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Neuromorphic computing is one of the strongest candidate to address the physical limitations in computational power known as Moore's Law. Neuromorphic computing is not an alternative to classical computing; it is rather the computational paradigm that replicates the human brain thinking in patterns. Intelligent techniques in machine learning/deep learning and neuromorphic computing have attractive properties – such as the ability to learn, or to make explanatory decisions – this makes them suited for broad classes of problems; however there is not one technique suited to solve every problem we encounter today in sophisticated pattern recognition tasks. Two prominent techniques aim to reconcile key challenges in pattern recognition, change detection, and more specific applications such as automated target recognition and malware detection. First, neural networks are very good at pattern recognition, yet they do not provide explanations on how they render a certain outcome. Second, fuzzy logic systems can reason with imprecise information, and explain their decisions, yet they cannot automatically obtain or compose the rules used to make decisions.
This topic concerns the possible development of hybrid systems that combine the power of complementary techniques, and advance machine learning towards machine understanding, using memristor arrays - the building blocks of neuromorphic computing -- as foundational theoretical and implementation environments for this technological advancement. Fuzzy logic yields inference mechanisms under cognitive uncertainty, whereas computational neural systems provide learning, adaptation, fault-tolerance, and generalization and parallelization. Fuzzy neural networks with threshold gates are such examples of potentially powerful hybrid systems. There are claims that the synergy between multivalued/fuzzy logic and threshold gate networks can have a 100% reliability in specific pattern recognition tasks. This research aims to investigate classes of pattern recognition problems for which this assertion is true.
Example Approaches
Research in this area could consider some of the following areas:
Some example approaches could be:
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