28 citations · 57 across the 5 of their papers we have counts for
5 papers
Working memory facilitates reward-modulated Hebbian learning in recurrent neural networks
Roman Pogodin, Dane Corneil, Alexander Seeholzer +2
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 t…
Efficient low-dimensional approximation of continuous attractor networks
Alexander Seeholzer, Moritz Deger, Wulfram Gerstner
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 d…
Multi-contact synapses for stable networks: a spike-timing dependent model of dendritic spine plasticity and turnover
Moritz Deger, Alexander Seeholzer, Wulfram Gerstner
Excitatory synaptic connections in the adult neocortex consist of multiple synaptic contacts, almost exclusively formed on dendritic spines. Changes of dendritic spine shape and vo…
Algorithmic Composition of Melodies with Deep Recurrent Neural Networks
Florian Colombo, Samuel P. Muscinelli, Alexander Seeholzer +2
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 i…
Periodic vs. intermittent adaptive cycles in quasispecies co-evolution
Alexander Seeholzer, Erwin Frey, Benedikt Obermayer
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 d…