W. Gerstner

Christian Pellegrini, W. Gerstner, Holger Bosch, 1999, ICONIP'99. ANZIIS'99 & ANNES'99 & ACNN'99. 6th International Conference on Neural Information Processing. Proceedings (Cat. No.99EX378).

25th Annual Computational Neuroscience Meeting: CNS-2016

Morten L. Kringelbach, Majid Nili Ahmadabadi, Anders M. Dale, 2016, BMC Neuroscience.

Learning Navigational Maps Through Potentiation and Modulation of Hippocampal Place Cells

Wulfram Gerstner, L. F. Abbott, L. Abbott, 2004, Journal of Computational Neuroscience.

Attractor Network Dynamics Enable Preplay and Rapid Path Planning in Maze-like Environments

Wulfram Gerstner, Dane S. Corneil, W. Gerstner, 2015, NIPS.

Learning to Generate Music with BachProp pdf

Wulfram Gerstner, Johanni Brea, Florian Colombo, 2018, ArXiv.

A General Model of Music Composition

Wulfram Gerstner, Florian Colombo, W. Gerstner, 2018 .

BachProp: Learning to Compose Music in Multiple Styles pdf

Wulfram Gerstner, Florian Colombo, W. Gerstner, 2018, ArXiv.

The accuracy of the population vector estimate in networks of integrate-and-fire type neurons using stationary and transient stimuli

Wulfram Gerstner, Mona Spiridon, W. Gerstner, 2001, Neurocomputing.

Effect of lateral connections on the accuracy of the population code for a network of spiking neurons.

W Gerstner, W. Gerstner, M. Spiridon, 2001, Network.

Coding properties of spiking neurons: reverse and cross-correlations

Wulfram Gerstner, W. Gerstner, 2001, Neural Networks.

Theory and Simulation in Neuroscience

Wulfram Gerstner, Gustavo Deco, Henning Sprekeler, 2012, Science.

A Model of Synaptic Reconsolidation

Wulfram Gerstner, Tilo Schwalger, David B. Kastner, 2016, Front. Neurosci..

From Stochastic Nonlinear Integrate-and-Fire to Generalized Linear Models

Wulfram Gerstner, Skander Mensi, Richard Naud, 2011, NIPS.

Wulfram Gerstner, Felipe Gerhard, Skander Mensi, 2011, Neural Computation.

Adaptive sensory processing for efficient place coding

Ricardo Chavarriaga, Wulfram Gerstner, Thomas Strösslin, 2006, Neurocomputing.

Spike-Timing-Dependent Plasticity: A Comprehensive Overview

W. Gerstner, H. Markram, P. J. Sjöström, 2012, Front. Syn. Neurosci..

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

Wulfram Gerstner, Moritz Deger, Alexander Seeholzer, 2018, bioRxiv.

Efficient low-dimensional approximation of continuous attractor networks

Wulfram Gerstner, Moritz Deger, Alexander Seeholzer, 2017, 1711.08032.

Modeling spatial and temporal aspects of visual backward masking.

Wulfram Gerstner, Frouke Hermens, Udo Ernst, 2008, Psychological review.

Intrinsic Stabilization of Output Rates by Spike-Based Hebbian Learning

Wulfram Gerstner, Richard Kempter, J. Leo van Hemmen, 2001, Neural Computation.

One-shot learning and behavioral eligibility

W. Gerstner, M. Herzog, K. Preuschoff, 2019 .

Spike-timing prediction in cortical neurons with active dendrites

Wulfram Gerstner, Richard Naud, Brice Bathellier, 2014, Front. Comput. Neurosci..

When shared concept cells support associations: Theory of overlapping memory engrams

R. Quiroga, W. Gerstner, T. Schwalger, 2021, bioRxiv.

Mesoscopic population equations for spiking neural networks with synaptic short-term plasticity

Wulfram Gerstner, Tilo Schwalger, Valentin Schmutz, 2018, The Journal of Mathematical Neuroscience.

Excitable neuronal assemblies with adaptation as a building block of brain circuits for velocity-controlled signal propagation

Wulfram Gerstner, Moritz Deger, Hesam Setareh, 2018, PLoS Comput. Biol..

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

Wulfram Gerstner, Tilo Schwalger, Moritz Deger, 2016, PLoS Comput. Biol..

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

Wulfram Gerstner, Richard Naud, W. Gerstner, 2012, PLoS Comput. Biol..

From spiking neurons to rate models: a cascade model as an approximation to spiking neuron models with refractoriness.

Wulfram Gerstner, Yuval Aviel, W. Gerstner, 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

A biologically motivated and analytically soluble model of collective oscillations in the cortex

Wulfram Gerstner, J. Leo van Hemmen, Raphael Ritz, 1993, Biological Cybernetics.

Universality in neural networks: the importance of the ‘mean firing rate’

Wulfram Gerstner, J. Leo van Hemmen, W. Gerstner, 1992, Biological Cybernetics.

Integrate-and-Fire Neurons and Networks

W. Gerstner, 2002 .

Noise in Integrate-and-Fire Neurons: From Stochastic Input to Escape Rates

Wulfram Gerstner, Hans Ekkehard Plesser, W. Gerstner, 2000, Neural Computation.

Population Dynamics of Spiking Neurons: Fast Transients, Asynchronous States, and Locking

Wulfram Gerstner, W. Gerstner, 2000, Neural Computation.

Frequency Spectrum of Coupled Stochastic Neurons with Refractoriness

Wulfram Gerstner, Carson C. Chow, Mona Spiridon, 1998 .

Coding and information processing in neural networks

Wulfram Gerstner, J. Leo van Hemmen, W. Gerstner, 1994 .

Working memory facilitates reward-modulated Hebbian learning in recurrent neural networks pdf

Wulfram Gerstner, Roman Pogodin, Dane Corneil, 2019, ArXiv.

A vision-driven model of hippocampal place cells and temporally asymmetric LTP-induction for action learning

Wulfram Gerstner, Angelo Arleo, W. Gerstner, 1999 .

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

Ricardo Chavarriaga, Wulfram Gerstner, Thomas Strösslin, 2005, Connect. Sci..

Place Cells and Spatial Navigation Based on 2D Visual Feature Extraction, Path Integration, and Reinforcement Learning

Angelo Arleo, Wulfram Gerstner, Fabrizio Smeraldi, 2000, NIPS.

Modeling rodent head-direction cells and place cells for spatial learning in bio-mimetic robotics

Angelo Arleo, Wulfram Gerstner, W. Gerstner, 2000 .

What Matters in Neuronal Locking?

Wulfram Gerstner, Jack D. Cowan, J. Leo van Hemmen, 1996, Neural Computation.

Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation

Wulfram Gerstner, Johanni Brea, Dane S. Corneil, 2018, ICML.

Neuro-mimetic navigation systems: A computational model of the rat Hippocampus

Wulfram Gerstner, Angelo Arleo, W. Gerstner, 1999 .

Paradoxical Evidence Integration in Rapid Decision Processes

Wulfram Gerstner, Michael H. Herzog, Henning Sprekeler, 2012, PLoS Comput. Biol..

Neural codes: firing rates and beyond.

A V Herz, W Gerstner, A K Kreiter, 1997, Proceedings of the National Academy of Sciences of the United States of America.

The quantitative single-neuron modeling competition

Wulfram Gerstner, Thomas K. Berger, Richard Naud, 2008, Biological Cybernetics.

Extracting non-linear integrate-and-fire models from experimental data using dynamic I–V curves

Wulfram Gerstner, Thomas K. Berger, Carl C. H. Petersen, 2008, Biological Cybernetics.

A benchmark test for a quantitative assessment of simple neuron models

Wulfram Gerstner, Richard Naud, Ryota Kobayashi, 2008, Journal of Neuroscience Methods.

Chapter 12 A framework for spiking neuron models: The spike response model

W. Gerstner, W. Gerstner, 2001 .

Why spikes? Hebbian learning and retrieval of time-resolved excitation patterns

Wulfram Gerstner, Raphael Ritz, W. Gerstner, 2004, Biological Cybernetics.

Fluctuations and information filtering in coupled populations of spiking neurons with adaptation.

Wulfram Gerstner, Richard Naud, Tilo Schwalger, 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

How to Describe Neuronal Activity: Spikes, Rates, or Assemblies?

Wulfram Gerstner, J. Leo van Hemmen, W. Gerstner, 1993, NIPS.

Predicting neuronal activity with simple models of the threshold type: Adaptive Exponential Integrate-and-Fire model with two compartments

Wulfram Gerstner, Claudia Clopath, Hans-Rudolf Lüscher, 2007, Neurocomputing.

Reduction of the Hodgkin-Huxley Equations to a Single-Variable Threshold Model

Wulfram Gerstner, J. Leo van Hemmen, Werner M. Kistler, 1997, Neural Computation.

Generalized integrate-and-fire models of neuronal activity approximate spike trains of a detailed model to a high degree of accuracy.

Wulfram Gerstner, Renaud Jolivet, Timothy J Lewis, 2004, Journal of neurophysiology.

One-shot learning and behavioral eligibility traces in sequential decision making

Kerstin Preuschoff, Wulfram Gerstner, Michael Herzog, 2017, eLife.

Balancing New against Old Information: The Role of Puzzlement Surprise in Learning

Kerstin Preuschoff, Wulfram Gerstner, Mohammad Javad Faraji, 2018, Neural Computation.

Reward-based learning under hardware constraints—using a RISC processor embedded in a neuromorphic substrate

Johannes Schemmel, Karlheinz Meier, Wulfram Gerstner, 2013, Front. Neurosci..

The Performance (and Limits) of Simple Neuron Models: Generalizations of the Leaky Integrate-and-Fire Model

Wulfram Gerstner, Richard Naud, W. Gerstner, 2012 .

Dependence of the spike-triggered average voltage on membrane response properties

Wulfram Gerstner, Laurent Badel, Magnus J. E. Richardson, 2006, Neurocomputing.

Synaptic Shot Noise and Conductance Fluctuations Affect the Membrane Voltage with Equal Significance

Wulfram Gerstner, Magnus J. E. Richardson, W. Gerstner, 2005, Neural Computation.

Reinforcement Learning in Continuous State and Action Space

Wulfram Gerstner, Thomas Strösslin, W. Gerstner, 2003 .

Spatial reorientation and environment geometry : extraction of global parameters from local visual features

W. Gerstner, Ricardo Chavarriaga, D. Sheynikhovich, 2007 .

Poisson-like neuronal firing due to multiple synfire chains in simultaneous action

Wulfram Gerstner, J. Leo van Hemmen, Raphael Ritz, 1997 .

Limits to high-speed simulations of spiking neural networks using general-purpose computers

Wulfram Gerstner, Friedemann Zenke, W. Gerstner, 2014, Front. Neuroinform..

The influence of structure on the response properties of biologically plausible neural network models

Wulfram Gerstner, Michael Avermann, Tim Vogels, 2011, BMC Neuroscience.

Consciousness & the small network argument

Wulfram Gerstner, Michael H. Herzog, Michael Esfeld, 2007, Neural Networks.

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

Wulfram Gerstner, Skander Mensi, Christian Pozzorini, 2016, PLoS Comput. Biol..

Stable Propagation of Activity Pulses in Populations of Spiking Neurons

Wulfram Gerstner, Werner M. Kistler, W. Gerstner, 2002, Neural Computation.

Rapid suppression and sustained activation of distinct cortical regions for a delayed sensory-triggered motor response

Georgios P. Foustoukos, W. Gerstner, K. Tamura, 2020, Neuron.

Beyond Pair-Based STDP: a Phenomenological Rule for Spike Triplet and Frequency Effects

Jean-Pascal Pfister, Wulfram Gerstner, W. Gerstner, 2005, NIPS.

Why spikes? Hebbian learning and retrieval of time-resolved excitation patterns

Wulfram Gerstner, J. Leo van Hemmen, Raphael Ritz, 1993, Biological Cybernetics.

Short-Term Synaptic Plasticity Orchestrates the Response of Pyramidal Cells and Interneurons to Population Bursts

Wulfram Gerstner, Henry Markram, Gilad Silberberg, 2005, Journal of Computational Neuroscience.

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

Wulfram Gerstner, Carl C. H. Petersen, Moritz Deger, 2017, Front. Comput. Neurosci..

Modeling directional firing properties of place cells

Ricardo Chavarriaga, Wulfram Gerstner, Eric L. Sauser, 2003 .

Extraction of Network Topology From Multi-Electrode Recordings: Is there a Small-World Effect?

Wulfram Gerstner, Gordon Pipa, Felipe Gerhard, 2011, Front. Comput. Neurosci..

The Silent Period of Evidence Integration in Fast Decision Making

Wulfram Gerstner, Michael H. Herzog, Henning Sprekeler, 2013, PloS one.

A biologically motivated and analytically soluble model of collective oscillations in the cortex

Wulfram Gerstner, J. Leo van Hemmen, Raphael Ritz, 2004, Biological Cybernetics.

Artificial Neural Networks — ICANN'97

Wulfram Gerstner, Martin Hasler, Jean-Daniel Nicoud, 1997, Lecture Notes in Computer Science.

Expand-and-Cluster: Exact Parameter Recovery of Neural Networks

W. Gerstner, Johanni Brea, Berfin Simsek, 2023, ArXiv.

Effect of lateral connections on the accuracy of the population code for a network of spiking neurons

W. Gerstner, M. Spiridon, 2001 .

Noise spectrum and signal transmission through a population of spiking neurons.

W Gerstner, W. Gerstner, M. Spiridon, 1999, Network.

Noise-enhanced computation in a model of a cortical column

Wulfram Gerstner, Julien Mayor, W. Gerstner, 2005, Neuroreport.

Computational models of episodic-like memory in food-caching birds

W. Gerstner, N. Clayton, Johanni Brea, 2023, Nature communications.

Noise and the PSTH Response to Current Transients: II. Integrate-and-Fire Model with Slow Recovery and Application to Motoneuron Data

Wulfram Gerstner, Alix Herrmann, W. Gerstner, 2002, Journal of Computational Neuroscience.

Effect of noise on neuron transient response

Wulfram Gerstner, Alix Herrmann, W. Gerstner, 2000, Neurocomputing.

Balancing New Against Old Information: The Role of Surprise pdf

Kerstin Preuschoff, Wulfram Gerstner, Mohammad Javad Faraji, 2016, ArXiv.

Learning in Volatile Environments With the Bayes Factor Surprise

Wulfram Gerstner, Johanni Brea, Vasiliki Liakoni, 2021, Neural Computation.

The curse of optimism: a persistent distraction by novelty

W. Gerstner, M. Herzog, Alireza Modirshanechi, 2023, bioRxiv.

Are training trajectories of deep single-spike and deep ReLU network equivalent?

W. Gerstner, A. Pantazi, G. Cherubini, 2023, ArXiv.

Approximating Relu Networks by Single-Spike Computation

W. Gerstner, E. Eleftheriou, Stanisław Woźniak, 2022, 2022 IEEE International Conference on Image Processing (ICIP).

On the choice of metric in gradient-based theories of brain function

Wulfram Gerstner, Johanni Brea, Simone Carlo Surace, 2018, PLoS Comput. Biol..

Connection-type-specific biases make uniform random network models consistent with cortical recordings

Wulfram Gerstner, Michael Avermann, Carl Petersen, 2014, Journal of neurophysiology.

network models consistent with cortical recordings Connection-type-specific biases make uniform random

W. Gerstner, C. Petersen, M. Avermann, 2014 .

Spike-response model

Wulfram Gerstner, W. Gerstner, 2008, Scholarpedia.

Associative Memory in a Network of 'Biological' Neurons

Wulfram Gerstner, W. Gerstner, 1990, NIPS.

GateON: an unsupervised method for large scale continual learning

W. Gerstner, G. Bellec, M. Barry, 2023, ArXiv.

Escape rate models for noisy integrate-and-free neurons

Wulfram Gerstner, Hans Ekkehard Plesser, W. Gerstner, 2000, Neurocomputing.

Extracting Oscillations: Neuronal Coincidence Detection with Noisy Periodic Spike Input

Wulfram Gerstner, Richard Kempter, J. Leo van Hemmen, 1998, Neural Computation.

Algorithmic Composition of Melodies with Deep Recurrent Neural Networks pdf

Wulfram Gerstner, Samuel P. Muscinelli, Johanni Brea, 2016, ArXiv.

Deep Artificial Composer: A Creative Neural Network Model for Automated Melody Generation

Wulfram Gerstner, Alexander Seeholzer, Florian Colombo, 2017, EvoMUSART.

Time structure of the activity in neural network models.

W. Gerstner, Gerstner, 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

Temporal coding in the sub-millisecond range: Model of barn owl auditory pathway

Wulfram Gerstner, Richard Kempter, J. Leo van Hemmen, 1995, NIPS.

Stress, noradrenaline, and realistic prediction of mouse behaviour using reinforcement learning

Wulfram Gerstner, Carmen Sandi, Gediminas Luksys, 2008, NIPS.

Surprise and novelty in the brain

W. Gerstner, Johanni Brea, Alireza Modirshanechi, 2023, Current Opinion in Neurobiology.

One-shot learning and behavioral eligibility traces in sequential decision making

W. Gerstner, M. Herzog, K. Preuschoff, 2017, eLife.

Novelty is not surprise: Human exploratory and adaptive behavior in sequential decision-making

W. Gerstner, M. Herzog, Alireza Modirshanechi, 2021, PLoS Comput. Biol..

An Approximate Bayesian Approach to Surprise-Based Learning pdf

Wulfram Gerstner, Johanni Brea, Vasiliki Liakoni, 2019, ArXiv.

Spike-timing dependent plasticity

W. Gerstner, Jesper Sjöström, J. Sjöström, 2010, Scholarpedia.

Correction: Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail

W. Senn, W. Gerstner, R. Urbanczik, 2009, PLoS Computational Biology.

Slowness Learning: Mathematical Approaches and Synaptic Mechanisms

I Mathematisch-NaturwissenschaftlichenFakultät, Laurenz Wiskott, R. Kempter, 2008 .

Coherence and incoherence in a globally coupled ensemble of pulse-emitting units.

W. Gerstner, van Hemmen JL, Gerstner, 1993, Physical review letters.

Convergence of redundancy-free spiking neural networks to rate networks

W. Gerstner, Johanni Brea, Valentin Schmutz, 2023, 2303.05174.

Trial matching: capturing variability with data-constrained spiking neural networks

W. Gerstner, C. Petersen, G. Bellec, 2023, 2306.03603.

Fitting summary statistics of neural data with a differentiable spiking network simulator pdf

Wulfram Gerstner, Johanni Brea, Guillaume Bellec, 2021, NeurIPS.

A new kernel clustering algorithm

W. Gerstner, S. Borer, W. Gerstner, 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

A Bayesian kernel for the prediction of neuron properties from binary gene profiles

Wulfram Gerstner, François Fleuret, W. Gerstner, 2005, Fourth International Conference on Machine Learning and Applications (ICMLA'05).

Noninvasive brain-actuated control of a mobile robot by human EEG

J. Millán, W. Gerstner, J. Mouriño, 2004, IEEE Transactions on Biomedical Engineering.

Combining visual and proprioceptive information in a model of spatial learning and navigation

R. Chavarriaga, W. Gerstner, W. Gerstner, 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

A computational model of parallel navigation systems in rodents

W. Gerstner, Ricardo Chavarriaga, D. Sheynikhovich, 2007, Neuroinformatics.

A Computational Model of Parallel Navigation Systems in Rodents

Ricardo Chavarriaga, Wulfram Gerstner, Thomas Strösslin, 2005 .

Dynamic Noise Annealing for Learning Temporal Sequences with Recurrent Neural Networks

Wulfram Gerstner, Pierre-Edouard Sottas, W. Gerstner, 2002, ICANN.

Rescaling, thinning or complementing? On goodness-of-fit procedures for point process models and Generalized Linear Models

Wulfram Gerstner, Felipe Gerhard, W. Gerstner, 2010, NIPS.

How Good Are Neuron Models?

W. Gerstner, Richard Naud, R. Naud, 2009, Science.

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

Kerstin Preuschoff, Wulfram Gerstner, Mohammadjavad Faraji, 2015 .

Connectivity reflects coding: a model of voltage-based STDP with homeostasis

W. Gerstner, C. Clopath, Lars Büsing, 2010, Nature Neuroscience.

Predicting spike times of a detailed conductance-based neuron model driven by stochastic spike arrival

Wulfram Gerstner, Renaud Jolivet, W. Gerstner, 2004, Journal of Physiology-Paris.

Title: Dynamic I-v Curves Are Reliable Predictors of Naturalistic Pyramidal-neuron Voltage Traces Dynamic I-v Curves of Layer-5 Pyramidal Cells

W. Gerstner, C. Petersen, M. J. Richardson, 2008 .

Title : Dynamic I-V curves are reliable predictors of naturalistic pyramidal-neuron voltage traces

W. Gerstner, C. Petersen, M. J. Richardson, 2007 .

Emergence of spatiotemporal receptive fields and its application to motion detection

Wulfram Gerstner, J. Leo van Hemmen, Stefan Wimbauer, 1994, Biological Cybernetics.

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

Henning Sprekeler, W. Gerstner, T. Vogels, 2011, Science.

Effects of Stress and Genotype on Meta-parameter Dynamics in Reinforcement Learning

Wulfram Gerstner, Denis Sheynikhovich, Carmen Sandi, 2006, NIPS.

The Human Brain Project A Report to the European Commission

H. Markram, W. Gerstner, S. Dehaene, 2022 .

Non-invasive brain-actuated control of a mobile robot

W. Gerstner, J. Mouriño, Frédéric Renkens, 2003, IJCAI 2003.

Spatial orientation in navigating agents: Modeling head-direction cells

Angelo Arleo, Wulfram Gerstner, W. Gerstner, 2001, Neurocomputing.

Multi-Timescale Memory Dynamics Extend Task Repertoire in a Reinforcement Learning Network With Attention-Gated Memory

Wulfram Gerstner, Aditya Gilra, Marco Martinolli, 2017, Front. Comput. Neurosci..

Towards deep learning with spiking neurons in energy based models with contrastive Hebbian plasticity pdf

Wulfram Gerstner, Johanni Brea, Thomas Mesnard, 2016, ArXiv.

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

W. Gerstner, J. Pfister, David Barber, 2005, Neural Computation.

Consciousness & the small network argument

W. Gerstner, M. Herzog, M. Esfeld, 2007, Neural Networks.

Gamma Oscillations in a Nonlinear Regime: A Minimal Model Approach Using Heterogeneous Integrate-and-Fire Networks

Wulfram Gerstner, Alan Carleton, Brice Bathellier, 2008, Neural Computation.

Curiosity-driven exploration: foundations in neuroscience and computational modeling

W. Gerstner, Alireza Modirshanechi, Sebastian Haesler, 2023, Trends in Neurosciences.

Local plasticity rules can learn deep representations using self-supervised contrastive predictions

W. Gerstner, G. Bellec, Bernd Illing, 2020, NeurIPS.

Fast Propagation of Firing Rates through Layered Networks of Noisy Neurons

Mark C. W. van Rossum, G. Turrigiano, W. Gerstner, 2002 .

Brain-actuated interaction

J. Millán, W. Gerstner, J. Mouriño, 2004, Artif. Intell..

Generalized integrate-and-fire models of neuronal activity approximate spike trains of a detailed model to a high degree of accuracy.

W. Gerstner, R. Jolivet, T. J. Lewis, 2004, Journal of neurophysiology.

Optimality Model of Unsupervised Spike-Timing-Dependent Plasticity: Synaptic Memory and Weight Distribution

W. Gerstner, J. Pfister, K. Aihara, 2007, Neural Computation.

2005 Special issue: Robust self-localisation and navigation based on hippocampal place cells

W. Gerstner, Ricardo Chavarriaga, D. Sheynikhovich, 2005 .

Context selectivity with dynamic availability enables lifelong continual learning

W. Gerstner, G. Bellec, M. Barry, 2023, 2306.01690.

Distributed and specific encoding of sensory, motor, and decision information in the mouse neocortex during goal-directed behavior.

W. Gerstner, S. Crochet, Vahid Esmaeili, 2023, Cell reports.

Spike-timing dependent plasticity

W. Gerstner, W. Gerstner, J. Sjöström, 2011 .

Perceptual learning, roving and the unsupervised bias

Henning Sprekeler, W. Gerstner, Nicolas Frémaux, 2012, Vision Research.

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

W. Gerstner, A. Pantazi, G. Cherubini, 2023, Nature Communications.

Representational similarity modulates neural and behavioral signatures of novelty

W. Gerstner, Alireza Modirshanechi, Sophia Becker, 2024, bioRxiv.