Charge symmetry, quarks and mesons

Numerical investigation of a single layer recurrent neural network model in which the synaptic connection matrix is formed by summing direct products of succesive patterns in cyclic sequeences shows that chaotic wandering dynamics can occur with the reduction of a connectivity parameter. The structure in these dynamics is discussed from the viewpoint of search among stored memory patterns

Chaotic wandering and search in a cycle-memory neural network

Pion absorption in light nuclei

A comparative study of the NNπ system: (I). The πd → πd, πd → NN channels

A chaos neural network model is studied towards an application to the associative memories. In this work the dynamics of the present neural network model is investigated to present how the chaotic behaviour can be controlled in terms of the target information. It is found that the present model may be externally controlled to close to a target pattern through a chaotic process. In practice the evidence of the chaotic behaviour in the present model is directly confirmed on the basis of the Lyapunov exponents.

On the Associative Model with Parameter Controlled Chaos Neurons

Search for the tetraneutron by the double-charge-exchange of negative pions

The relative cross sections for the $2N+\ensuremath{\pi}\ensuremath{\rightarrow}2N$ reactions were determined where the initial two nucleons are in a relative $s$ state, and coupled either to isospin zero or to one. This information is extracted from two-nucleon coincidence measurements of the (${\ensuremath{\pi}}^{+}$, $2p$) and the (${\ensuremath{\pi}}^{\ensuremath{-}}$, $\mathrm{pn}$) reactions on targets of $^{3}\mathrm{He}$ and $^{4}\mathrm{He}$.

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J.E. Ungar

Papers

Search for the tetraneutron by the double-charge-exchange of negative pions

Isospin Dependence of Pion Absorption on a Pair of Nucleons