A project in computational cognitive modeling: the mnemonic-perceptual functions of the hippocampus Computational Neuroscience


Cognitive processes, such as perception and memory, are  complex phenomena that operate in the face of biological noise. Although limited in their biological plausibility, abstract cognitive models are a useful tool to break down the underlying principles and mechanisms of such phenomena. We apply this top-down approach to study memory, in particular episodic memory and its interactions with other modules, such as semantic memory and perceptual systems. Our special interest is directed to the hippocampus, a structure in the medial temporal lobe that lies on top of the visual hierarchy and is crucially engaged in memory.

We have ongoing projects related to this topic and interested students are welcome to contact us for potential projects. Note that programming skills (primarily Python and Matlab) are necessary to complete a project.  

For your orientation, here are some of the projects that we are/ have been working on

  • An abstract model of recognition memory studying the contributions of the hippocampus and perirhinal cortex
  • Integrating sensory modules to into abstract memory modules using
    • Slow feature analysis (SFA): an unsupervised machine learning algorithm meant to mimic sensory processing. The main principle of SFA is to extract slowly varying features from quickly changing, noisy sensory signal. (Wiskott et al. 2011)
    • Hierarchical deep learning models for sensory processing and object recognition (Riesenhuber & Poggio 1999)
    • Face morphing procedure
  • Exploring the role of stimulus material on memory tasks, e.g. recognition memory

Eichenbaum, H., Yonelinas, A. and Ranganath, C. (2007). The Medial Temporal Lobe and Recognition Memory. Annual Review of Neuroscience, 30(1), pp.123-152.

Laurenz Wiskott et al. (2011) Slow feature analysis. Scholarpedia, 6(4):5282.

Riesenhuber, M. and Poggio, T. (1999). Hierarchical models of object recognition in cortex. Nature Neuroscience, 2(11), pp.1019-1025.


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.

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210