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