Modeling Associative Memories Using Formal Concept Analysis
01 Jan 2015-pp 109-118
TL;DR: This paper model the associative memory activity using Formal Concept Analysis (FCA), which is a standard technique for data and knowledge processing, and shows that the extent and intent relations in the concepts help to recall the patterns bi-directionally.
Abstract: Associative memory is one of the primary functions of the human brain. In the literature, there are several neural networks based models that represent associative memory with the help of pattern associations. In this paper, we model the associative memory activity using Formal Concept Analysis (FCA), which is a standard technique for data and knowledge processing. In our proposal, patterns are associated with the help of object-attribute relations and the memory is represented using the formal concepts generated using FCA. We show that the extent and intent relations in the concepts help us to recall the patterns bi-directionally. Further, we model the pattern recall process for the given input even when the exact match is not found in the memory, using the concept hierarchies in the concept lattice.
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Book•
01 Jan 2007
TL;DR: Vadis et al. as discussed by the authors introduced the conceptual graph to the Semantic Web and proposed a framework for analysing and testing overlapping requirements with actors in Conceptual Graphs.
Abstract: Invited Papers.- An Introduction to Conceptual Graphs.- Trikonic Inter-Enterprise Architectonic.- Hypermedia Discourse: Contesting Networks of Ideas and Arguments.- Dynamic Epistemic Logic and Knowledge Puzzles.- Peirce on Icons and Cognition.- Conceptual Graphs.- Using Cognitive Archetypes and Conceptual Graphs to Model Dynamic Phenomena in Spatial Environments.- A Datatype Extension for Simple Conceptual Graphs and Conceptual Graphs Rules.- A Knowledge Management Optimization Problem Using Marginal Utility in a Metric Space with Conceptual Graphs.- Conceptual Graphs as Cooperative Formalism to Build and Validate a Domain Expertise.- An Inferential Approach to the Generation of Referring Expressions.- A Conceptual Graph Description of Medical Data for Brain Tumour Classification.- A Conceptual Graph Based Approach to Ontology Similarity Measure.- A Comparison of Different Conceptual Structures Projection Algorithms.- A Conceptual Graph Approach to Feature Modeling.- From Conceptual Structures to Semantic Interoperability of Content.- Formal Concept Analysis.- Faster Concept Analysis.- The Design Space of Information Presentation: Formal Design Space Analysis with FCA and Semiotics.- Reducing the Representation Complexity of Lattice-Based Taxonomies.- An FCA Perspective on n-Distributivity.- Towards a Semantology of Music.- Analysis of the Publication Sharing Behaviour in BibSonomy.- The MILL - Method for Informal Learning Logistics.- Bilingual Word Association Networks.- Using FCA for Encoding Closure Operators into Neural Networks.- Conceptual Structures.- Arc Consistency Projection: A New Generalization Relation for Graphs.- Mining Frequent Closed Unordered Trees Through Natural Representations.- Devolved Ontology for Smart Applications.- Historical and Conceptual Foundation of Diagrammatical Ontology.- Learning Common Outcomes of Communicative Actions Represented by Labeled Graphs.- Belief Flow in Assertion Networks.- Conceptual Fingerprints: Lexical Decomposition by Means of Frames - a Neuro-cognitive Model.- Constants and Functions in Peirce's Existential Graphs.- Revelator Game of Inquiry: A Peircean Challenge for Conceptual Structures in Application and Evolution.- Short Papers.- Helping System Users to Be Smarter by Representing Logic in Transaction Frame Diagrams.- Quo Vadis, CS? - On the (non)-Impact of Conceptual Structures on the Semantic Web.- A Framework for Analyzing and Testing Overlapping Requirements with Actors in Conceptual Graphs.- Implementation of SPARQL Query Language Based on Graph Homomorphism.- Cooperative CG-Wrappers for Web Content Extraction.- Conceptual Graphs and Ontologies for Information Retrieval.- Representation Levels Within Knowledge Representation.- Supporting Lexical Ontology Learning by Relational Exploration.- Characterizing Implications of Injective Partial Orders.- DVDSleuth: A Case Study in Applied Formal Concept Analysis for Navigating Web Catalogs.- Navigation in Knowledge-Based System for Helpdesk Based on FCA.- Functorial Properties of Formal Concept Analysis.- Towards an Ontology to Conceptualize Solution Analysis Tasks in CSCL Environments.
54 citations
01 Apr 2015
TL;DR: This work makes use of Formal Concept Analysis (FCA), a mathematical framework for data and knowledge processing, to represent memories and to perform some of the cognitive functions of human brain, in particular, the functionalities of bidirectional associative memories.
Abstract: Pattern association is one among the ways through which human brain stores and recalls information. From the literature, it is evident that cognitive abilities of human brain such as learning, memorizing, recalling and updating of information are performed via concepts and their connections. In this work we have made use of Formal Concept Analysis (FCA), a mathematical framework for data and knowledge processing, to represent memories and to perform some of the cognitive functions of human brain. In particular, we model the functionalities of bidirectional associative memories. The proposed model can learn, memorize the learnt information, bi-directionally recall the information that is associated with the presented cue with the help of object-attribute relations that exists in the scenario and update the knowledge when there is a change in the considered scenario. Also when a noisy cue is given, the model is capable of recalling the most closely associated pattern by exploiting the concept hierarchy principle of FCA. Similarly, when a new information is presented on a learnt scenario, the proposed model can update its knowledge by avoiding the need to re-learn scenario. We illustrate the proposed model with a case study and validate with experiments on few real world datasets.
44 citations
Posted Content•
TL;DR: The proposed model of high dimensional formal representation of conceptual space is mathematically analysed and inferred to exhibit quantum aspects and achieves cognition, in particular, consciousness.
Abstract: Human cognition is a complex process facilitated by the intricate architecture of human brain. However, human cognition is often reduced to quantum theory based events in principle because of their correlative conjectures for the purpose of analysis for reciprocal understanding. In this paper, we begin our analysis of human cognition via formal methods and proceed towards quantum theories. Human cognition often violate classic probabilities on which formal representation of conceptual spaces are built. Further, geometric representation of conceptual spaces proposed by Gardenfors discusses the underlying content but lacks a systematic approach (Gardenfors, 2000; Kitto et. al, 2012). However, the aforementioned views are not contradictory but different perspective with a gap towards sufficient understanding of human cognitive process. A comprehensive and systematic approach to model a relatively complex scenario can be addressed by vector space approach of conceptual spaces as discussed in literature. In this research, we have proposed an approach that uses both formal representation and Gardenfors geometric approach. The proposed model of high dimensional formal representation of conceptual space is mathematically analysed and inferred to exhibit quantum aspects. Also, the proposed model achieves cognition, in particular, consciousness. We have demonstrated this process of achieving consciousness with a constructive learning scenario. We have also proposed an algorithm for conceptual scaling of a real world scenario under different quality dimensions to obtain a conceptual scale.
3 citations
Cites background from "Modeling Associative Memories Using..."
...Human cognition process is conventionally described and represented in the form of conceptual space (Kumar et al ., 2015)....
[...]
01 Nov 2017
TL;DR: The construction of Galois connections between different structures provides a number of advantages, both from the theoretical and the applied standpoints.
Abstract: The construction of Galois connections between different structures provides a number of advantages, both from the theoretical and the applied standpoints. In this paper, we survey some works on Galois connections focused essentially on certain aspects of Computational Intelligence.
2 citations
Cites methods from "Modeling Associative Memories Using..."
...In [16], the authors model the associative memory activity using FCA techniques: patterns are associated with the help of objectattribute relations and the memory is represented using the formal concepts generated using FCA....
[...]
References
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Book•
04 Dec 1998
TL;DR: This is the first textbook on formal concept analysis that gives a systematic presentation of the mathematical foundations and their relation to applications in computer science, especially in data analysis and knowledge processing.
Abstract: From the Publisher:
This is the first textbook on formal concept analysis. It gives a systematic presentation of the mathematical foundations and their relation to applications in computer science, especially in data analysis and knowledge processing. Above all, it presents graphical methods for representing conceptual systems that have proved themselves in communicating knowledge. Theory and graphical representation are thus closely coupled together. The mathematical foundations are treated thoroughly and illuminated by means of numerous examples.
4,757 citations
"Modeling Associative Memories Using..." refers methods in this paper
...It is mainly used as an analytical tool for knowledge representation and discovery [2]....
[...]
TL;DR: A bird's eye review of the various types of ANNs and the related learning rules is presented, with special emphasis on backpropagation ANNs theory and design, and a generalized methodology for developing successful ANNs projects from conceptualization, to design, to implementation is described.
2,622 citations
TL;DR: This interpretation of neural networks is built with fuzzy rules using a new fuzzy logic operator which is defined after introducing the concept of f-duality and offers an automated knowledge acquisition procedure.
Abstract: Artificial neural networks are efficient computing models which have shown their strengths in solving hard problems in artificial intelligence. They have also been shown to be universal approximators. Notwithstanding, one of the major criticisms is their being black boxes, since no satisfactory explanation of their behavior has been offered. In this paper, we provide such an interpretation of neural networks so that they will no longer be seen as black boxes. This is stated after establishing the equality between a certain class of neural nets and fuzzy rule-based systems. This interpretation is built with fuzzy rules using a new fuzzy logic operator which is defined after introducing the concept of f-duality. In addition, this interpretation offers an automated knowledge acquisition procedure.
488 citations
01 Mar 2006
TL;DR: This paper attempts to develop a layered reference model of the brain (LRMB) that formally and rigorously explains the functional mechanisms and CPs of natural intelligence.
Abstract: A variety of life functions and cognitive processes (CPs) have been identified in cognitive informatics, psychology, cognitive science, and neurophilosophy. This paper attempts to develop a layered reference model of the brain (LRMB) that formally and rigorously explains the functional mechanisms and CPs of natural intelligence. A comprehensive and coherent set of mental processes and their relationships is identified in LRMB that encompasses 37 CPs at six layers known as the sensation, memory, perception, action, metacognitive, and higher cognitive layers from the bottom-up. The LRMB reference model provides an integrated framework for modeling the brain and the mind. LRMB also enables future extension and refinement of the CPs within the same hierarchical framework. LRMB can be applied to explain a wide range of physiological, psychological, and cognitive phenomena in cognitive informatics, particularly the relationships and interactions between the inherited and the acquired life functions, as well as those of the subconscious and conscious CPs.
289 citations
Proceedings Article•
01 Jan 2006
TL;DR: The LRMB reference model as discussed by the authors provides an integrated framework for modeling the brain and the mind and can be applied to explain a wide range of physiological, psychological, and cognitive phenomena in cognitive informatics, particularly the relationships and interactions between the inherited and the acquired life functions, as well as those of the subconscious and conscious CPs.
Abstract: A variety of life functions and cognitive processes (CPs) have been identified in cognitive informatics, psychology, cognitive science, and neurophilosophy. This paper attempts to develop a layered reference model of the brain (LRMB) that formally and rigorously explains the functional mechanisms and CPs of natural intelligence. A comprehensive and coherent set of mental processes and their relationships is identified in LRMB that encompasses 37 CPs at six layers known as the sensation, memory, perception, action, metacognitive, and higher cognitive layers from the bottom-up. The LRMB reference model provides an integrated framework for modeling the brain and the mind. LRMB also enables future extension and refinement of the CPs within the same hierarchical framework. LRMB can be applied to explain a wide range of physiological, psychological, and cognitive phenomena in cognitive informatics, particularly the relationships and interactions between the inherited and the acquired life functions, as well as those of the subconscious and conscious CPs.
259 citations