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Attractor models are simplified models used to describe the dynamics of firing rate profiles of a pool of neurons. The firing rate profile, or the neuronal activity, is thought to carry information. Continuous attractor neural networks (CANNs) describe the neural processing of continuous information such as object position, object orientation, and direction of object motion. Recently it was found that in one-dimensional CANNs, short-term synaptic depression can destabilize bump-shaped neuronal attractor activity profiles. In this article, we study two-dimensional CANNs with short-term synaptic depression and spike frequency adaptation. We found that the dynamics of CANNs with short-term synaptic depression and CANNs with spike frequency adaptation are qualitatively similar. We also found that in both kinds of CANNs, the perturbative approach can be used to predict phase diagrams, dynamical variables, and speed of spontaneous motion. [ABSTRACT FROM AUTHOR]

In these lecture notes, the authors have outlined several approaches to solve a NMF/NTF problem. The following main conclusions can be drawn: 1) Multiplicative algorithms are not necessary the best approaches for NMF, especially if data representations are not very redundant or sparse. 2) Much better performance can be achieved using the FP-ALS (especially for large-scale problems), IPC, and QN methods. 3) To achieve high performance it is quite important to use the multilayer structure with multistart initialization conditions. 4) To estimate physically meaningful nonnegative components it is often necessary to use some a priori knowledge and impose additional constraints or regularization terms (to control sparsity, boundness, continuity or smoothness of the estimated nonnegative components). [ABSTRACT FROM PUBLISHER]

The present paper looks for possible neural mechanism underlying such high-level brain functioning as association and concept-formation. Primitive neural models of association and concept-formation are presented, which will elucidate the distributed and multiply superposed manner of retaining knowledge in the brain. The models are subject to two rules of self-organization of synaptic weights, orthogonal and covariance learning. The convergence of self-organization is proved, and the characteristics of these learning rules are shown. The performances, especially the noise immunity, of the association net and concept-formation net are analyzed. [ABSTRACT FROM AUTHOR]