We seek to understand how populations of neurons collectively process sensory input, perform computations and control behaviour. To this end, we develop statistical models and machine learning algorithms for large-scale analysis of neural data, and collaborate with experimental laboratories performing measurements of neural activity and behaviour.
We develop statistical models for the analysis of neural and behavioural data.
Read more about our work in our articles and conference papers.
We share code for many of our publications.
... paper accepted to Journal of Neuroscience - 2018-01-18
Our paper “Can serial dependencies in choices and neural activity explain choice probabilities?” by Jan-Matthis, Jakob, and Hendrikje Nienborg has been accepted to Journal of Neuroscience and is available online.
Correlations, unexplained by the sensory input, between the activity of sensory neurons and an animal’s perceptual choice (“choice probabilities”) have received attention from both a systems and computational neuroscience perspective. Conversely, while temporal correlations for both spiking activity (“non-stationarities”) and for a subject’s choices in perceptual tasks (“serial dependencies”) have long been established, they have typically been ignored when measuring choice probabilities. Some accounts of choice probabilities incorporating feedback predict that these observations are linked. Here, we explore the extent to which this is the case. We find that, contrasting with these predictions, choice probabilities are largely independent of serial dependencies, which adds new constraints to accounts of choice probabilities that include feedback.