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Wulfram Gerstner

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Publications associées (129)

Simplification of neural network models

Wulfram Gerstner, Henry Markram, Christian Antonio Pozzorini, James Gonzalo King, Eilif Benjamin Muller, Csaba Erö, Idan Segev, Marc-Oliver Gewaltig, Willem Anna Mark Wybo, Christian Andreas Rössert

The simplification of neural network models is described. For example, a method for simplifying a neural network model includes providing the neural network model to be simplified, defining a first temporal filter for the conveyance of input from a neuron ...

2018

Integrating Hebbian and homeostatic plasticity: the current state of the field and future research directions

Wulfram Gerstner, Taro Toyoizumi, Lu Chen

We summarize here the results presented and subsequent discussion from the meeting on Integrating Hebbian and Homeostatic Plasticity at the Royal Society in April 2016. We first outline the major themes and results presented at the meeting. We next provide ...

Royal Soc2017

Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size

Wulfram Gerstner, Moritz Deger, Tilo Schwalger

Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacti ...

Public Library of Science2017

Cortical Dynamics in Presence of Assemblies of Densely Connected Weight-Hub Neurons

Wulfram Gerstner, Hesam Setareh, Moritz Deger

Experimental measurements of pairwise connection probability of pyramidal neurons together with the distribution of synaptic weights have been used to construct randomly connected model networks. However, several experimental studies suggest that both wiri ...

Frontiers Research Foundation2017

Exponentially long orbits in Hopfield neural networks

Wulfram Gerstner, Samuel Pavio Muscinelli, Johanni Michael Brea

We show that Hopfield neural networks with synchronous dynamics and asymmetric weights admit stable orbits that form sequences of maximal length. For N units, these sequences have length T = 2^N; that is, they cover the full state space. We present a mathe ...

Massachusetts Institute of Technology Press2017

Hebbian plasticity requires compensatory processes on multiple timescales

Wulfram Gerstner, Friedemann Zenke

We review a body of theoretical and experimental research on Hebbian and homeostatic plasticity, starting from a puzzling observation: while homeostasis of synapses found in experiments is a slow compensatory process, most mathematical models of synaptic p ...

Royal Soc2017

Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network

Wulfram Gerstner, Aditya Gilra

The brain needs to predict how the body reacts to motor commands, but how a network of spiking neurons can learn non-linear body dynamics using local, online and stable learning rules is unclear. Here, we present a supervised learning scheme for the feedfo ...

Elife Sciences Publications Ltd2017

Biological Learning: Synaptic Plasticity, Hebb Rule and Spike Timing Dependent Plasticity

Wulfram Gerstner

Springer Science+Business Media2017

Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation

Wulfram Gerstner

The development of sensory receptive fields has been modeled in the past by a variety of models including normative models such as sparse coding or independent component analysis and bottom-up models such as spike-timing dependent plasticity or the Bienen- ...

Public Library of Science2016

A Model of Synaptic Reconsolidation

Wulfram Gerstner, Lorric Ziegler, Tilo Schwalger, David Barak Kastner

Reconsolidation of memories has mostly been studied at the behavioral and molecular level. Here, we put forward a simple extension of existing computational models of synaptic consolidation to capture hippocampal slice experiments that have been interprete ...

Frontiers Research Foundation2016

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

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, Lorric Ziegler, Friedemann Zenke, 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, Mohammadjavad Faraji, Kerstin Preuschoff

2015

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

Wulfram Gerstner, Skander Mensi, Christian Antonio Pozzorini, Richard Naud, 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, Guillaume Hennequin, Tim Vogels

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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