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Latent Attractors: A Mechanism for Context-Dependent Information Processing in Biological and Artificial Neural Systems

Doboli, Simona
http://rave.ohiolink.edu/etdc/view?acc_num=ucin984613871

Abstract Details

2001, PhD, University of Cincinnati, Engineering : Electrical Engineering.
The hippocampus is an important area in the brain involved mainly in memory processes. In humans, the hippocampus is essential for the formation and consolidation of memory, and is a primary target of Alzheimer's disease. In other animals, the hippocampus is especially involved in spatial tasks (e.g. navigation). It has been the subject of extensive experimental and theoretical investigation due to its major role in memory and cognition. This thesis focuses mainly on the mechanisms of context-dependent, non-linear spatial information processing in the rodent hippocampus. There is strong experimental evidence that the hippocampus creates and stores cognitive maps of an animal's environment. These maps facilitate path planning and goal-directed behavior – all tasks of great interest in robots as well as animals. However, the mechanisms of spatial information processing in the hippocampus are not completely understood. One aspect of cognitive maps that is not yet clarified is their dependence on the past experience, or context. The first part of the thesis focuses on developing computational models for context-dependent cognitive maps. These models are based on the idea of latent attractors – patterns embedded in recurrent neural networks that influence network dynamics and the response to external inputs without becoming fully manifested themselves. Context-dependent information processing is important not only in animal cognition, but also for problems such as robot navigation, sequence disambiguation, sequential recognition, etc. In the second part of the thesis the biologically inspired concept of latent attractor networks is studied as a general computational paradigm for solving context-dependent problems with neural networks. To gain better understanding of the capabilities of latent attractor networks, a theoretical analysis of their capacity and dynamics is performed. In addition to the model for context-dependence, more comprehensive computational models of the hippocampus are developed to explain specific experimental results such as the effect of changes in the environment on cognitive maps.
Ali Minai (Advisor)
227 p.
context-dependent information processing; attractor neural networks; spatial processing in the hippocampus; analysis of attractor neural networks

Recommended Citations

Citations

  • Doboli, S. (2001). Latent Attractors: A Mechanism for Context-Dependent Information Processing in Biological and Artificial Neural Systems [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin984613871

    APA Style (7th edition)

  • Doboli, Simona. Latent Attractors: A Mechanism for Context-Dependent Information Processing in Biological and Artificial Neural Systems. 2001. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin984613871.

    MLA Style (8th edition)

  • Doboli, Simona. "Latent Attractors: A Mechanism for Context-Dependent Information Processing in Biological and Artificial Neural Systems." Doctoral dissertation, University of Cincinnati, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin984613871

    Chicago Manual of Style (17th edition)

ucin984613871
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© 2001, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.