Computational cognitive models of spatial memory in navigation space: A review. Madl, T.; Chen, K.; Montaldi, D.; and Trappl, R. Neural networks : the official journal of the International Neural Network Society, 65C:18-43, 1, 2015.
Computational cognitive models of spatial memory in navigation space: A review. [pdf]Paper  Computational cognitive models of spatial memory in navigation space: A review. [link]Website  abstract   bibtex   
Spatial memory refers to the part of the memory system that encodes, stores, recognizes and recalls spatial information about the environment and the agent's orientation within it. Such information is required to be able to navigate to goal locations, and is vitally important for any embodied agent, or model thereof, for reaching goals in a spatially extended environment. In this paper, a number of computationally implemented cognitive models of spatial memory are reviewed and compared. Three categories of models are considered: symbolic models, neural network models, and models that are part of a systems-level cognitive architecture. Representative models from each category are described and compared in a number of dimensions along which simulation models can differ (level of modeling, types of representation, structural accuracy, generality and abstraction, environment complexity), including their possible mapping to the underlying neural substrate. Neural mappings are rarely explicated in the context of behaviorally validated models, but they could be useful to cognitive modeling research by providing a new approach for investigating a model's plausibility. Finally, suggested experimental neuroscience methods are described for verifying the biological plausibility of computational cognitive models of spatial memory, and open questions for the field of spatial memory modeling are outlined.
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 title = {Computational cognitive models of spatial memory in navigation space: A review.},
 type = {article},
 year = {2015},
 identifiers = {[object Object]},
 keywords = {Computational cognitive modeling,Spatial memory models},
 pages = {18-43},
 volume = {65C},
 websites = {http://www.sciencedirect.com/science/article/pii/S0893608015000040},
 month = {1},
 day = {20},
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 abstract = {Spatial memory refers to the part of the memory system that encodes, stores, recognizes and recalls spatial information about the environment and the agent's orientation within it. Such information is required to be able to navigate to goal locations, and is vitally important for any embodied agent, or model thereof, for reaching goals in a spatially extended environment. In this paper, a number of computationally implemented cognitive models of spatial memory are reviewed and compared. Three categories of models are considered: symbolic models, neural network models, and models that are part of a systems-level cognitive architecture. Representative models from each category are described and compared in a number of dimensions along which simulation models can differ (level of modeling, types of representation, structural accuracy, generality and abstraction, environment complexity), including their possible mapping to the underlying neural substrate. Neural mappings are rarely explicated in the context of behaviorally validated models, but they could be useful to cognitive modeling research by providing a new approach for investigating a model's plausibility. Finally, suggested experimental neuroscience methods are described for verifying the biological plausibility of computational cognitive models of spatial memory, and open questions for the field of spatial memory modeling are outlined.},
 bibtype = {article},
 author = {Madl, Tamas and Chen, Ke and Montaldi, Daniela and Trappl, Robert},
 journal = {Neural networks : the official journal of the International Neural Network Society}
}
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