Computational Neuroscience

© Boström

To understand the function of the neural control system, we developed a computational model for the nociceptive and proprioceptive afferent information processing to understand the reorganization of the sensory cortex. Furthermore, we are developing computational model to describe the neural motor control of human movements. These artificial neural networks are based on the theory of reservoir computing.
At the moment we are working on a reformulation of the reafference principle of von Holst and Mittelstedt.
We started these kind of studies based on simple models with two and four neurons. These models were used to understand the postural reflexive responses, as well as the complex activation patterns of shank muscles during walking.

PIs: H. Wagner, K. Boström

Boström, K. J., de Lussanet, M. H. E., Weiss, T., Puta, C., & Wagner, H. (2014). A computational model unifies apparently contradictory findings concerning phantom pain. Scientific Reports, 4.
Boström, K. J., Wagner, H., Prieske, M., & de Lussanet Marc. (2013). Model for a flexible motor memory based on a self-active recurrent neural network. Human Movement Science, 32(5), 880–898.
Chong, S. Y., Wagner, H., & Wulf, A. (2012). Neural oscillators triggered by loading and hip orientation can generate activation patterns at the ankle during walking in humans. Medical & Biological Engineering & Computing, 50(9), 917–923.
Chong, S. Y., Wagner, H., & Wulf, A. (2013). Application of neural oscillators to study the effects of walking speed on rhythmic activations at the ankle. Theoretical Biology & Medical Modelling, 10(1), 9.
Wulf, A., Wagner, H., Wulf, T., Schinowski, D., Puta, C., Anders, C., & Chong, S. Y. (2012). Phasic bursting pattern of postural responses may reflect internal dynamics Simulation of trunk reflexes with a neural oscillator model. J Biomech, 45(15), 2645–2650.