Neural networks: the official journal of the International Neural Network SocietyZhang, C., Dangelmayr, G., Oprea, I.: Storing cycles in Hopfield-type networks with pseudo inverse learning rule: admissibility and network topology. Neural Networks 46 , 283–298 (2013) MATH...
Numerous models for memristor-based single neuron and its network were proposed by taking memristor as a synaptic weight between the adjacent neurons [22–26] or characterizing the effects of electromagnetic induction and radiation [27–30], which leads to the emergence of more complex dynamical ...
Abstract :By theoretical analysis and simulation ,the dynamic behavior of a 4-dimensional memristor -based self -synaptic Hopfield Neural Network (MSHNN )and its simulation implementation were studied.Firstly ,the basic conditions for the MSHNN to produce complex dynamic behavior were analyzed.Secondly...
2, the connection topology considering the activation gradients and the mathematical model is described. The dissipative and symmetrical nature of the network are detailed there, the equilibrium points are illustrated graphically, and the determination of the characteristic equation from the Jacobian matrix...
Our main finding is that embedding the Hebbian rule on a hierarchical topology allows the network to accomplish both serial and parallel processing. By tuning the level of fast noise affecting it or triggering the decay of the interactions with the distance among neurons, the system may switch ...
Simple cycles contain rows from one loop only, and the network topology is a feed-forward chain with feedback to one neuron if the loop-vectors in Sigma are cyclic permutations of each other. For special cases this topology simplifies to a ring with only one feedback. Composite cycles ...
It is shown how a new parameter, degree of periodicity, affects the dynamics of the neural network. Keywords: Hopfield-type neural networks; modulo periodic unpredictable synaptic connections; rates and inputs; unpredictable solutions; exponential stability; numerical simulations Citation: Akhmet, M.;...
Interestingly, the Boltzmann machine and the Hopfield network, if considered to be two cognitive processes (learning and information retrieval), are nothing more than two sides of the same coin. In fact, it is possible to exactly map the one into the other. We will inspect such an ...
Interestingly, the Boltzmann machine and the Hopfield network, if considered to be two cognitive processes (learning and information retrieval), are nothing more than two sides of the same coin. In fact, it is possible to exactly map the one into the other. We will inspect such an ...
However, optimal networks topology and implementation technology have not yet been selected (the generalizability of networks is not well understood, and there is a lack of explanation for the relationship between the network topology and performance [19]. Nevertheless, we claim that ANN should ...