Haefner Lab

Multiple postdoc and PhD student positions available. me for more information.


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 (causal) 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.

Haefner et al. 2016 (Neuron)
Lange & Haefner 2017 (Curr Opin Neurobiol)
Lange & Haefner 2020 (biorxiv)
Lange et al. 2020 (biorxiv)

Test of model predictions using data from macaque V1.

Bondy, Haefner & Cumming 2018 (Nature Neuroscience)

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.

Nature Neuroscience 16, 235–242 (2013) PDF+Code+Bibtex

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.

Neuron 81(1), 208-219 (2015)

Application to MT data allowed us to investigate the neural basis of the psychophysical suppression effect.

Liu et al. 2016 eLife

Binocular vision

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.

Neuron 57, vol 1, 147-158 (2008) PDF
NeurIPS 2008 PDF NeurIPS 2010 PDF
J Neuroscience, 31(22): 8295-8305 (2011) PDF 

Natural image statistics

Understanding the statistics of the natural world is important for understanding the properties of early sensory processing. Traditionally, this argument has been made in the context of efficient coding (Barlow) but what learning principle (objective function) is responsible for the properties of early sensory neurons, e.g. their receptive fields in the case of visual neurons, is still an open question and active field of research. Ultimately, this question is related to what generative model the brain has learnt for its sensory inputs.

PLoS Comput Biol 10(3): e1003468



Haefner Lab 2019

Lab Members

Ralf Haefner
Ralf Haefner, PI


Ankani Chattoraj
Ankani Chattoraj, Graduate Student

Sabya Shivkumar
Sabya Shivkumar, Graduate Student


Samuel Alvernaz
Samuel Alvernaz, Graduate Student

Zhen Chen
Zhen Chen, Graduate Student

Himanshu Ahuja Himanshu Ahuja, Graduate Student

Linghao Xu Linghao Xu, Graduate Student

Yong Soo Ra Yong Soo Ra, Undergraduate Student

Martynas Snarskis Martynas Snarskis, Undergraduate Student

Katarina Nichols Katarina Nichols, Undergraduate Student (Physics REU)

Katherine Moon Katherine Moon, High School Student



Experimental labs

Theoretical labs




Lab Conference Presentations



Bernstein Meeting 2017: Satellite workshop on Neural sampling



Department for Brain & Cognitive Sciences
358 Meliora Hall, Box 270268
University of Rochester
Rochester, NY 14627-0268



Postdoc position for modeling complex motion in the presence of self-motion:

We are looking for a postdoctoral research fellow as part of our multi-university project to investigate the neural underpinnings of causal inference.

The successful candidate will develop probabilistic causal inference models of how the brain perceives object motion in the presence of self-motion. These models will span all three of Marr's levels of analysis considering different computational goals, representations and algorithms, and implementations in neural circuits. A key component of the work will be to make model predictions for the neural and behavioral data collected by our experimental collaborators in the lab of Greg DeAngelis. For related references see below.

We expect suitable applicants to have a Doctorate in a related discipline (including psychology, neuroscience, computer science, among others), ideally involving productive research using probabilistic methods. Knowledge of sensory processing and neural data analysis are a plus. If interested, please contact , or apply through Indeed, while making explicit in the cover letter the position they are applying to.


  • Haefner et al. (2016). Perceptual Decision-Making as Probabilistic Inference by Neural Sampling. Neuron, 90(3):649-60. | article link
  • Gershman et al. (2016). Discovering hierarchical motion structure. Vision Research, 126 | http://dx.doi.org/10.1016/j.visres.2015.03.004
  • Shivkumar et al. (2019). A causal inference model for the perception of complex motion in the presence of self-motion. Conference on Cognitive Computational Neuroscience | pdf
  • Lange & Haefner (2020). Task-induced neural covariability as a signature of approximate Bayesian learning and inference | preprint