Arturo Tozzi (Corresponding Author)

Center for Nonlinear Science, University of North Texas

1155 Union Circle, #311427 Denton, TX 76203-5017 USA

Computational Intelligence Laboratory, University of Manitoba, Winnipeg, Canada

Winnipeg R3T 5V6 Manitoba


James F. Peters

Department of Electrical and Computer Engineering, University of Manitoba

75A Chancellor’s Circle Drive, Winnipeg, MB R3T 5V6 CANADA and

Department of Mathematics, Adıyaman University, 02040 Adıyaman, Turkey





©Neuroscience Letters, 2018.



A novel demon-based architecture is introduced to elucidate brain functions such as pattern recognition during human perception and mental interpretation of visual scenes.  Starting from the topological concepts of invariance and persistence, we introduce a Selfridge pandemonium variant of brain activity that takes into account a novel feature, namely, demons that recognize short straight-line segments, curved lines and scene shapes, such as shape interior, density and texture. Low-level representations of objects can be mapped to higher-level views (our mental interpretations): a series of transformations can be gradually applied to a pattern in a visual scene, without affecting its invariant properties.  This makes it possible to construct a symbolic multi-dimensional representation of the environment.  These representations can be projected continuously to an object that we have seen and continue to see, thanks to the mapping from shapes in our memory to shapes in Euclidean space.  Although perceived shapes are 3-dimensional (plus time), the evaluation of shape features (volume, colour, contour, closeness, texture, and so on) leads to n-dimensional brain landscapes.  Here we discuss the advantages of our parallel, hierarchical model in pattern recognition, computer vision and biological nervous system’s evolution.