Extending a Hippocampal Model for Navigation Around a Maze Generated from Real-World Data

Luke W. Boorman, Andreas C. Damianou, Uriel Martinez Hernandez, Tony J. Prescott

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

2 Citations (SciVal)
49 Downloads (Pure)


An essential component in the formation of understanding is the ability to use past experience to comprehend the here and now, and to aid selection of future action. Past experience is stored as memories which are then available for recall at very short notice, allowing for understanding of short and long term action. Autobiographical memory (ABM) is a form of temporally organised memory and is the organisation of episodes and contextual information from an individual’s experience into a coherent narrative, which is key to a sense of self. Formation and recall of memories is essential for effective and adaptive behaviour in the world, providing contextual information necessary for planning actions and memory functions, such as event reconstruction. Here we tested and developed a previously defined computational memory model, based on hippocampal structure and function, as a first step towards developing a synthetic model of human ABM (SAM). The hippocampal model chosen has functions analogous to that of human ABM. We trained the model on real-world sensory data and demonstrate successful, biologically plausible memory formation and recall, in a navigational task. The hippocampal model will later be extended for application in a biologically inspired system for human-robot interaction.
Original languageEnglish
Title of host publication Biomimetic and Biohybrid Systems. Living Machines 2015.
Number of pages12
ISBN (Electronic)978-3-319-22979-9
ISBN (Print)978-3-319-22978-2
Publication statusPublished - 16 May 2016

Publication series

NameLecture Notes in Computer Science


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