Higher Order and Long-Range Synchronization Effects for Classification and Computing in Oscillator-Based Spiking Neural Networks

In the circuit of two thermally coupled VO2 oscillators, we studied a higher order synchronization effect, which can be used in object classification techniques to increase the number of possible synchronous states of the oscillator system. We developed the phase-locking estimation method to determine the values of subharmonic ratio and synchronization effectiveness. In our experiment, the number of possible synchronous states of the oscillator system was twelve, and subharmonic ratio distributions were shaped as Arnold's tongues. In the model, the number of states may reach the maximum value of 150 at certain levels of coupling strength and noise. The long-range synchronization effect in a one-dimensional chain of oscillators occurs even at low values of synchronization effectiveness for intermediate links. We demonstrate a technique for storing and recognizing vector images, which can used for reservoir computing. In addition, we present the implementation of analog operation of multiplication, the synchronization based logic for binary computations, and the possibility to develop the interface between spike neural network and a computer. Based on the universal physical effects, the high order synchronization can be applied to any spiking oscillators with any coupling type, enhancing the practical value of the presented results to expand spike neural network capabilities.Comment: 25 pages, 13 figure

An object-based visual selection framework

Real scenes are composed of multiple points possessing distinct characteristics. Selectively, only part of the scene undergoes scrutiny at a time, and the mechanism responsible for this task is named selective visual attention. Spatial location with the highest contrast might highlight from scene reaching level of awareness (bottom-up attention). On the other hand, attention may also be voluntarily directed to a particular object in the scene (object-based attention), which requires the recognition of a specific target (top-down modulation). In this paper, a new visual selection model is proposed, which combines both early visual features and object-based visual selection modulations. The possibility of the modulation regarding specific features enables the model to be applied to different domains. The proposed model integrates three main mechanisms. The first handles the segmentation of the scene allowing the identification of objects. In the second one, the average of saliency of each object is computed, which provides the modulation of the visual attention for one or more features. Finally, the third builds the object saliency map, which highlights the salient objects in the scene. We show that top-down modulation has a stronger effect than bottom-up saliency when a memorized object is selected, and this evidence is clearer in the absence of any bottom-up clue. Experiments with synthetic and real images are conducted, and the obtained results demonstrate the effectiveness of the proposed approach for visual selection. (C) 2015 Elsevier B.V. All rights reserved.Fed Univ Sergipe UFS, Dept Informat Syst, Itabaiana, Sergipe, BrazilFed Univ Sao Paulo UNIFESP, Dept Sci & Technol, Sao Paulo, SP, BrazilUniv Sao Paulo, Inst Math & Comp Sci, Dept Comp Sci, Sao Carlos, SP, BrazilFed Univ Sao Paulo UNIFESP, Dept Sci & Technol, Sao Paulo, SP, BrazilWeb of Scienc