Wulfram Gerstner

Publications associées (129)

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Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons

Wulfram Gerstner, Skander Mensi, Christian Antonio Pozzorini, Olivier Richard Hagens

The way in which single neurons transform input into output spike trains has fundamental consequences for network coding. Theories and modeling studies based on standard Integrate-and-Fire models implicitly assume that, in response to increasingly strong i ...

Public Library of Science2016

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

Does computational neuroscience need new synaptic learning paradigms?

Wulfram Gerstner, Johanni Michael Brea

Computational neuroscience is dominated by a few paradigmatic models, but it remains an open question whether the existing modelling frameworks are sufficient to explain observed behavioural phenomena in terms of neural implementation. We take learning and ...

Elsevier Science Bv2016

Neuromodulated-Spike-Timing-Dependent Pasticity, and Theory of Three-Factor Learning Rules

Wulfram Gerstner, Nicolas Frémaux

Classical Hebbian learning puts the emphasis on joint pre- and postsynaptic activity, but neglects the potential role of neuromodulators. Since neuromodulators convey information about novelty or reward, the influence of neuromodulators on synaptic plastic ...

Frontiers Research Foundation2016

Synaptic consolidation: from synapses to behavioral modeling

Wulfram Gerstner, Friedemann Zenke, Lorric Ziegler, David Barak Kastner

Synaptic plasticity, a key process for memory formation, manifests itself across different time scales ranging from a few seconds for plasticity induction up to hours or even years for consolidation and memory retention. We developed a three-layered model ...

Society for Neuroscience2015

Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks

Wulfram Gerstner, Friedemann Zenke

Synaptic plasticity, the putative basis of learning and memory formation, manifests in various forms and across different timescales. Here we show that the interaction of Hebbian homosynaptic plasticity with rapid non-Hebbian heterosynaptic plasticity is, ...

Nature Publishing Group2015

A biologically plausible 3-factor learning rule for expectation maximization in reinforcement learning and decision making

Wulfram Gerstner, Kerstin Preuschoff, Mohammadjavad Faraji

2015

Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models

Wulfram Gerstner, Richard Naud, Skander Mensi, Christian Antonio Pozzorini, Olivier Richard Hagens

Single-neuron models are useful not only for studying the emergent properties of neural circuits in large-scale simulations, but also for extracting and summarizing in a principled way the information contained in electrophysiological recordings. Here we d ...

Public Library of Science2015

Optimal Control of Transient Dynamics in Balanced Networks Supports Generation of Complex Movements

Wulfram Gerstner, Tim Vogels, Guillaume Hennequin

Populations of neurons in motor cortex engage in complex transient dynamics of large amplitude during the execution of limb movements. Traditional network models with stochastically assigned synapses cannot reproduce this behavior. Here we introduce a clas ...

Cell Press2014

Stochastic variational learning in recurrent spiking networks

Wulfram Gerstner

The ability to learn and perform statistical inference with biologically plausible recurrent networks of spiking neurons is an important step toward understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent sp ...

Frontiers Research Foundation2014

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A biologically plausible 3-factor learning rule for expectation maximization in reinforcement learning and decision making

Wulfram Gerstner, Kerstin Preuschoff, Mohammadjavad Faraji

2015