My primary scientific interest lies in understanding how the brain forms percepts and how it uses them to make decisions, especially in the visual domain. In particular, I am interested in how the brain's perceptual beliefs about the outside world are represented by the responses of populations of cortical neurons. To that end I use tools from machine learning to construct mathematical models that aim to explain neural responses and behavior. More details...
- Haefner RM, Berkes P, Fiser J (2016). Perceptual Decision-Making as Probabilistic Inference by Neural Sampling. Neuron, 90(3):649-60.
- Lies, Häfner & Bethge (2014). Slow Subspace Analysis: A new Algorithm for Invariance Learning, PLoS Computational Biology 10(3):e1003468
- Haefner, R. M., Gerwinn, S., Macke, J. H., & Bethge, M. (2013). Inferring decoding strategies from choice probabilities in the presence of correlated variability. Nature Neuroscience, 16(2), 235–242. doi:10.1038/nn.3309
- Haefner & Bethge (2010). Evaluating neural codes for inference using Fisher Information, Advances in Information Processing Systems 23, 1993-2001
- Tanabe, Haefner & Cumming (2010). Push-pull organization of binocular receptive ﬁelds robustly encodes disparity in monkey V1, Journal for Neuroscience 31, 22, 8295-8305.
- Haefner & Cumming (2008). An improved estimator of Variance Explained in the presence of noise, Advances in Information Processing Systems 21, 585-592.
- Haefner & Cumming (2008). A specialization for the statistics of binocular images in primate V1, Neuron 57, 147-156.
- Häfner, Evans, Dehnen & Binney (2000). A dynamical model of the inner Galaxy, Monthly Notes of the Royal Astronomical Society 314, 433.
- Häfner, Evans, Dehnen & Binney (1999). A dynamical model of the inner Galaxy in "Galaxy Dynamics: A Rutgers Symposium", Eds. Merrit, Sellwood, Valluri, 371.
- Evans, Häfner & De Zeeuw (1997). Simple three-integral scale-free galaxy models, Monthly Notes of the Royal Astronomical Society 268, 328.