Wulfram Gerstner

发表

Spatial cognition and neuro-mimetic navigation: a model of hippocampal place cell activity

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

Exponentially Long Orbits in Hopfield Neural Networks

Wulfram Gerstner, Samuel P. Muscinelli, Johanni Brea, 2017, Neural Computation.

Efficient modeling of neural activity using coupled renewal processes

Wulfram Gerstner, Felipe Gerhard, 2011, BMC Neuroscience.

Voltage Recordings and Genetic Algorithms Constraining Compartmental Models Using Multiple

Wulfram Gerstner, Noam Peled, Alon Korngreen, 2015 .

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.

Bridging spiking neuron models and mesoscopic population models - a general theory for neural population dynamics

Wulfram Gerstner, Tilo Schwalger, Moritz Deger, 2015, BMC Neuroscience.

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

Wulfram Gerstner, Aditya Gilra, W. Gerstner, 2017, eLife.

A model of perceptual discrimination under sequential sensory evidence

Wulfram Gerstner, Gustavo Deco, Ranulfo Romo, 2013, BMC Neuroscience.

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

Kerstin Preuschoff, Wulfram Gerstner, Mohammadjavad Faraji, 2015 .

Noisy input models: barrage of spike arrivals

Wulfram Gerstner, Richard Naud, Werner M. Kistler, 2014 .

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

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

Spike-timing dependent plasticity

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

Cognitive navigation based on nonuniform Gabor space sampling, unsupervised growing networks, and reinforcement learning

Angelo Arleo, Wulfram Gerstner, Fabrizio Smeraldi, 2004, IEEE Transactions on Neural Networks.

A novel information theoretic measure of surprise

Kerstin Preuschoff, Wulfram Gerstner, Mohammadjavad Faraji, 2016 .

Learning Navigational Maps Through Potentiation and Modulation of Hippocampal Place Cells

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

Spiking Neuron Models: Signal transmission and neuronal coding

Wulfram Gerstner, Werner M. Kistler, 2002 .

Combining Multimodal Sensory Input for Spatial Learning

Angelo Arleo, Wulfram Gerstner, Thomas Strösslin, 2002, ICANN.

Baum-Welch Algorithm

Wulfram Gerstner, Pascal Poupart, Hannu Toivonen, 2009, Encyclopedia of Machine Learning and Data Mining.

Adaptive sensory processing for efficient place coding

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

Phenomenological models of synaptic plasticity based on spike timing

Wulfram Gerstner, Markus Diesmann, Abigail Morrison, 2008, Biological Cybernetics.

General conditions for spiking neurons and plasticity rules to perform independent component analysis

Wulfram Gerstner, Carlos SN Brito, 2011, BMC Neuroscience.

Stochastic variational learning in recurrent spiking networks

Wulfram Gerstner, Danilo Jimenez Rezende, W. Gerstner, 2014, Front. Comput. Neurosci..

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

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

A Computational Model of Parallel Navigation Systems in Rodents

Ricardo Chavarriaga, Wulfram Gerstner, Thomas Strösslin, 2005 .

SOME GENERALIZATIONS OF INTEGRATE-AND-FIRE MODELS

Wulfram Gerstner, Arnaud Tonnelier, 2003 .

Artificial Neural Networks — ICANN'97

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

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

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

Detailed neuron models

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

A General Model of Music Composition

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

What is Different with Spiking Neurons

Wulfram Gerstner, W. Gerstner, 2001 .

Escape rate models for noisy integrate-and-free neurons

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

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.

Spatial Representation and Navigation in a Bio-inspired Robot

Ricardo Chavarriaga, Wulfram Gerstner, Thomas Strösslin, 2005, Biomimetic Neural Learning for Intelligent Robots.

The quantitative single-neuron modeling competition

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

Surprise-based learning: neuromodulation by surprise in multi-factor learning rules

Kerstin Preuschoff, Wulfram Gerstner, Mohammadjavad Faraji, 2014 .

Stable Propagation of Activity Pulses in Populations of Spiking Neurons

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

Theory and Simulation in Neuroscience

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

Hebbian and non-Hebbian plasticity orchestrated to form and retrieve memories in spiking networks

Wulfram Gerstner, Everton J. Agnes, Friedemann Zenke, 2015 .

The quality of Coincidence detection and ITD-tuning: a theoretical framework

Wulfram Gerstner, Richard Kempter, J. L. van Hemmen, 1999 .

Consciousness & the small network argument

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

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

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

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.

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

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

Quantitative single-neuron modeling: competition 2008

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

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

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

Brain-actuated interaction

José del R. Millán, Wulfram Gerstner, Josep Mouriño, 2004, Artif. Intell..

Noise Modulation by Stochastic Neurons of the Integrate-and Fire Type

Wulfram Gerstner, Mona Spiridon, 1999, IWANN.

Noise-enhanced computation in a model of a cortical column

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

Bias Variance Decomposition

Wulfram Gerstner, Pascal Poupart, Hannu Toivonen, 2010, Encyclopedia of Machine Learning.

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

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

Localized random projections challenge benchmarks for bio-plausible deep learning

Wulfram Gerstner, Johanni Brea, Bernd Illing, 2018 .

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

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

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

Wulfram Gerstner, Dane Corneil, 2015, NIPS 2015.

Associative Memory in a Network of 'Biological' Neurons

Wulfram Gerstner, W. Gerstner, 1990, NIPS.

Special issue on quantitative neuron modeling

Wulfram Gerstner, Felix Schürmann, Walter Senn, 2008, 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.

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

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

Associative memory in a network of ‘spiking’ neurons

Wulfram Gerstner, J. L. van Hemmen, W. Gerstner, 1992 .

Optimal Hebbian Learning: A Probabilistic Point of View

Jean-Pascal Pfister, David Barber, Wulfram Gerstner, 2003, ICANN.

Stabilizing working memory in spiking networks with biologically plausible synaptic dynamics

Wulfram Gerstner, Moritz Deger, Alexander Seeholzer, 2014, BMC Neuroscience.

On the role of dimensionality in face authentication

Wulfram Gerstner, Fabrizio Smeraldi, Josef Bigun, 2000 .

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

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

Automated point-neuron simplification of data-driven microcircuit models

James G. King, Wulfram Gerstner, Henry Markram, 2016, 1604.00087.

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

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

Place Cells and Spatial Navigation based on Vision, Path Integration, and Reinforcement Learning

Angelo Arleo, Wulfram Gerstner, Fabrizio Smeraldi, 2001 .

Introduction: neurons and mathematics

Wulfram Gerstner, Richard Naud, Liam Paninski, 2014 .

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.

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

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

Nonlinear integrate-and-fire models

Wulfram Gerstner, Richard Naud, Liam Paninski, 2014 .

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

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

GENERALIZED INTEGRATE-AND-FIRE NEURONS

Wulfram Gerstner, Richard Naud, Werner M. Kistler, 2014 .

Spiking Neuron Models: An Introduction

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

Effect of noise on neuron transient response

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

Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector

Wulfram Gerstner, Guillaume Hennequin, Friedemann Zenke, 2013, PLoS Comput. Biol..

Support Vector Features and the Role of Dimensionality in Face Authentication

Wulfram Gerstner, Fabrizio Smeraldi, Josef Bigün, 2002, SVM.

Emergence of spatiotemporal receptive fields and its application to motion detection

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

Fast and richly structured activity in cortical networks with local inhibition

Wulfram Gerstner, Guillaume Hennequin, Tim Vogels, 2011, BMC Neuroscience.

Rapid path planning and preplay in maze{like environments using attractor networks

Wulfram Gerstner, Dane Corneil, 2015 .

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

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

Lecture Notes in Computer Science - Artificial Neural Networks - ICANN "97

Wulfram Gerstner, Jean-Daniel Nicoud, Alain Germond, 1997 .

Temporal Transition from Illusory Conjunctions to Object Recognition

Wulfram Gerstner, Andreas Lang, Tilo Schwalger, 2016 .

Support Vector Representation of Multi-categorical Data

Wulfram Gerstner, Silvio Borer, 2002, ICANN.

A Memory-Augmented Reinforcement Learning Model of Food Caching Behaviour in Birds

Wulfram Gerstner, Johanni Brea, 2019 .

Vertical signal flow and oscillations in a three-layer model of the cortex

Wulfram Gerstner, J. Leo van Hemmen, Raphael Ritz, 2004, Journal of Computational Neuroscience.

Firing patterns in the adaptive exponential integrate-and-fire model

Wulfram Gerstner, Claudia Clopath, Richard Naud, 2008, Biological Cybernetics.

Plasticity and stability in recurrent neural networks

Wulfram Gerstner, Guillaume Hennequin, Henning Sprekeler, 2011, BMC Neuroscience.

Learning to Generate Music with BachProp pdf

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

An Approximate Bayesian Approach to Surprise-Based Learning pdf

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

Non-linear motor control by local learning in spiking neural networks

Wulfram Gerstner, Aditya Gilra, W. Gerstner, 2017, ICML.

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

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

Frontiers in Synaptic Neuroscience Synaptic Neuroscience

Wulfram Gerstner, Claudia Clopath, W. Gerstner, 2022 .

Noise and the PSTH Response to Current Transients: I. General Theory and Application to the Integrate-and-Fire Neuron

Wulfram Gerstner, Alix Herrmann, W. Gerstner, 2001, Journal of Computational Neuroscience.

Non-Invasive Brain-Actuated Control of a Mobile Robot

José del R. Millán, Wulfram Gerstner, Josep Mouriño, 2003, IJCAI.

From Hebb Rules to Spike-Timing-Dependent Plasticity: A Personal Account

Wulfram Gerstner, W. Gerstner, 2010, Front. Syn. Neurosci..

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

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

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.

Estimating small-world topology of neural networks from multi-electrode recordings

Wulfram Gerstner, Gordon Pipa, Felipe Gerhard, 2010 .

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

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

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

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

Surprise minimization as a learning strategy in neural networks

Kerstin Preuschoff, Wulfram Gerstner, Mohammad Javad Faraji, 2015, BMC Neuroscience.

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

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

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

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

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

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

BachProp: Learning to Compose Music in Multiple Styles pdf

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

Neuronal Dynamics: From Single Neurons To Networks And Models Of Cognition

Wulfram Gerstner, Richard Naud, Liam Paninski, 2014 .

Understanding the PSTH response to synaptic input

Wulfram Gerstner, Alix Herrmann, 1999 .

Subthreshold cross-correlations between cortical neurons: A reference model with static synapses

Wulfram Gerstner, Henry Markram, Gilad Silberberg, 2005, Neurocomputing.

Spatial orientation in navigating agents: Modeling head-direction cells

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

Mathematical formulations of Hebbian learning

Wulfram Gerstner, Werner M. Kistler, W. Gerstner, 2002, Biological Cybernetics.

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

Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation

Wulfram Gerstner, Carlos S. N. Brito, W. Gerstner, 2016, PLoS Comput. Biol..

Coding properties of spiking neurons: reverse and cross-correlations

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

Encoding and decoding with stochastic neuron models

Wulfram Gerstner, Richard Naud, Liam Paninski, 2014 .

Extracting Oscillations: Neuronal Coincidence Detection with Noisy Periodic Spike Input

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

Faculty Opinions recommendation of Random synaptic feedback weights support error backpropagation for deep learning.

Wulfram Gerstner, 2017 .

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).

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

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

Modelling feature-integration in human vision with drift diffusion models

Wulfram Gerstner, Michael H. Herzog, Johannes Rüter, 2008 .

Artificial neural networks : ICANN '97 : 7th International Conference, Lausanne, Switzerland, October 8-10, 1997 : proceedings

Wulfram Gerstner, 1997 .

Predicting neuronal activity with an adaptive exponential integrate-and-fire model

Wulfram Gerstner, Claudia Clopath, Felix Schuermann, 2007, BMC Neuroscience.

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

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

Coding properties of spiking neurons: modeling reverse correlations.

Wulfram Gerstner, W. Gerstner, 2001 .

New support vector algorithms for multicategorical data applied to real-time object

Wulfram Gerstner, Silvio Borer, 2003 .

0 Hebbian Learning and Plasticity

Wulfram Gerstner, W. Gerstner, 2011 .

Coding and information processing in neural networks

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

Weight-space symmetry in neural network loss landscapes revisited

Wulfram Gerstner, Johanni Brea, Berfin Simsek, 2019 .

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

Jean-Pascal Pfister, Kazuyuki Aihara, Wulfram Gerstner, 2007, Neural Computation.

Motion detection in a Linsker network

Wulfram Gerstner, Stefan Wimbauer, J. L. van Hemmen, 1994 .

Spiking Neuron Models: Single Neurons, Populations, Plasticity

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

Spike-Based Compared to Rate-Based Hebbian Learning

Wulfram Gerstner, Richard Kempter, J. Leo van Hemmen, 1998, NIPS.

Online Processing of Multiple Inputs in a Sparsely-Connected Recurrent Neural Network

Wulfram Gerstner, Julien Mayor, 2003, ICANN.

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

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

Modulation of a decision-making process by spatiotemporal spike patterns decoding: evidence from spike-train metrics analysis and spiking neural network modeling

Angelo Arleo, Wulfram Gerstner, Emmanuel Procyk, 2013, BMC Neuroscience.

Does computational neuroscience need new synaptic learning paradigms?

Wulfram Gerstner, Johanni Brea, W. Gerstner, 2016, Current Opinion in Behavioral Sciences.

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..

Neuronal Dynamics: Fast transients and rate models

Wulfram Gerstner, Richard Naud, Liam Paninski, 2014 .

Proceedings of the 7th International Conference on Artificial Neural Networks

Wulfram Gerstner, Jean-Daniel Nicoud, Alain Germond, 1997 .

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

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

Robust self-localisation and navigation based on hippocampal place cells

Ricardo Chavarriaga, Wulfram Gerstner, Thomas Strösslin, 2005, Neural Networks.

Adaptive exponential integrate-and-fire model

Wulfram Gerstner, Romain Brette, W. Gerstner, 2009, Scholarpedia.

Spike-timing prediction in cortical neurons with active dendrites

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

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.

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

Wulfram Gerstner, Moritz Deger, Alexander Seeholzer, 2019, PLoS Comput. Biol..

Balancing New Against Old Information: The Role of Surprise pdf

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

Intrinsic Stabilization of Output Rates by Spike-Based Hebbian Learning

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

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

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

Code-specific policy gradient rules for spiking neurons

Wulfram Gerstner, Guillaume Hennequin, Henning Sprekeler, 2009, NIPS.

Adaptive exponential integrate-and-fire model as an effective description of neuronal activity.

Wulfram Gerstner, Romain Brette, W. Gerstner, 2005, Journal of neurophysiology.

The role of interconnected hub neurons in cortical dynamics

Wulfram Gerstner, Moritz Deger, Hesam Setareh, 2014, BMC Neuroscience.

Neural pre-coding increases the pattern retrieval capacity of Hopfield and Bidirectional Associative Memories

Wulfram Gerstner, Amin Shokrollahi, K. Raj Kumar, 2011, 2011 IEEE International Symposium on Information Theory Proceedings.

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..

Neuronal Dynamics: Preface

Wulfram Gerstner, Richard Naud, Liam Paninski, 2014 .

How Good Are Neuron Models?

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

Bellman Equation

Wulfram Gerstner, Pascal Poupart, Hannu Toivonen, 2010, Encyclopedia of Machine Learning.

Encoding and Decoding of Patterns which are Correlated in Space and Time

Wulfram Gerstner, J. Leo van Hemmen, Andreas V. M. Herz, 1990, ÖGAI.

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

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

Biological Learning: Synaptic Plasticity, Hebb Rule and Spike TimingDependent Plasticity

Wulfram Gerstner, 2010, Encyclopedia of Machine Learning.

Reinforcement Learning in Continuous State and Action Space

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

Rapid signal transmission by populations of spiking neurons

Wulfram Gerstner, 1999 .

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

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

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.

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..

Automatic characterization of three cortical neuron types reveals two distinct adaptation mechanisms

Wulfram Gerstner, Skander Mensi, Richard Naud, 2011, BMC Neuroscience.

Inference of neuronal network spike dynamics and topology from calcium imaging data

Wulfram Gerstner, Felipe Gerhard, Fritjof Helmchen, 2013, Front. Neural Circuits.

A neuronal learning rule for sub-millisecond temporal coding

Wulfram Gerstner, Richard Kempter, J. Leo van Hemmen, 1996, Nature.

Variational Learning for Recurrent Spiking Networks

Wulfram Gerstner, Daan Wierstra, Danilo Jimenez Rezende, 2011, NIPS.

An online Hebbian learning rule that performs independent component analysis

Wulfram Gerstner, Claudia Clopath, André Longtin, 2008, BMC Neuroscience.

How Can the Brain Be So Fast

Wulfram Gerstner, 2006 .

Spike-timing Dependent Plasticity and Mutual Information Maximization for a Spiking Neuron Model

Jean-Pascal Pfister, Kazuyuki Aihara, Wulfram Gerstner, 2004, NIPS.

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

José del R. Millán, Wulfram Gerstner, Josep Mouriño, 2004, IEEE Transactions on Biomedical Engineering.

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.

Integrate-and-Fire models with adaptation are good enough

Wulfram Gerstner, Hans-Rudolf Lüscher, Alexander Rauch, 2005, NIPS.

Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation

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

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

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

Neuromodulation by surprise: a biologically plausible model of the learning rate dynamics

Kerstin Preuschoff, Wulfram Gerstner, Mohammad Javad Faraji, 2014, BMC Neuroscience.

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

Wulfram Gerstner, 2017, Encyclopedia of Machine Learning and Data Mining.

Spiking Neuron Models

Wulfram Gerstner, W. K. Kistler, W. Gerstner, 2002 .

Plasticity and coding

Wulfram Gerstner, Werner M. Kistler, 2002 .

Neuronal Dynamics: Outlook: dynamics in plastic networks

Wulfram Gerstner, Richard Naud, Liam Paninski, 2014 .

What Matters in Neuronal Locking?

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

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

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

Towards truly local gradients with CLAPP: Contrastive, Local And Predictive Plasticity pdf

Wulfram Gerstner, Guillaume Bellec, Bernd Illing, 2020, ArXiv.

A developmental model of spatio-temporal receptive field properties for simple cells in the visual cortex

Wulfram Gerstner, Stefan Wimbauer, J. L. van Hemmen, 1995 .

Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules

Wulfram Gerstner, Johanni Brea, Marco Lehmann, 2018, Front. Neural Circuits.

Populations of spiking neurons

Wulfram Gerstner, 1999 .

Frequency Spectrum of Coupled Stochastic Neurons with Refractoriness

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

Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons

Wulfram Gerstner, Henning Sprekeler, Nicolas Frémaux, 2013, PLoS Comput. Biol..

Tag-Trigger-Consolidation: A Model of Early and Late Long-Term-Potentiation and Depression

Wulfram Gerstner, Claudia Clopath, Lars Buesing, 2008, PLoS Comput. Biol..

Spiking neurons

Wulfram Gerstner, W. Gerstner, 1999 .

Spiking Neuron Models: References

Wulfram Gerstner, Werner M. Kistler, 2002 .

Weight-space symmetry in deep networks gives rise to permutation saddles, connected by equal-loss valleys across the loss landscape pdf

Wulfram Gerstner, Johanni Brea, Berfin Simsek, 2019, ArXiv.

A benchmark test for a quantitative assessment of simple neuron models

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

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..

Synaptic tagging and capture: a bridge from molecular to behaviour

Wulfram Gerstner, Lorric Ziegler, 2011, BMC Neuroscience.

Learning in Volatile Environments With the Bayes Factor Surprise

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

STDP in Adaptive Neurons Gives Close-To-Optimal Information Transmission

Jean-Pascal Pfister, Wulfram Gerstner, Guillaume Hennequin, 2010, Front. Comput. Neurosci..

Spiking Neuron Models: Formal spiking neuron models

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

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

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

Predicting spike timing of neocortical pyramidal neurons by simple threshold models

Wulfram Gerstner, Hans-Rudolf Lüscher, Alexander Rauch, 2006, Journal of Computational Neuroscience.

Spontaneous Excitations in the Visual Cortex: Stripes, Spirals, Rings, and Collective Bursts

Wulfram Gerstner, J. Leo van Hemmen, Raphael Ritz, 1995, Neural Computation.

Hebbian learning of pulse timing in the Barn Owl auditory system

Wulfram Gerstner, J. Leo van Hemmen, Hermann Wagner, 1999 .

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

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

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

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

Surprise-modulated belief update: how to learn within changing environments?

Kerstin Preuschoff, Wulfram Gerstner, Mohammadjavad Faraji, 2016 .

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

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

Spike-response model

Wulfram Gerstner, W. Gerstner, 2008, Scholarpedia.

Effect of correlations on signal transmission in a population of spiking neurons

Wulfram Gerstner, Carson C. Chow, Mona Spiridon, 2000, Neurocomputing.

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

Jean-Pascal Pfister, David Barber, Wulfram Gerstner, 2005, Neural Computation.

Optimal Stimulation Protocol in a Bistable Synaptic Consolidation Model

Wulfram Gerstner, Samuel P. Muscinelli, Chiara Gastaldi, 2018, Front. Comput. Neurosci..

Multi-timescale memory dynamics in a reinforcement learning network with attention-gated memory pdf

Wulfram Gerstner, Aditya Gilra, Marco Martinolli, 2017, ArXiv.

Modelling Path Integrator Recalibration Using Hippocampal Place Cells

Ricardo Chavarriaga, Wulfram Gerstner, Thomas Strösslin, 2005, ICANN.

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.

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

Wulfram Gerstner, Walter Senn, Robert Urbanczik, 2009, PLoS Comput. Biol..

Quantitative Single-Neuron Modeling: Competition 2009

Wulfram Gerstner, Richard Naud, Thomas Berger, 1970 .

Paradoxical Evidence Integration in Rapid Decision Processes

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

A History of Spike-Timing-Dependent Plasticity

Wulfram Gerstner, Henry Markram, Per Jesper Sjöström, 2011, Front. Syn. Neurosci..

Comparison of face verification results on the XM2VTFS database

Hong Yan, Anastasios Tefas, Ioannis Pitas, 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

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

Wulfram Gerstner, Christof Koch, Skander Mensi, 2015, PLoS Comput. Biol..

Head direction cells and place cells in models for navigation and robotic applications

Angelo Arleo, Wulfram Gerstner, W. Gerstner, 2005 .

Biologically plausible deep learning - but how far can we go with shallow networks?

Wulfram Gerstner, Johanni Brea, Bernd Illing, 2019, Neural Networks.

Algorithmic Composition of Melodies with Deep Recurrent Neural Networks pdf

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

Reducing Spike Train Variability: A Computational Theory Of Spike-Timing Dependent Plasticity

Wulfram Gerstner, Timothy J. Lewis, Renaud Jolivet, 2004, BNAIC.

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

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

Visual backward masking: Feed-forward or recurrent?

Wulfram Gerstner, Michael H. Herzog, Frouke Hermens, 2005 .

Dynamical principles for neuroscience and intelligent biomimetic devices

Wulfram Gerstner, Dario Floreano, Aude Billard, 2006 .

Surprise-based learning: a novel measure of surprise with applications for learning within changing environments

Kerstin Preuschoff, Wulfram Gerstner, Mohammadjavad Faraji, 2016 .

How single neuron properties shape chaotic dynamics and signal transmission in random neural networks

Wulfram Gerstner, Samuel P. Muscinelli, Tilo Schwalger, 2018, PLoS Comput. Biol..

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

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

Brain signals of a Surprise-Actor-Critic model: Evidence for multiple learning modules in human decision making

Kerstin Preuschoff, Wulfram Gerstner, Johanni Brea, 2021, NeuroImage.

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

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

Learning in Volatile Environments With the Bayes Factor Surprise

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

Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances pdf

Wulfram Gerstner, Johanni Brea, Arthur Jacot, 2021, ICML.

Oscillations and synchrony

Wulfram Gerstner, Werner M. Kistler, 2002 .

Statistics of subthreshold neuronal voltage fluctuations due to conductance-based synaptic shot noise.

Wulfram Gerstner, Magnus J E Richardson, 2006, Chaos.

Neuronal Dynamics: Variability of spike trains and neural codes

Wulfram Gerstner, Richard Naud, Liam Paninski, 2014 .

A developmental learing rule for coincidence tuning in the barn owl auditory system

Wulfram Gerstner, Richard Kempter, J. Leo van Hemmen, 1997 .

Modeling directional firing properties of place cells

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

Single neuron properties shape chaotic dynamics in random neural networks

Wulfram Gerstner, Samuel P. Muscinelli, Tilo Schwalger, 2018 .

The Silent Period of Evidence Integration in Fast Decision Making

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

Hebbian Learning and Plasticity

Wulfram Gerstner, 2018 .

Wulfram Gerstner, Felipe Gerhard, Skander Mensi, 2010 .

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.

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

Wulfram Gerstner, Brent Doiron, Taro Toyoizumi, 2017, Philosophical Transactions of the Royal Society B: Biological Sciences.

Dendrites and synapses

Wulfram Gerstner, Richard Naud, Liam Paninski, 2014 .

Modeling spatial and temporal aspects of visual backward masking.

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

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

Wulfram Gerstner, Moritz Deger, Alexander Seeholzer, 2016, Cerebral cortex.

Supervised Learning for Neural Networks : A Tutorial with JAVA exercises 1

Wulfram Gerstner, W. Gerstner, 2022 .

Efficient low-dimensional approximation of continuous attractor networks

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

A Model of Synaptic Reconsolidation

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

Associative binding and segregation in a network of spiking neurons

Wulfram Gerstner, J. Leo van Hemmen, Raphael Ritz, 1994 .

Noisy Inputs In Vitro The Dynamical Response Properties of Neocortical Neurons to Temporally Modulated

Johannes Schemmel, Karlheinz Meier, Wulfram Gerstner, 2008 .

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

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

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

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

Spike timing prediction with active dendrites

Wulfram Gerstner, Richard Naud, Brice Bathellier, 2013, 1311.3586.