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Despite being extensively used in many fields of science, there is still no general procedure to determine the most suitable neural architecture for a given task. Even today the design of an artificial neural network (ANN) is still very dependent on the designer's experience. Several methods for automatic design of ANNs have been proposed, but they are not biologically inspired, in this paper it's...
Models of spiking neural networks have a great potential to become a crucial tool in the development of complex network theory. Of particular interest, these models can be used to better understand the important class of brain functional networks, which are frequently studied in the context of computational network analysis. A fundamental question is whether functional connectivity sampling via surface...
This work investigates the effect of different data structures on the performance and accuracy of VG-RAM-based classifiers. This weightless neural model is based on RAM nodes having very large address input, what suggests the use of special data structures in order to deal with space and time computational costs. Four different data structures are explored, including the classical one used in recent...
Understanding the influence of some factors on a particular phenomenon can be very relevant in many cases of decision-making. An example would be the identification of the level of influence that factors such as smoking, stress and lack of exercise have on the predisposition to heart disease. Knowing which of these inputs are relevant for a person to become a cardiac patient, it is possible to take...
Thirty-Seven undergraduate students (23 engineering students, 14 social and human science students) had their electroencephalogram (EEG) recorded during the performing of mental rotation and recognition of virtual tridimensional geometric patterns tasks. Their spatial cognition degree of development was assessed by a BPR-5 psychological test. The Largest Lyapunov Exponent (LLE) was calculated from...
In this article a number of neural networks based on self organizing maps, that can be successfully used for dynamic object identification, is described. The structure and algorithms of learning and operation of such SOM-based neural networks are described in details, also some experimental results is given.
In this paper, we present a WiSARD-based system (CwisarD) facing the problem of change detection (CD) in multiple images of the same scene taken at different time, and, in particular, motion in videos of the same view taken by a static camera. Although the proposed weightless neural approach is very simple and straightforward, it provides very good results in challenging with others approaches on...
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