Home

Person

Wulfram Gerstner

This person has left EPFL

About
Privacy
Disclaimer

Graph Chatbot

Related publications (124)

Page 4 of 7

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

Wulfram Gerstner, Tilo Schwalger, Moritz Deger

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

Competition between cue response and place response: A model of rat navigation behaviour

Wulfram Gerstner, Ricardo Andres Chavarriaga Lozano, Thomas Strösslin

Different neural systems are involved in animal navigation depending on the type of task. Experimental studies support the idea that the hippocampus is necessary to learn a spatial representation required to navigate toward hidden goals (place response), w ...

Taylor & Francis2005

Algorithmic Composition of Melodies with Deep Recurrent Neural Networks

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

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

An exact mapping from ReLU networks to spiking neural networks

Wulfram Gerstner, Stanislaw Andrzej Wozniak, Angeliki Pantazi, Guillaume Emmanuel Fernand Bellec, Ana Stanojevic

Deep spiking neural networks (SNNs) offer the promise of low-power artificial intelligence. However, training deep SNNs from scratch or converting deep artificial neural networks to SNNs without loss of performance has been a challenge. Here we propose an ...

2023

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

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

Wulfram Gerstner, Moritz Deger, Hesam Setareh

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

Changing the responses of cortical neurons from sub- to suprathreshold using single spikes in vivo

Wulfram Gerstner

Action Potential (APs) patterns of sensory cortex neurons encode a variety of stimulus features, but how can a neuron change the feature to which it responds? Here, we show that in vivo a spike-timing-dependent plasticity (STDP) protocol-consisting of pair ...

Elife Sciences Publications Ltd2013

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

Optimal Spike-Timing Dependent Plasticity for Precise Action Potential Firing in Supervised Learning

Wulfram Gerstner, Jean-Pascal Théodor Pfister

In timing-based neural codes, neurons have to emit action potentials at precise moments in time. We use a supervised learning paradigm to derive a synaptic update rule that optimizes by gradient ascent the likelihood of postsynaptic firing at one or severa ...

Massachusetts Institute of Technology Press2006

Coding and Decoding with Adapting Neurons: A Population Approach to the Peri-Stimulus Time Histogram

Wulfram Gerstner, Richard Naud

The response of a neuron to a time-dependent stimulus, as measured in a Peri-Stimulus-Time-Histogram (PSTH), exhibits an intricate temporal structure that reflects potential temporal coding principles. Here we analyze the encoding and decoding of PSTHs for ...

Public Library of Science2012

Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks

Wulfram Gerstner, Friedemann Zenke, Tim Vogels

Cortical neurons receive balanced excitatory and inhibitory synaptic currents. Such a balance could be established and maintained in an experience-dependent manner by synaptic plasticity at inhibitory synapses. We show that this mechanism provides an expla ...

American Association for the Advancement of Science2011

Voltage and spike timing interact in STDP – a unified model

Wulfram Gerstner

2010

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

Phenomenological models of synaptic plasticity based on spike timing

Wulfram Gerstner

Synaptic plasticity is considered to be the biological substrate of learning and memory. In this document we review phenomenological models of short-term and long-term synaptic plasticity, in particular spike-timing dependent plasticity (STDP). The aim of ...

Springer Verlag2008

Computational models of intrinsic motivation for curiosity and creativity

Wulfram Gerstner, Alireza Modirshanechi, Sophia Becker

We link Ivancovsky et al.'s novelty-seeking model (NSM) to computational models of intrinsically motivated behavior and learning. We argue that dissociating different forms of curiosity, creativity, and memory based on the involvement of distinct intrinsic ...

2024

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

High-performance deep spiking neural networks with 0.3 spikes per neuron

Wulfram Gerstner, Stanislaw Andrzej Wozniak, Guillaume Emmanuel Fernand Bellec, Ana Stanojevic, Angeliki Pantazi

Communication by rare, binary spikes is a key factor for the energy efficiency of biological brains. However, it is harder to train biologically-inspired spiking neural networks than artificial neural networks. This is puzzling given that theoretical resul ...

Springer Science and Business Media LLC2024

Functional Requirements for Reward-Modulated Spike-Timing-Dependent Plasticity

Wulfram Gerstner, Nicolas Frémaux

Recent experiments have shown that spike-timing-dependent plasticity is influenced by neuromodulation. We derive theoretical conditions for successful learning of reward-related behavior for a large class of learning rules where Hebbian synaptic plasticity ...

Society for Neuroscience2010

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

Context selectivity with dynamic availability enables lifelong continual learning

Wulfram Gerstner, Guillaume Emmanuel Fernand Bellec, Martin Louis Lucien Rémy Barry

“You never forget how to ride a bike”, – but how is that possible? The brain is able to learn complex skills, stop the practice for years, learn other skills in between, and still retrieve the original knowledge when necessary. The mechanisms of this capab ...

Elsevier BV2025