Crosstalk and the spectrum of biological global broadcasts: Toward generalization of the Baars consciousness model across physiological subsystems

Once cognitive biological phenomena are recognized as necessarily having 'dual' information sources, it is easy to show that the information theory chain rule implies isolating coresident information sources from crosstalk requires more metabolic free energy than permitting correlation. This provides conditions for an evolutionary exaptation leading to dynamic global broadcasts of interacting cognitive biological processes analogous to, but slower than, consciousness, itself included within the paradigm. The argument is closely analogous to the well-studied exaptation of noise to trigger stochastic resonance amplification in physiological systems

When Spandrels Become Arches: Neural crosstalk and the evolution of consciousness

Once cognition is recognized as having a 'dual' information source, the information theory chain rule implies that isolating coresident information sources from crosstalk requires more metabolic free energy than permitting correlation. This provides conditions for an evolutionary exaptation leading to the rapid, shifting global neural broadcasts of consciousness. The argument is quite analogous to the well-studied exaptation of noise to trigger stochastic resonance amplification in neurons and neuronal subsystems. Astrobiological implications are obvious

Automatic and semi-automatic extraction of curvilinear features from SAR images

Extraction of curvilinear features from synthetic aperture radar (SAR) images is important for automatic recognition of various targets, such as fences, surrounding the buildings. The bright pixels which constitute curvilinear features in SAR images are usually disrupted and also degraded by high amount of speckle noise which makes extraction of such curvilinear features very difficult. In this paper an approach for the extraction of curvilinear features from SAR images is presented. The proposed approach is based on searching the curvilinear features as an optimum unidirectional path crossing over the vertices of the features determined after a despeckling operation. The proposed method can be used in a semi-automatic mode if the user supplies the starting vertex or in an automatic mode otherwise. In the semi-automatic mode, the proposed method produces reasonably accurate real-time solutions for SAR images

Laplacian Mixture Modeling for Network Analysis and Unsupervised Learning on Graphs

Laplacian mixture models identify overlapping regions of influence in unlabeled graph and network data in a scalable and computationally efficient way, yielding useful low-dimensional representations. By combining Laplacian eigenspace and finite mixture modeling methods, they provide probabilistic or fuzzy dimensionality reductions or domain decompositions for a variety of input data types, including mixture distributions, feature vectors, and graphs or networks. Provable optimal recovery using the algorithm is analytically shown for a nontrivial class of cluster graphs. Heuristic approximations for scalable high-performance implementations are described and empirically tested. Connections to PageRank and community detection in network analysis demonstrate the wide applicability of this approach. The origins of fuzzy spectral methods, beginning with generalized heat or diffusion equations in physics, are reviewed and summarized. Comparisons to other dimensionality reduction and clustering methods for challenging unsupervised machine learning problems are also discussed.Comment: 13 figures, 35 reference