Computational Studies of the Role of Semantic Representation in Episodic Memory Computational Neuroscience


Experimental studies have found that episodic memory in humans is unreliable, often preserving little more than the gist of the experienced episode, but none of the details. We hypothesize that this property of the episodic memory system results from the fact that episodes are stored only in terms of higher order information, i.e., semantic representation, not their underlying sensory inputs. This hypothesis is similar to Tulving's SPI (Serial-Parallel-Independent) model (Tulving, 1995), in which sensory information is first processed by the semantic system before being stored in the episodic system. We have recently developed a computational model to study the interrelation between the semantic and episodic system. In this model, the semantic system compresses the high-dimensional sensory inputs to a lower dimensionality both in space and time, i.e., the semantic representation can be represented by fewer neurons and varies at a lower rate than the sensory input stream. Episodic memories are represented as sequences of semantic representations, which are stored in and retrieved from a memory network. Preliminary results indicate that episodic memory is more accurate if the underlying semantic representation has been optimized for the object that is being stored in episodic memory. The goal of this project is to extend these results to new object types and classes of objects. A good command of Python is required.


Tulving, E. (1995). Organization of memory: Quo vadis. In M. Gazzaniga (Ed.), The cognitive neurosciences (pp. 839–847). Cambridge, MA: MIT Press.


Jing Fang and Prof. Dr. Sen Cheng

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

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