We formulate the problem of oxygen delivery to neural tissue as a problem of association. Input to a pool of neurons in one brain area must be matched in space and time with metabolic inputs from the vascular network via the glial network. We thus have a model in which neural, glial, and vascular layers are connected bidirectionally, in that order. Connections between neuro-glial and glial-vascular stages are trained by an unsupervised learning mechanism such that input to the neural layer is sustained by the precisely patterned delivery of metabolic inputs from the vascular layer via the glial layer. Simulations show that the capacity of such a system to sustain patterns is weak when the glial layer is absent. Capacity is higher when a glial layer is present and increases with the layer size. The proposed formulation of neurovascular interactions raises many intriguing questions about the role of glial cells in cerebral circulation.

Matching and analysis of patterns or shapes in the digital plane are of utmost importance in various problems of computer vision and pattern recognition. A digital point set is such a pattern that corresponds to an object in the digital plane. Although there exist several data structures that can be employed for Approximate Point Set Pattern Matching (APSPM) in the real domain, they require substantial modification to support algorithms in the digital domain. To bridge this gap, a novel data structure called "angular tree" is proposed, targeting an efficient and error-controllable circular range query in the digital plane. The farthest pair of points may be used as the starting correspondence between the pattern set and the background set. Several classical discrete structures and methodologies of computational geometry, as well as some topological features of circles/discs in digital geometry, have been used in tandem, for successful realization of the proposed APSPM algorithm in the digital plane. The APSPM algorithm based on the angular tree has been implemented and tested on various point sets and the reported results demonstrate the efficiency and versatility of the new data structure for supporting APSPM algorithms.