Konstantin-Klemens Lurz

Graduate Student

Konstantin-Klemens Lurz

My interest lies in the topic of system identification, i.e. the finding of a mathematical model that maps the measurements of system inputs (visual stimuli) to system outputs (neuronal activity in the visual cortex of mice). The approach I chose for fitting these models is machine learning, more precisely deep convolutional neural networks (DCNNs). Identifying the underlying computations in a biological neural network using DCNNs can help the field in two ways: It can 1) provide insights about the functioning of biological neural networks for the neuroscience community and 2) it can identify useful inductive biases to be transferred to artificial neural networks for the machine learning community. My current project revolves around fitting such models that generalize between animals of the same species. Only if this condition is met, we can assume that the fitted model is not susceptible to subject-specific features and noise but captures general non-linear features that are characteristic for the visual cortex of mice as a whole.


Lab members are shown in this color. Preprints are shown in this color.


Mohammad Bashiri, Edgar Y. Walker, Konstantin-Klemens Lurz, Akshay Kumar Jagadish, Taliah Muhammad, Zhiwei Ding, Zhuokun Ding, Andreas S. Tolias, Fabian H. Sinz A flow-based latent state generative model of neural population responses to natural images NeurIPS (spotlight)
Konstantin-Klemens Lurz, Mohammad Bashiri, Konstantin Friedrich Willeke, Akshay Kumar Jagadish, Eric Wang, Edgar Y Walker, Santiago Cadena, Taliah Muhammad, Eric Cobos, Andreas Tolias, Alexander Ecker, Fabian Sinz Generalization in data-driven models of primary visual cortex ICLR (spotlight)


Konstantin-Klemens Lurz Natural Language Processing in Artificial Neuronal Networks: Sentence analysis in medical papers Lund University Library