Paper Info
Title
Extending a Hippocampal Model for Navigation Around a Maze Generated from Real-World Data.
Abstract
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.
Year
DOI
Venue
2015
10.1007/978-3-319-22979-9_44
Living Machines
Keywords
Field
DocType
Autobiographical memory,Episodic memory,Hippocampus,Robotics,Temporally restricted boltzmann machine,Navigation
Episodic memory,Psychology of self,Memory functions,Cognitive science,Computer science,Memory model,Artificial intelligence,Autobiographical memory,Recall,Event reconstruction,Encoding (memory)
Conference
Volume
ISSN
Citations 
9222
0302-9743
0
PageRank 
References 
Authors
0.34
5
4
Name
Order
Citations
PageRank
Luke Boorman1204.27
andreas damianou215117.68
Uriel Martinez-Hernandez314018.23
Tony J. Prescott416428.77