On-line blind separation of non-stationary signals

This paper addresses the problem of blind separation of non-stationary signals. We introduce an on-line separating algorithm for estimation of independent source signals using the assumption of non-stationary of sources. As a separating model, we apply a self-organizing neural network with lateral connections, and define a contrast function based on correlation of the network outputs. A separating algorithm for adaptation of the network weights is derived using the state-space model of the network dynamics, and the extended Kalman filter. Simulation results obtained in blind separation of artificial and real-world signals from their artificial mixtures have shown that separating algorithm based on the extended Kalman filter outperforms stochastic gradient based algorithm both in convergence speed and estimation accuracy

A new self-organizing neural gas model based on Bregman divergences

In this paper, a new self-organizing neural gas model that we call Growing Hierarchical Bregman Neural Gas (GHBNG) has been proposed. Our proposal is based on the Growing Hierarchical Neural Gas (GHNG) in which Bregman divergences are incorporated in order to compute the winning neuron. This model has been applied to anomaly detection in video sequences together with a Faster R-CNN as an object detector module. Experimental results not only confirm the effectiveness of the GHBNG for the detection of anomalous object in video sequences but also its selforganization capabilities.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Intrinsic adaptation in autonomous recurrent neural networks

A massively recurrent neural network responds on one side to input stimuli and is autonomously active, on the other side, in the absence of sensory inputs. Stimuli and information processing depends crucially on the qualia of the autonomous-state dynamics of the ongoing neural activity. This default neural activity may be dynamically structured in time and space, showing regular, synchronized, bursting or chaotic activity patterns. We study the influence of non-synaptic plasticity on the default dynamical state of recurrent neural networks. The non-synaptic adaption considered acts on intrinsic neural parameters, such as the threshold and the gain, and is driven by the optimization of the information entropy. We observe, in the presence of the intrinsic adaptation processes, three distinct and globally attracting dynamical regimes, a regular synchronized, an overall chaotic and an intermittent bursting regime. The intermittent bursting regime is characterized by intervals of regular flows, which are quite insensitive to external stimuli, interseeded by chaotic bursts which respond sensitively to input signals. We discuss these finding in the context of self-organized information processing and critical brain dynamics.Comment: 24 pages, 8 figure

Letter to the Editor: Physiological Interpretation of the Self-Organizing Map Algorithm

Air Force Office of Scientific Research (F49620-92-J-0499); Office of Naval Research (N00014-92-J-4015, N00014-91-J-4100

Asymptotic Level Density of the Elastic Net Self-Organizing Feature Map

Whileas the Kohonen Self Organizing Map shows an asymptotic level density following a power law with a magnification exponent 2/3, it would be desired to have an exponent 1 in order to provide optimal mapping in the sense of information theory. In this paper, we study analytically and numerically the magnification behaviour of the Elastic Net algorithm as a model for self-organizing feature maps. In contrast to the Kohonen map the Elastic Net shows no power law, but for onedimensional maps nevertheless the density follows an universal magnification law, i.e. depends on the local stimulus density only and is independent on position and decouples from the stimulus density at other positions.Comment: 8 pages, 10 figures. Link to publisher under http://link.springer.de/link/service/series/0558/bibs/2415/24150939.ht