Cycles: A Simulation Tool for Studying Cyclic Neural Networks

Storing cycles in Hopfield-type networks with pseudoinverse learning rule: Admissibility and network topology

Cyclic patterns of neuronal activity are ubiquitous in animal nervous systems, and partially responsible for generating and controlling rhythmic movements such as locomotion, respiration, swallowing and so on. Clarifying the role of the network connectivities for generating cyclic patterns is fundamental for understanding the generation of rhythmic movements. In this paper, the storage of binar...

A Strain Range Dependent Cyclic Plasticity Model

Hysteresis loop curves are highly important for numerical simulations of materials deformation under cyclic loadings. The models mainly take account of only the tensile half of the stabilized cycle in hysteresis loop for identification of the constants which don’t vary with accumulation of plastic strain and strain range of the hysteresis loop. This approach may be quite erroneous particularly ...

Influence of Nonlinearity on the Limit-cycles of Neural Networks with Asymmetrical Cyclic Connections

We have analysed the effect of nonlinearity of an activation function on the oscillation of the neural network with asymmetrical cyclic connections by using the averaging method. The oscillation obtained by linear approximation, in which the activation function is approximated by a linear function, is multi-modal although the actual oscillation with numerical simulation is unimodal. When the ac...

Cooperative Co-Evolving Neural Networks for Robosoccer Simulation

Recursive Neural Networks and Graphs: Dealing with Cycles

Recursive neural networks are a powerful tool for processing structured data. According to the recursive learning paradigm, the input information consists of directed positional acyclic graphs (DPAGs). In fact, recursive networks are fed following the partial order defined by the links of the graph. Unfortunately, the hypothesis of processing DPAGs is sometimes too restrictive, being the nature...