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Multiscale Models of Brain Disorders pp 157–163Cite as

Using a Neurocomputational Autobiographical Memory Model to Study Memory Loss

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Part of the book series: Springer Series in Cognitive and Neural Systems ((SSCNS,volume 13))

Abstract

Autobiographical memory (AM) is a core component of human life and plays an important role in self-identification. Various conceptual models have been proposed to explain its functionalities and describe its dynamics. However, most existing computational AM models do not distinguish AM from other long-term memory. Specifically, during model design, the unique features and the memory encoding, storage, and retrieval procedures of AM were not taken into consideration in prior models. In this chapter, we introduce our neurocomputational AM model, which is consistent with Conway and Pleydell-Pearce’s model in terms of both the network structure and dynamics. We further propose how to apply our parameterized computational model to quantitatively study memory loss in people of different age groups. As such, we provide a suitable tool to evaluate the effect of different memory loss phases in a rapid and quantitative manner, which may be difficult or impossible in experimental studies on human subjects.

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Acknowledgements

This research is supported, in part, by the National Research Foundation, Prime Minister’s Office, Singapore, under its IDM Futures Funding Initiative and the Singapore Ministry of Health under its National Innovation Challenge on Active and Confident Ageing (NIC Project No. MOH/NIC/COG04/2017).

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Authors and Affiliations

  1. Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, Nanyang Technological University, Singapore, Singapore

    Di Wang, Ah-Hwee Tan & Chunyan Miao

  2. School of Social Sciences and Psychology & Marcs Institute for Brain and Behaviour, Western Sydney University, Sydney, New South Wales, Australia

    Ahmed A. Moustafa

  3. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Ah-Hwee Tan & Chunyan Miao

Authors
  1. Di Wang
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  2. Ahmed A. Moustafa
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  3. Ah-Hwee Tan
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  4. Chunyan Miao
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Corresponding author

Correspondence to Di Wang .

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Editors and Affiliations

  1. School of Computer Science, University of Lincoln, Lincoln, UK

    Vassilis Cutsuridis

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Wang, D., Moustafa, A.A., Tan, AH., Miao, C. (2019). Using a Neurocomputational Autobiographical Memory Model to Study Memory Loss. In: Cutsuridis, V. (eds) Multiscale Models of Brain Disorders. Springer Series in Cognitive and Neural Systems, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-030-18830-6_15

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