Leonid I. Perlovsky

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Fuzzy Logic is extended toward dynamic adaptation of the degree of fuzziness. The motivation is to explain the process of learning as joint model improvement and fuzziness reduction. A learning system with fuzzy models is introduced. Initially, the system is in a highly fuzzy state of uncertain knowledge, and it dynamically evolves into a low-fuzzy state of(More)
This brief describes neural modeling fields (NMFs) for object perception, a bio-inspired paradigm. We discuss previous difficulties in object perception algorithms encountered since the 1950s, and describe how NMF overcomes these difficulties. NMF mechanisms are compared to recent experimental neuroimaging observations, which have demonstrated that initial(More)
Processes in the mind: perception, cognition, concepts, instincts, emotions, and higher cognitive abilities for abstract thinking, beautiful music are considered here within a neural modeling fields (NMFs) paradigm. Its fundamental mathematical mechanism is a process "from vague-fuzzy to crisp," called dynamic logic (DL). This paper discusses why this(More)
What is the role of language in cognition? Do we think with words, or do we use words to communicate made-up decisions? The paper briefly reviews ideas in this area since 1950s. Then we discuss mechanisms of cognition, recent neuroscience experiments, and corresponding mathematical models. These models are interpreted in terms of a biological drive for(More)
Fusion of sensor and communication data currently can only be performed at a late processing stage after sensor and textual information are formulated as logical statements at appropriately high level of abstraction. Contrary to this it seems, the human mind integrates sensor and language signals seamlessly, before signals are understood, at pre-conceptual(More)