Program
The workshop starts with dinner on Sunday, March 25 and ends after lunch on Thursday, March 29. Further details will follow soon.

Asja Fischer
TBA
TBA

Mareike Fischer
TBA
TBA

Nina Gantert
TBA
TBA

Matthias Hammer
TBA
TBA

Benedikt Jahnel
TBA
TBA

Sándor Kolumbán
TBA
TBA

Christian Leibold
TBA
TBA

Johannes Lengler
TBA
TBA

Eva Löcherbach
TBA
TBA

Matthias Löwe
TBA
TBA

Michael Messer
Multiscale change point detection in point processes
Neuronal spike trains often show temporal changes in their firing activity such as changes in the rate or in the regularity of spike occurrences. Such changes in the parameters are believed to have crucial relevance for information processing in the nervous system and also impact statistical analyses which require stationarity of the underlying models. Therefore, we are interested in localizing 'change points' in spike trains, i.e., points in time where the parameters change. Since change points are typically observed in different time scales, a multiscale procedure was proposed: in the context of stochastic point process models a multiple filter test is discussed which tests the null hypothesis of constant parameters. After rejection of the null hypothesis, change points can be localized using a multiple filter algorithm.
In this talk we focus on the detection of changes in the rate, but also touch on related questions, e.g., the detection of changes in the regularity or the asymptotic behavior of the underlying auxiliary statistics under alternative scenarios of change points. Further, we discuss recent ideas of jointly detecting both, changes in the rate as well as changes in the regularity. 
Dirk Metzler
TBA
TBA

Jesper Møller
The cylindrical Kfunction and Poisson line cluster point processes
The analysis of point patterns with linear structures is of interest in many applications. To detect anisotropy in such cases, in particular in case of a columnar structure, we introduce a functional summary statistic, the cylindrical Kfunction, which is a directional Kfunction whose structuring element is a cylinder. Further we introduce a class of anisotropic Cox point processes, called Poisson line cluster point processes. The points of such a process are random displacements of Poisson point processes deﬁned on the lines of a Poisson line process. Parameter estimation based on moment methods or Bayesian inference for this model is discussed when the underlying Poisson line process is latent. To illustrate the methodologies, we analyze two and threedimensional point pattern data sets. The threedimensional data set is of particular interest as it relates to the minicolumn hypothesis in neuroscience, claiming that pyramidal and other brain cells have a columnar arrangement perpendicular to the surface of the brain.

Guido Montúfar
TBA
TBA

Klaus Obermayer
TBA
TBA

Silke Rolles
TBA
TBA

Wioletta Ruszel
TBA
TBA

Ngoc Tran
Random subsampling in neuroscience
Neuroscientists often randomly subsample from a large scale recording to estimate how certain population quantities vary as the population size changes. In this talk, we show that this method can lead to erroneous conclusions for certain quantities.
Our example is the specific heat constant. Experimental estimations of this constant led to influential publications which propose that neural populations are optimized to operate at a critical thermodynamic point. However, further theoretical analyses reproduced this `critical' behavior through simple models. Here we generalize this work, by showing that the ‘signature of criticality’ arises precisely because the neurons are subsampled uniformly at random, except in two explicit cases. Our work calls for more research into which population quantities can reliably be estimated through random subsampling of large scale recordings.
Joint work with Jakob Macke 
Anton Wakolbinger
TBA
TBA