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.
Recommended previous knowledge
- 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
Examination
- 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
Organization
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.
Links to lecture
Schedule
| 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.
