Researches On Sparsely Connected Associative Memory And Its Complex Network Realization

Associative Memory modeling human intelligence as information storage and recovery is a hot research issue in neural computing, and receives widely application in the field of artificial intelligence and pattern recognition. Complex network focuses on the relationship between topology and function of the network, and it's a brand-new method of complex system investigation. Associative Memory model is a dynamic nonlinear complex system in essence, and small-word and scale-free characteristics are a universal phenomenon in biological neural system. Therefore, from these points of view, it's a novel and feasible manner to study Associative Memory through complex network ideology.In this dissertation, we utilize complex network ideology to research the influence of the neurons' sparse interconnection style on the network performance, i.e. topology versus function. Starting from network topology, we do detailed theoretical analysis and application instance research on Associative Memory realization.The main research contents and innovative contributions of this dissertation are as follows:(1) Status of the Associative Memory modeling technology is summarized. The problems existed in current models as lacking of biological modeling background and its' unrealistic VLSI implementation are pointed out. The feasibility of researching sparsely connected Associative Memory through complex network is analyzed, and the new ideology of complementary investigation both theoretical and numerical is realized.(2) To go one step closer to more biological realistic model which displays widely sparely connected architecture and meanwhile possesses complex network property, we study a general sparely connected Associative Memory model using probabilistic approach, and explicit analytical solutions for the transient dynamics of the model with arbitrary connectionism are derived.(3) In view of the limited energy consumptions as in the human brain, we derive optimal synaptic dilution strategy under the constraint of limited energy consumptions on fully connected Hopfield network through signal-to-noise analysis. Such synaptic dilution strategy can maintain the network performance utmost while contributing to the energy saving.(4) A novel Associative Memory model based on small-world adaptive structure is proposed in this paper. Aimed at overcoming the disadvantage of random shortcuts formation of the existing methods, this new model takes the ideology of Harmonious Unifying Hybrid Preferential Model and the optimal synaptic dilution strategy under the constraint of limited energy consumptions both into account. This new model breaks the traditional mean of random rewiring but instead constructs a task-based network structure which is much closer to human brain as possessing small-world architecture and can also achieve better performance than the existing counterparts of the same class. The rationality and validity of the proposed model is validated from great number experiments. (5) In order to imitating the scale-free characteristic discovered in the human brain through fMRI. We propose a new Associative Memory model base on dynamic preferential attachment scale-free structure. The conception of affinity between neurons is defined via the optimal synaptic dilution strategy under the constraint of limited energy consumptions. Then the preferential attachment is done by integrate driven of the neuron degree and affinity. The new model not only possesses scale-free architecture but also achieves better performance than the existing counterparts.