Publications by Alexander Kevin Seeholzer

Stability of working memory in continuous attractor networks under the control of short-term plasticity

Wulfram Gerstner, Alexander Kevin Seeholzer, Moritz Deger

Continuous attractor models of working-memory store continuous-valued information in continuous state-spaces, but are sensitive to noise processes that degrade memory retention. Short-term synaptic plasticity of recurrent synapses has previously been shown ...

2019

Multicontact Co-operativity in Spike-Timing-Dependent Structural Plasticity Stabilizes Networks

Wulfram Gerstner, Alexander Kevin Seeholzer, Moritz Deger

Oxford University Press2018

Synaptic patterning and the timescales of cortical dynamics

Alexander Kevin Seeholzer

Neocortical circuits, as large heterogeneous recurrent networks, can potentially operate and process signals at multiple timescales, but appear to be differentially tuned to operate within certain temporal receptive windows. The modular and hierarchical or ...

Elsevier2017

Mapping the function of neuronal ion channels in model and experiment

Rajnish Ranjan, Alexander Kevin Seeholzer, Tim Vogels, William Francis Podlaski

Ion channel models are the building blocks of computational neuron models. Their biological fidelity is therefore crucial for the interpretation of simulations. However, the number of published models, and the lack of standardization, make the comparison o ...

Elife Sciences Publications Ltd2017

Continuous attractor working memory and provenance of channel models

Alexander Kevin Seeholzer

The brain is a complex biological system composed of a multitude of microscopic processes, which together give rise to computational abilities observed in everyday behavior. Neuronal modeling, consisting of models of single neurons and neuronal networks at ...

EPFL2017

Algorithmic Composition of Melodies with Deep Recurrent Neural Networks

Wulfram Gerstner, Johanni Michael Brea, Alexander Kevin Seeholzer, Florian François Colombo, Samuel Pavio Muscinelli

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 corpus o ...

2016

Periodic versus Intermittent Adaptive Cycles in Quasispecies Coevolution

Alexander Kevin Seeholzer

We study an abstract model for the coevolution 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 interdepe ...

American Physical Society2014