natural language processing. ==Related models== * Computational creativity〔Marupaka, Nagendra, and Ali A. Minai. "Connectivity and creativity in semantic neural networks." Neural Networks (IJCNN), The 2011 International Joint Conference on. IEEE, 2011.〕 * Semantic hashing 〔Salakhutdinov, Ruslan, and Geoffrey Hinton. "Semantic hashing." RBM 500.3 (2007): 500.〕 * Semantic Pointer Architecture〔Eliasmith, Chris, et al. "A large-scale model of the functioning brain." science 338.6111 (2012): 1202-1205.〕 *Sparse distributed memory 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Semantic neural network」の詳細全文を読む スポンサード リンク
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