Current Topics in Computational Neuroscience

Current Topics in Computational Neuroscience

In this seminar recent computational neuroscience papers will be presented and discussed. The goal is to dive deeper into topics beyond what can be covered in a lecture and also to give an insight in recent research topics in our lab. To name some of the topics: Predictive models, BMI, CNN vs. human, neuronal morphologies. Below there is a list of papers of which you can choose a paper to present.

  • B.Inf.1236 Machine Learning (the seminar can accompany lecture in the same term)
  • B.Inf.1237 Deep Learning
  • B.Phy.5605.Mp: Computational Neuroscience: Basics


  • Oral presentation (approx. 30 min.) and term paper (max. 5000 words)
  • Examination requirements
    • Knowledge in a specific field of neuroscience
    • Ability to present the acquired knowledge in a both orally and in a written report


There will be a starting session where the topics are presented. In the following week we give you an introduction in how to present and give feedback. In the next weeks, in each weekly session, one paper will be presented (25 minutes) and subsequently discussed (~15 minutes) and the other slot will be a trial presentation of the presenter of the following week.

Our plan is to hold this seminar in presence in room Informatik-Provisorium - 0.102 on Mondays 10:00 - 12:00.


Date Topic Supervisor Paper Student
2022/04/25 Introductory session Alex, Suhas, Michaela, Laura
2022/05/2 How to present and give feedback Alex
09.05.22 -
16.05.22 First trial run Mishka Ang (2012) Sergio
23.05.22 BMI Mishka Ang (2012) Sergio
30.05.22 BMI Mishka Schirrmeister (2017) Lavanya
06.06.22 Pfingsten - - -
13.06.22 Predictive Models Suhas Pillow (2008) Darius
20.06.22 Predictive Models Suhas Walker (2019) Florentin
27.06.22 Predictive Models Mishka McIntosh (2017) Viktor
11.07.22 Neuronal Morphologies Laura Gouwens (2019) Ali
11.07.22 Neuronal Morphologies Laura Kanari (2019) Sarah
18.07.22 CNNs vs. humans Alex Geirhos (2019) Mahdi
18.07.22 CNN vs. humans Alex Güclü (2015) Shruti
31.07.22 Submission of report outline
15.08.22 Submission of final report

Papers to be discussed

  • Pillow et al. (2008): Spatio-temporal correlations and visual signalling in a complete neuronal population. Nature.
  • Walker et al. (2019): Inception loops discover what excites neurons most using deep predictive models. Nature Neuroscience.
  • Ang et al. (2012): Filter bank common spatial pattern algorithm on BCI competition IV Datasets 2a and 2b.Frontiers in Neuroscience.
  • McIntosh et al. (2017): Deep Learning Models of the Retinal Response to Natural Scenes. Neural Information Processing Systems.
  • Schirrmeister et al. (2017): Deep learning with convolutional neural networks for EEG decoding and visualization. Human Brain Mapping.
  • Geirhos et. al (2019): ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. International Conference for Learning Representations.
  • Güclü & van Gerven (2015): Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream. Journal of Neuroscience.
  • Gouwens et al. (2019): Classification of electrophysiological and morphological neuron types in the mouse visual cortex. Nature Neuroscience.
  • Kanari et al. (2019): Objective Morphological Classification of Neocortical Pyramidal Cells. Cerebral Cortex.
Neural Data Science Group
Institute of Computer Science
University of Goettingen