• Modellierung der Dynamik Rekurrenter Neuronaler Netze
• Visuelle Informationsverarbeitung und Computer Vision
• Neuromorphe Architekturen und Verarbeitungsprinzipien
• Lernverfahren (Machine Learning, Deep Learning)

Lehre

Journal Articles:

• Habtegiorgis, S., Jarvers, C., Rifai, K., Neumann, H. & Wahl, S. (2019). The role of bottom-up and top-down cortical interactions in adaptation to natural scene statistics. Frontiers in Neural Circuits, 13 (9).
• Jarvers, C., Brosch, T., Brechmann, A., Woldeit, M. L., Schulz, A. L., Ohl, F. W., Lommerzheim, M. & Neumann, H. (2016). Reversal learning in humans and gerbils: dynamic control network facilitates learning. Frontiers in Neuroscience, 10.

Conference Talks:

• Jarvers, C., Schimd, D. & Neumann, H. (2019). Temporal learning of dynamics in complex neuron models using backpropagation. IJCNN 2019.
• Jarvers, C. & Neumann, H. (2017). Adaptive dynamic network architectures for companion systems. ICCT 2017.

Conference Posters:

• Jarvers, C. & Neumann, H. (2019). Incorporating feedback in convolutional neural networks. CCN 2019. [poster]
• Jarvers, C. & Neumann, H. (2019). A compartmental model of feedback modulation in visual cortex. VSS 2019. [poster]
• Wahl, S., Habtegiorgis, S., Jarvers, C., Rifai, K. & Neumann, H. (2018). Model investigation on contribution of feedback in distortion induced motion adaptation. MODVIS 2018.
• König, D., Adam, M., Jarvers, C., Layher, G., Neumann, H. & Teutsch, M. (2017). Fully convolutional region proposal networks for multispectral person detection. PBVS 2017.