Our primary scientific interest lies in understanding how the brain forms percepts and uses them to make decisions, especially in the visual domain. In particular, we are interested in how the brain's perceptual beliefs about the outside world are represented by the responses of populations of cortical neurons and how their spiking activity gives rise to percepts and decisions. To that end we construct mathematical models that aim to explain neural responses and behavior.
Key concepts in the context of our work are perceptual decision-making, probabilistic inference, neural sampling, noise correlations, choice probabilities, population responses, optimal linear read-out, feedforward, recurrent and top-down processing, covert attention, psychophysical kernel, confirmation bias.
Probabilistic inference and neural sampling
In order to draw conclusions, or inferences, about the outside world, the brain has to combine sensory information with its learnt knowledge about the structure of the external world. How this is implemented in the brain is still unknown. By generating predictions for classic perceptual tasks, we test the hypothesis that the brain performs probabilistic inference by sampling, i.e. that neuronal activity can be interpreted as samples from a generative model of the world that the brain has previously learnt.
Population (de)coding and perceptual decision-making
How many sensory neurons contribute to a particular decision, how are they being read out (e.g. optimal or not) and which neurons are they? We have made significant progress recently towards answering these questions by deriving the analytical relationship between noise correlations, choice probabilities and read-out weights. This will allow us to answer two of these questions as soon as multi-electrode recordings from behaving animals become available, i.e. very soon.
Applying this framework to neural recordings from MT during a dual motion direction and binocular discrimination task while area V2 was being cooled, we could constrain the origin of the noise correlations in MT.
Application to MT data allowed us to investigate the neural basis of the psychophysical suppression effect.
Depth perception from binocular images is an exemplary model system for studying how the brain extracts information not explicitly present in its (2D) inputs. We have been particularly interested in understanding what feedforward computations might underlie the observed neurophysiology and how much information different binocular neuron types contain about depth.
Ralf Haefner, PI
Richard Lange, Graduate Student
Ankani Chattoraj, Graduate Student
Shuchen Wu, Undergraduate Student
- Chicharro, D., Panzeri, S., and Haefner, R.M. Choice Probabilities in the presence of nonzero signal stimuli, internal bias, and decision feedback. | pdf
- Lange, R.D. and Haefner, R.M. Inferring the brain’s internal model from sensory responses in a probabilistic inference framework, bioRxiv. | pdf
- Lange, R.D. and Haefner, R.M. Characterizing the influence of "internal states" on sensory activity, submitted. | pdf
- Liu LD, Haefner RM, Pack CC (2016). A neural basis for the spatial suppression of visual motion perception. | pdf
- Haefner RM, Berkes P, Fiser J (2016). Perceptual Decision-Making as Probabilistic Inference by Neural Sampling. Neuron, 90(3):649-60. | pdf
- Smolyanskaya A, Haefner RM, Lomber SG, Born RT (2015). A Modality-Specific Feedforward Component of Choice-Related Activity in MT. Neuron, 87(1):208-19. | pdf
- Lies, Häfner & Bethge (2014). Slow Subspace Analysis: A new Algorithm for Invariance Learning, PLoS Computational Biology 10(3):e1003468 | pdf
- 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 | pdf
- Haefner & Bethge (2010). Evaluating neural codes for inference using Fisher Information, Advances in Information Processing Systems 23, 1993-2001. | pdf
- Tanabe, Haefner & Cumming (2010). Push-pull organization of binocular receptive ﬁelds robustly encodes disparity in monkey V1, Journal for Neuroscience 31, 22, 8295-8305. | pdf
- Haefner & Cumming (2008). An improved estimator of Variance Explained in the presence of noise, Advances in Information Processing Systems 21, 585-592. | pdf
- Haefner & Cumming (2008). A specialization for the statistics of binocular images in primate V1, Neuron 57, 147-156. | pdf
- Häfner, Evans, Dehnen & Binney (2000). A dynamical model of the inner Galaxy, Monthly Notes of the Royal Astronomical Society 314, 433. | pdf
- Häfner, Evans, Dehnen & Binney (1999). A dynamical model of the inner Galaxy in "Galaxy Dynamics: A Rutgers Symposium", Eds. Merrit, Sellwood, Valluri, 371. | pdf
- Evans, Häfner & De Zeeuw (1997). Simple three-integral scale-free galaxy models, Monthly Notes of the Royal Astronomical Society 268, 328. | pdf
Department for Brain & Cognitive Sciences
358 Meliora Hall, Box 270268
University of Rochester
Rochester, NY 14627-0268
- New pre-print on "Decision-Related Signals In The Presence Of Nonzero Signal Stimuli, Internal Bias, And Feedback" with Daniel Chicharro and Stefano Panzeri.
- New preprint on “Characterizing the influence of 'internal states' on sensory activity
- Cosyne 2017: Poster on "Perceptual confirmation biases from approximate online inference"
- New pre-print on "Inferring the brain's internal model from sensory responses in a probabilistic inference framework"
- Poster at AREADNE: "Top-down attention in a probabilistic inference framework" in collaboration with Pietro Berkes and Josef Fiser.
- Paper out in eLife: "A neural basis for the spatial suppression of visual motion perception" in collaboration with Dave Liu and Chris Pack.
- Paper out in Neuron: "The implications of perception as probabilistic inference for correlated neural variability during behavior" in collaboration with Pietro Berkes and Jozsef Fiser.
- Spring School: "The role of simulations in neuroscience". Co-organized and co-taught a week-long seminar on the philosophy & science of simulations together with Philipp Berens & Eckhart Arnold, including visits to HBP co-director Felix Schürmann and chief critic Alex Pouget.
- Paper accepted at Neuron: "The implications of perception as probabilistic inference for correlated neural variability during behavior" in collaboration with Pietro Berkes and Jozsef Fiser: http://arxiv.org/abs/1409.0257
- Cosyne 2016
Two posters accepted
Invited Talk in workshop on "Form and function of choice-related feedback signals in decision making"
- New paper: "A modality-specific feedforward component of choice-related activity in MT" in collaboration with Alexandra Smolyanskaya, Stephen G. Lomber and Richard T. Born (Neuron)
- 2015, June 10-11: Bernstein SPARKS workshop on Deep Learning & Brain
- Cosyne 2015
Poster on "Choice probabilities, detect probabilities, and read-out with multiple neuronal input populations"
Workshop Talk on "The source of sensory & decision-related activity in area MT"
- SfN 2014
Poster on "On the relationship between stimulus-evoked and choice-related responses and correlations during perceptual decision-making in a probabilistic inference framework"
New job at the Department for Brain & Cognitive Sciences, University of Rochester (NY)
In Sept 2014 moved to Rochester and started my own group as an assistant professor. I'm looking to recruit postdocs, graduate students and undergraduates, so please be in touch if you'd like to work with me.