Position: Professor agregat
Matthias S. Keil
Cognition, Development, and Psychology of Education
Faculty of Psychology, Passeig de la Vall d’Hebron 171
08035 Barcelona (Spain)
matskeil (at) ub.edu
My research focuses on computational neuroscience. This means to understand how neurons compute in order to produce the results that are seen in experiments. But I do not stop here such as many (if not most) people who do computational neuroscience: The next step is to apply my computational models to real-world stimuli. What does that mean? For example, the locust (a grasshopper) has a big neuron which responds selectively to approaching objects. It responds much less to other types of movement (e.g. receding objects, an objects that moves from one side to another etc). Now, if we could understand how this relatively “simple” visual system works, then we would be able to derive a computer algorithm which signals collision threats. And then put this into a camera. And put the camera into a car. In this way, we would have a driving assistant system that could warn the driver if he or she is about to collide with some object.
Other things I do/did are/were to model the computations which lead to brightness (perceived luminance) and lightness (perceived reflectance) in the visual system of humans. In a nutshell, the problem here is to disentangle reflectance (the physical amount of light being reflected by an object’s surface) from illumination. The problem is ill-posed, meaning that it cannot be solved mathematically exactly. So you need your creativity in order to design models which explain psychophysical results (visual illusions, such as White’s effect, grating induction, or simultaneous contrast), and ideally link the models to the neuronal level on the one hand, and apply them to image processing tasks on the other.
· Keil, M.S. (2015) Dendritic Pooling of Noisy Threshold Processes Can Explain Many Properties of a Collision-Sensitive Visual Neuron. PLoS Computational Biology 11(10):e1004479
· Keil, M.S. and López-Moliner, J. (2012) Unifying time to contact estimation and collision avoidance across species. PLoS Computational Biology 8(8):e1002625
· Keil, M.S. (2008) Does Face Image Statistics Predict a Preferred Spatial Frequency for Human Face Processing? Proceedings of the Royal Society of London B 1647 (275):2095-2100
· Keil, M.S. (2007) Gradient Representations and the Perception of Luminosity. Vision Research 47:3360-3372
· Keil, M.S. and Cristóbal, G. and Hansen, T. and Neumann, H. (2005). RecoveringReal-World Images from Single-Scale Boundaries with a Novel Filling-in Architecture. Neural Networks 18(10):1319-1331