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of 26
pro vyhledávání: '"Seeholzer, Alexander"'
Reservoir computing is a powerful tool to explain how the brain learns temporal sequences, such as movements, but existing learning schemes are either biologically implausible or too inefficient to explain animal performance. We show that a network c
Externí odkaz:
http://arxiv.org/abs/1910.10559
Continuous "bump" attractors are an established model of cortical working memory for continuous variables and can be implemented using various neuron and network models. Here, we develop a generalizable approach for the approximation of bump states o
Externí odkaz:
http://arxiv.org/abs/1711.08032
Publikováno v:
Cerebral Cortex 28-4 (2018) 1396-1415
Excitatory synaptic connections in the adult neocortex consist of multiple synaptic contacts, almost exclusively formed on dendritic spines. Changes of dendritic spine shape and volume, a correlate of synaptic strength, can be tracked in vivo for wee
Externí odkaz:
http://arxiv.org/abs/1609.05730
Autor:
Colombo, Florian, Muscinelli, Samuel P., Seeholzer, Alexander, Brea, Johanni, Gerstner, Wulfram
A big challenge in algorithmic composition is to devise a model that is both easily trainable and able to reproduce the long-range temporal dependencies typical of music. Here we investigate how artificial neural networks can be trained on a large co
Externí odkaz:
http://arxiv.org/abs/1606.07251
We study an abstract model for the co-evolution between mutating viruses and the adaptive immune system. In sequence space, these two populations are localized around transiently dominant strains. Delocalization or error thresholds exhibit a novel in
Externí odkaz:
http://arxiv.org/abs/1408.6345
Publikováno v:
In Current Opinion in Neurobiology April 2017 43:156-165
Publikováno v:
PLoS Computational Biology. 4/19/2019, Vol. 15 Issue 5, p1-48. 48p. 1 Chart, 7 Graphs.
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