Neuroplasticity is the process by which our brain is able to learn and adapt to the changing environment. Neuroplasticity can be observed at multiple scales, from microscopic changes in individual neurons to larger-scale changes in the function of cortical and other brain areas.

This workshop will cover recent developments in neuroplasticity and learning ranging from molecular and cellular level plasticity changes up to system level changes using both detailed and phenomenological modeling and analysis of experimental data. Topics such as spine dynamics, developmental, homeostatic and structural plasticity will be addressed. The rules governing neuroplasticity are also at the core of current models applied to machine learning which helps for the development of more efficient computers.

We aim to bring together a group of researchers from the experimental, theoretical and computational neuroscience communities to discuss with researchers interested in learning rules and principles for machine learning applications. A particular interest is in the integration of biologically plausible neuroplasticity rules with the more abstract learning rules in engineering applications.
 

The registration is free however mandatory due to a limited number of places.
 

Preliminary list of speakers :

• Guillaume Bellec (TU Graz)
• Sander Bohte (CWI, NL)
• Giuseppe Chindemi (BBP, CH)
• SueYeon Chung (Columbia and MIT, US)
• Jan Kirchner (MPI Brain Research, DE)
• Claire Meissner-Bernard (FMI, CH)
• Ausra Saudargiene (LSMUNI, LT)
• Walter Senn (UBERN)
• Jesper Sjostrom (McGill, CN)
• Taro Toyoizumi (Riken, JP) 
• Katharina Wilmes (Imperial, UK)