Abstract— Supervised learning rules for spiking neural networks are currently only able to use time-to-first-spike coding and are plagued by very irregular learning curves due t...
The need for a neuronal coding scheme that is robust against the corruption of action potentials seems to support the idea of population rate coding, where the relevance of a sing...
— Recurrent networks and hardware analogs that perform a winner-take-all computation have been studied extensively. This computation is rarely demonstrated in a spiking network o...
We report and compare the performance of different learning algorithms based on data from cortical recordings. The task is to predict the orientation of visual stimuli from the ac...
Jan Eichhorn, Andreas S. Tolias, Alexander Zien, M...