Learning what matters: Synaptic plasticity with invariance to second-order input correlations

A. Morrison; C. S. N. d. Brito; W. Gerstner 

Fast adaptation to rule switching using neuronal surprise

A. Morrison; M. L. L. R. Barry; W. Gerstner 

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

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

A. Oryshchuk; C. Sourmpis; J. Weverbergh; R. Asri; V. Esmaeili et al. 

A dynamic attractor network model of memory formation, reinforcement and forgetting

M. Boscaglia; C. Gastaldi; W. Gerstner; R. Q. Quiroga 

Automated neuron tracking inside moving and deforming C. elegans using deep learning and targeted augmentation

C. F. Park; M. Barzegar-Keshteli; K. Korchagina; A. Delrocq; V. Susoy et al. 

NMDA-driven dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways

W. A. M. Wybo; M. C. Tsai; V. A. K. Tran; B. A. Illing; J. Jordan et al. 

An exact mapping from ReLU networks to spiking neural networks’ [Neural Networks(vol168, pg 74, 2023)

A. Stanojevic; S. Wozniak; G. Bellec; G. Cherubini; A. Pantazi et al. 

An exact mapping from ReLU networks to spiking neural networks

A. Stanojevic; S. Woźniak; G. Bellec; G. Cherubini; A. Pantazi et al. 

Surprise and novelty in the brain

A. Modirshanechi; S. Becker; J. Brea; W. Gerstner 

On a Finite-Size Neuronal Population Equation

V. Schmutz; E. Locherbach; T. Schwalger 

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

J. Brea; N. S. Clayton; W. Gerstner 

Curiosity-driven exploration: foundations in neuroscience and computational modeling

A. Modirshanechi; K. Kondrakiewicz; W. Gerstner; S. Haesler 

A systematic review of empirical studies using log data from open-ended learning environments to measure science and engineering practices

K. D. Wang; J. M. Cock; T. Kaeser; E. Bumbacher 

Supervised learning and inference of spiking neural networks with temporal coding

From event-based surprise to lifelong learning. A journey in the timescales of adaptation

Automated music composition and generation system and method

F. Colombo 

Modulation of working memory duration by synaptic and astrocytic mechanisms

S. Becker; A. Nold; T. Tchumatchenko 

Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity

B. Pietras; V. Schmutz; T. Schwalger 

Time-encoded multiplication-free spiking neural networks: application to data classification tasks

A. Stanojevic; G. Cherubini; S. Wozniak; E. Eleftheriou 

Long Time Behavior of an Age- and Leaky Memory-Structured Neuronal Population Equation

C. Fonte; V. M. Schmutz 

Mesoscopic modeling of hidden spiking neurons

S. Wang; V. Schmutz; G. Bellec; W. Gerstner 

Kernel Memory Networks: A Unifying Framework for Memory Modeling

G. Iatropoulos; J. Brea; W. Gerstner 

A taxonomy of surprise definitions

A. Modirshanechi; J. Brea; W. Gerstner 

Mean-field limit of age and leaky memory dependent Hawkes processes

V. Schmutz 

Hippocampal sharp wave-ripples and the associated sequence replay emerge from structured synaptic interactions in a network model of area CA3

A. Ecker; B. Bagi; E. Vertes; O. Steinbach-Nemeth; M. R. Karlocai et al. 

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

V. Liakoni; M. P. Lehmann; A. Modirshanechi; J. Brea; A. Lutti et al. 

Approximating Relu Networks By Single-Spike Computation

A. Stanojevic; E. Eleftheriou; G. Cherubini; S. Wozniak; A. Pantazi et al. 

Taming neuronal noise with large networks

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

C. Gastaldi; T. Schwalger; E. De Falco; R. Q. Quiroga; W. Gerstner 

Spike frequency adaptation supports network computations on temporally dispersed information

D. Salaj; A. Subramoney; C. Kraisnikovi; G. Bellec; R. Legenstein et al. 

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

V. Esmaeili; K. Tamura; S. P. Muscinelli; A. Modirshanechi; M. Boscaglia et al. 

A functional model of adult dentate gyrus neurogenesis

O. Gozel; W. Gerstner 

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

H. Xu; A. Modirshanechi; M. P. Lehmann; W. Gerstner; M. Herzog 

Learning in Volatile Environments With the Bayes Factor Surprise

V. Liakoni; A. Modirshanechi; W. Gerstner; J. Brea 

Testing two competing hypotheses for Eurasian jays’ caching for the future

P. Amodio; J. Brea; B. G. Farrar; L. Ostojic; N. S. Clayton 

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

B. Şimşek; F. Ged; A. Jacot; F. Spadaro; C. Hongler et al. 

Biologically plausible unsupervised learning in shallow and deep neural networks

A dynamical systems approach to synaptic consolidation and associations of concepts in hippocampus

Learning music composition with recurrent neural networks

Surprise-based model estimation in reinforcement learning: algorithms and brain signatures

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

G. Bellec; S. Wang; A. Modirshanechi; J. M. Brea; W. Gerstner 

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

B. A. Illing; J. Ventura; G. Bellec; W. Gerstner 

CauseOccam: Learning Interpretable Abstract Representations in Reinforcement Learning Environments via Model Sparsity

S. Volodin 

Dendritic Voltage Recordings Explain Paradoxical Synaptic Plasticity: A Modeling Study

C. Meissner-Bernard; M. C. Tsai; L. Logiaco; W. Gerstner 

Low-dimensional firing-rate dynamics for populations of renewal-type spiking neurons

B. Pietras; N. Gallice; T. Schwalger 

Data-driven integration of hippocampal CA1 synaptic physiology in silico

A. Ecker; A. Romani; S. Saray; S. Kali; M. Migliore et al. 

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

S. C. Surace; J-P. Pfister; W. Gerstner; J. Brea 

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

V. Schmutz; W. Gerstner; T. Schwalger 

Spiking Neural Networks Trained With Backpropagation For Low Power Neuromorphic Implementation Of Voice Activity Detection

F. Martinelli; G. Dellaferrera; P. Mainar; M. Cernak 

A Bin Encoding Training Of A Spiking Neural Network Based Voice Activity Detection

G. Dellaferrera; F. Martinelli; M. Cernak 

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

M. P. Lehmann; H. A. Xu; V. Liakoni; M. H. Herzog; W. Gerstner et al. 

Optimal Stimulation Protocol in a Bistable Synaptic Consolidation Model

C. Gastaldi; S. Muscinelli; W. Gerstner 

Cell-specific image-guided transcriptomics identifies complex injuries caused by ischemic acute kidney injury in mice

T. Miyazaki; S. A. Gharib; Y-W. A. Hsu; K. Xu; P. Khodakivskyi et al. 

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

S. P. Muscinelli; W. Gerstner; T. Schwalger 

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

A. Seeholzer; M. Deger; W. Gerstner 

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

B. Illing; W. Gerstner; J. Brea 

From adult dentate gyrus neurogenesis to pattern separation

Specific synaptic input strengths determine the computational properties of excitation-inhibition integration in a sound localization circuit

E. Gjoni; F. Zenke; B. Bouhours; R. Schneggenburger 

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

M. Martinolli; W. Gerstner; A. Gilra 

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

M. Deger; A. Seeholzer; W. Gerstner 

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

H. Setareh; M. Deger; W. Gerstner 

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

W. Gerstner; M. Lehmann; V. Liakoni; D. Corneil; J. Brea 

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

M. Faraji; K. Preuschoff; W. Gerstner 

In vitro Cortical Network Firing is Homeostatically Regulated: A Model for Sleep Regulation

S. Saberi-Moghadam; A. Simi; H. Setareh; C. Mikhail; M. Tafti 

Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation

D. S. Corneil; W. Gerstner; J. M. Brea 

One-shot learning and eligibility traces in sequential decision making

Slow dynamics in structured neural network models

Model-based reinforcement learning and navigation in animals and machines

Simplification of neural network models

H. Markram; W. Gerstner; M-O. Gewaltig; C. Rössert; E. B. Muller et al. 

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

A. Gilra; W. Gerstner 

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

H. Setareh; M. Deger; C. C. Petersen; W. Gerstner 

Mapping the function of neuronal ion channels in model and experiment

W. F. Podlaski; A. Seeholzer; L. N. Groschner; G. Miesenbock; R. Ranjan et al. 

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

T. Schwalger; M. Deger; W. Gerstner 

On the stability and dynamics of stochastic spiking neuron models: Nonlinear Hawkes process and point process GLMs

F. Gerhard; M. Deger; W. Truccolo 

Exponentially long orbits in Hopfield neural networks

S. P. Muscinelli; W. Gerstner; J. M. Brea 

The temporal paradox of Hebbian learning and homeostatic plasticity

F. Zenke; W. Gerstner; S. Ganguli 

Synaptic patterning and the timescales of cortical dynamics

R. Duarte; A. Seeholzer; K. Zilles; A. Morrison 

Hebbian plasticity requires compensatory processes on multiple timescales

F. Zenke; W. Gerstner 

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

T. Keck; T. Toyoizumi; L. Chen; B. Doiron; D. E. Feldman et al. 

Neural assemblies as core elements for modeling neural networks in the brain

Continuous attractor working memory and provenance of channel models

Evidence for eligibility traces in human learning

M. Lehmann; H. Xu; V. Liakoni; M. Herzog; W. Gerstner et al. 

Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation

C. S. N. Brito; W. Gerstner 

Prospective Coding by Spiking Neurons

J. Brea; A. T. Gaal; R. Urbanczik; W. Senn 

Contribution of next-to-leading order and Landau-Pomeranchuk-Migdal corrections to thermal dilepton emission in heavy-ion collisions

Y. Burnier; C. Gastaldi 

Balancing New Against Old Information: The Role of Surprise

M. Faraji; K. Preuschoff; W. Gerstner 

Does computational neuroscience need new synaptic learning paradigms?

J. M. Brea; W. Gerstner 

A Model of Synaptic Reconsolidation

D. B. Kastner; T. Schwalger; L. Ziegler; W. Gerstner 

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

S. Mensi; O. Hagens; W. Gerstner; C. Pozzorini 

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

N. Fremaux; W. Gerstner 

A Multiscale Pyramid Transform for Graph Signals

D. Shuman; M. Faraji; P. Vandergheynst 

Algorithmic Composition of Melodies with Deep Recurrent Neural Networks

F. Colombo; S. P. Muscinelli; A. Seeholzer; J. Brea; W. Gerstner 

Learning with Surprise

Theory of representation learning in cortical neural networks

Learning and generation of slow sequences: an application to music composition

F. Colombo 

Algorithmic composition of melodies with deep recurrent neural networks

F. Colombo 

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

M. Faraji; K. Preuschoff; W. Gerstner 

A novel information theoretic measure of surprise

M. Faraji; K. Preuschoff; W. Gerstner 

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

M. Faraji; K. Preuschoff; W. Gerstner 

Analytical approach to an integrate-and-fire model with spike-triggered adaptation

T. Schwalger; B. Lindner 

Slow fluctuations in recurrent networks of spiking neurons

S. Wieland; D. Bernardi; T. Schwalger; B. Lindner 

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

C. A. Pozzorini; S. Mensi; O. Hagens; R. Naud; C. Koch et al. 

Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity

Y. V. Zaytsev; A. Morrison; M. Deger 

Interspike interval correlation in a stochastic exponential integrate-and-fire model with subthreshold and spike-triggered adaptation

L. Shiau; T. Schwalger; B. Lindner 

Statistical structure of neural spiking under non-Poissonian or other non-white stimulation

T. Schwalger; F. Droste; B. Lindner 

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

F. Zenke; E. J. Agnes; W. Gerstner 

Synaptic consolidation: from synapses to behavioral modeling

L. Ziegler; F. Zenke; D. B. Kastner; W. Gerstner 

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

D. S. Corneil; W. Gerstner 

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

T. Schwalger; M. Deger; W. Gerstner 

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

M. Faraji; K. Preuschoff; W. Gerstner 

Surprise minimization as a learning strategy in neural networks

M. Faraji; K. Preuschoff; W. Gerstner 

Bayesian filtering, parallel hypotheses and uncertainty: a new, combined model for human learning

M. P. Lehmann; A. Aivazidis; M. Faraji; K. Preuschoff 

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

D. S. Corneil; W. Gerstner 

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

F. Zenke; E. Agnes; W. Gerstner 

Learning associations with a neurally-computed global novelty signal

M. Faraji; K. Preuschoff; W. Gerstner 

Synaptic efficacy tunes speed of activity propagation through chains of bistable neural assemblies

H. Setareh; M. Deger; W. Gerstner 

Music Learning with Long Short Term Memory Networks

F. F. Colombo 

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

C. Tomm; M. Avermann; C. Petersen; W. Gerstner; T. P. Vogels 

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

M. Deger; T. Schwalger; R. Naud; W. Gerstner 

Periodic versus Intermittent Adaptive Cycles in Quasispecies Coevolution

A. Seeholzer; E. Frey; B. Obermayer 

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

F. Zenke; W. Gerstner 

Spike-timing prediction in cortical neurons with active dendrites

R. Naud; B. Bathellier; W. Gerstner 

Analyse des réseaux de personnages dans Les Confessions de Jean-Jacques Rousseau

Y. Rochat; F. Kaplan 

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

G. Hennequin; T. Vogels; W. Gerstner 

Stochastic variational learning in recurrent spiking networks

D. J. Rezende; W. Gerstner 

Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition

W. Gerstner; W. M. Kistler; R. Naud; L. Paninski 

Computational principles of single neuron adaptation

A new Mathematical Framework to Understand Single Neuron Computations

Memory formation and recall in recurrent spiking neural networks

Synaptic Learning Rules with Consolidation

Statistical models of effective connectivity in neural microcircuits

A biologically plausible model of the learning rate dynamics

M. Faraji; K. Preuschoff; W. Gerstner 

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

M. Faraji; K. Preuschoff; W. Gerstner 

The role of interconnected hub neurons in cortical dynamics

H. Setareh; M. Deger; W. Gerstner 

Visualizing the similarity and pedigree of NEURON ion channel models available on ModelDB

W. F. Podlaski; A. Seeholzer; R. Rajnish; T. Vogels 

Second Order Phase Transition Describes Maximally Informative Encoding in the Retina

D. Kastner; S. A. Baccus; T. O. Sharpee 

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

M. Faraji; K. Preuschoff; W. Gerstner 

Learning Multi-Stability in Plastic Neural Networks

F. Zenke; E. Agnes 

Network dynamics of spiking neurons with adaptation

M. Deger; T. Schwalger; R. Naud 

Learning, Inference, and Replay of Hidden State Sequences in Recurrent Spiking Neural Networks

D. S. Corneil; E. Neftci; G. Indiveri; M. Pfeiffer 

Statistical structure of neural spiking under non-Poissonian stimulation

T. Schwalger; F. Droste; B. Lindner 

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

F. Zenke; G. Hennequin; W. Gerstner 

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

H. Luetcke; F. Gerhard; F. Zenke; W. Gerstner; F. Helmchen 

Patterns of interval correlations in neural oscillators with adaptation

T. Schwalger; B. Lindner 

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

V. Pawlak; D. S. Greenberg; H. Sprekeler; W. Gerstner; J. N. D. Kerr 

Temporal whitening by power-law adaptation in neocortical neurons

C. A. Pozzorini; R. Naud; S. Mensi; W. Gerstner 

Successful Reconstruction of a Physiological Circuit with Known Connectivity from Spiking Activity Alone

F. Gerhard; T. Kispersky; G. J. Gutierrez; E. Marder; U. Eden 

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

N. Frémaux; H. Sprekeler; W. Gerstner 

Parallel retrieval of correlated patterns: From Hopfield networks to Boltzmann machines

E. Agliari; A. Barra; A. De Antoni; A. Galluzzi 

The Silent Period of Evidence Integration in Fast Decision Making

J. Rüter; H. Sprekeler; W. Gerstner; M. H. Herzog 

Inhibitory synaptic plasticity: spike timing-dependence and putative network function

T. P. Vogels; R. C. Froemke; N. Doyon; M. Gilson; J. S. Haas et al. 

Models of Reward-Modulated Spike-Timing-Dependent Plasticity

Stability and amplification in plastic cortical circuits

Can We Predict Every Spike?

R. Naud; W. Gerstner 

Temporal decorrelation by power-law adaptation in pyramidal neurons

C. A. Pozzorini; R. Naud; S. Mensi 

Evidence for a nonlinear coupling between firing threshold and subthreshold membrane potential

S. Mensi; C. A. Pozzorini; O. Hagens 

Rhythmic Modulation of Theta Oscillations Supports Encoding of Spatial and Behavioral Information in the Rat Hippocampus

C. Molter; J. O’Neill; Y. Yamaguchi; H. Hirase; X. Leinekugel 

Theory and Simulation in Neuroscience

W. Gerstner; H. Sprekeler; G. Deco 

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

R. Naud; W. Gerstner 

Non-normal amplification in random balanced neuronal networks

G. Hennequin; T. Vogels; W. Gerstner 

Microcircuits of excitatory and inhibitory neurons in layer 2/3 of mouse barrel cortex

M. Avermann; C. Tomm; C. Mateo; W. Gerstner; C. C. H. Petersen 

Perceptual learning, roving and the unsupervised bias

M. H. Herzog; K. C. Aberg; N. Frémaux; W. Gerstner; H. Sprekeler 

Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms

S. Mensi; R. Naud; C. Pozzorini; M. Avermann; C. C. H. Petersen et al. 

Firmware for ensuring realtime radio regulations compliance in WSN

M. Tanevski; A. Boegli; P-A. Farine 

Spline- and Wavelet-based Models of Neural Activity in Response to Natural Visual Stimulation

F. Gerhard; L. Szegletes 

Analysing Neuronal Network Architectures

Extraction of network topology from multi-electrode recordings: is there a small-world effect?

F. Gerhard; G. Pipa; B. Lima; S. Neuenschwander; W. Gerstner 

Slow Feature Analysis

L. Wiskott; P. Berkes; M. Franzius; H. Sprekeler; N. Wilbert 

On the Relation of Slow Feature Analysis and Laplacian Eigenmaps

H. Sprekeler 

A Theory of Slow Feature Analysis for Transformation-Based Input Signals with an Application to Complex Cells

H. Sprekeler; L. Wiskott 

A triplet spike-timing-dependent plasticity model generalizes the Bienenstock-Cooper-Munro rule to higher-order spatiotemporal correlations

J. Gjorgjieva; C. Clopath; J. Audet; J-P. Pfister 

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

T. Vogels; H. Sprekeler; F. Zenke; C. Clopath; W. Gerstner 

Improved Similarity Measures for Small Sets of Spike Trains

R. Naud; F. Gerhard; S. Mensi; W. Gerstner 

A history of spike-timing-dependent plasticity

H. Markram; W. Gerstner; P. J. Sjöström 

Neural mechanisms and computations underlying stress effects on learning and memory

G. Luksys; C. Sandi 

Applying the Multivariate Time-Rescaling Theorem to Neural Population Models

F. Gerhard; R. Haslinger; G. Pipa 

Quantitative Analysis for Authentication of Low-cost RFID Tags

I. Paparrizos; S. Basagiannis; S. Petridou 

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

S. Mensi; R. Naud; W. Gerstner 

Variational Learning for Recurrent Spiking Networks

D. Jimenez Rezende; D. Wierstra; W. Gerstner 

Neural Pre-coding Increases the Pattern Retrieval Capacity of Hopfield and Bidirectional Associative Memories

A. H. Salavati; K. R. Kumar; A. Shokrollahi; W. Gerstner 

The Dynamics of Adapting Neurons

Models of Evidence Integration in Rapid Decision Making Processes

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

F. Gerhard; G. Pipa; W. Gerstner 

Estimation of small-world topology of cortical networks using Generalized Linear Models

F. Gerhard; G. Pipa; W. Gerstner 

Perceptual Learning, Roving and the Unsupervised Bias

A. M. Clarke; H. Sprekeler; W. Gerstner; M. H. Herzog 

PyBrain

T. Schaul; J. Bayer; D. Wierstra; Y. Sun; M. Felder et al. 

STDP in adaptive neurons gives close-to-optimal information transmission

G. Hennequin; W. Gerstner; J-P. Pfister 

From Hebb rules to Spike-Timing-Dependent Plasticity: a personal account

W. Gerstner 

Voltage and spike timing interact in STDP – a unified model

C. Clopath; W. Gerstner 

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

N. Frémaux; H. Sprekeler; W. Gerstner 

Spike-timing dependent plasticity

W. Gerstner; J. Sjostrom 

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

C. Clopath; L. Büsing; E. Vasilaki; W. Gerstner 

A two stage model for perceptual decision making

N. Marcille; J. Rueter; M. H. Herzog; W. Gerstner 

Exponential Natural Evolution Strategies

T. Glasmachers; T. Schaul; S. Yi; D. Wierstra; J. Schmidhuber 

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

F. Gerhard; W. Gerstner 

Complexity and performance in simple neuron models

S. Mensi; R. Naud; T. K. Becker; W. Gerstner 

Statistical tests for neural population models – The multivariate time rescaling theorem

R. H. Haslinger; F. Gerhard; G. Pipa 

Goodness-of-fit tests for neural population models: the multivariate time-rescaling theorem

F. Gerhard; R. Haslinger; G. Pipa 

Improved Similarity Measures for Small Sets of Spike Trains

R. Naud; F. Gerhard; S. Mensi; W. Gerstner 

A two stage model for perceptual decision making

N. Marcille; J. Rüter; M. H. Herzog; W. Gerstner 

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

F. Gerhard; G. Pipa; W. Gerstner 

Stress, genotype and norepinephrine in the prediction of mouse behavior using reinforcement learning

G. Luksys; W. Gerstner; C. Sandi 

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

E. Vasilaki; N. Frémaux; R. Urbanczik; W. Senn; W. Gerstner 

How Good are Neuron Models?

W. Gerstner; R. Naud 

Establishing a Novel Modeling Tool: A Python-based Interface for a Neuromorphic Hardware System

D. Bruederle 

NEURON and Python

M. Hines; A. P. Davison; E. Muller 

Is there a geometric module for spatial orientation? Insights from a rodent navigation model

D. Sheynikhovich; R. Chavarriaga; T. Strösslin; A. Arleo; W. Gerstner 

Linear Compressive Networks

N. Goela; M. Gastpar 

Code-specific policy gradient rules for spiking neurons

H. Sprekeler; G. Hennequin; W. Gerstner 

Spike-timing prediction in a neuron model with active dendrites

R. Naud; B. Bathellier; W. Gerstner 

Synaptic plasticity across different time scales and its functional implications

Stress, individual differences, and norepinephrine in reinforcement learning-based prediction of mouse behavior in conditioning and spatial learning

Interpretation of neuronal response properties with simplified neuron models

Functional shape of the spike-triggered adaptation

C. Pozzorini 

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

B. Bathellier; A. Carleton 

Special issue on quantitative neuron modeling

R. Jolivet; A. Roth; F. Schuermann; W. Gerstner; W. Senn 

Modeling spatial and temporal aspects of visual backward masking

F. Hermens; G. Luksys; W. Gerstner; M. H. Herzog; U. Ernst 

PyNEST: a convenient interface to the NEST simulator

J. M. Eppler; M. Helias; E. Muller; M. Diesmann; M-O. Gewaltig 

PyNN: a common interface for neuronal network simulators

A. P. Davison; D. Brüderle; J. Eppler; J. Kremkow; E. Muller et al. 

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

L. Badel; S. Lefort; T. K. Berger; C. C. H. Petersen; W. Gerstner et al. 

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

C. Clopath; L. Ziegler; E. Vasilaki; L. Buesing; W. Gerstner 

The quantitative single-neuron modeling competition

R. Jolivet; F. Schürmann; T. K. Berger; R. Naud; W. Gerstner et al. 

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

R. Naud; N. Marcille; C. Clopath; W. Gerstner 

Predictive Coding and the Slowness Principle: An Information-Theoretic Approach

F. Creutzig; H. Sprekeler 

Phenomenological models of synaptic plasticity based on spike timing

A. Morrison; M. Diesmann; W. Gerstner 

Spike-triggered averages for passive and resonant neurons receiving filtered excitatory and inhibitory synaptic drive

L. Badel; W. Gerstner; J. E. Richardson 

A benchmark test for a quantitative assessment of simple neuron models

R. Jolivet; R. Kobayashi; A. Rauch; R. Naud; S. Shinomoto et al. 

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

L. Badel; S. Lefort; R. Brette; C. C. H. Petersen; W. Gerstner et al. 

Modelling feature-integration in human vision with drift diffusion models

N. Marcille; J. Rüter; M. H. Herzog; W. Gerstner 

Quantitative single-neuron modeling: competition 2008

R. Naud; T. Berger; L. Badel; A. Roth; W. Gerstner 

An online Hebbian learning rule that performs Independent Component Analysis

C. Clopath; A. Longtin; W. Gerstner 

Slowness and Sparseness Lead to Place, Head-Direction, and Spatial-View Cells

M. Franzius; H. Sprekeler; L. Wiskott 

Slowness: An Objective for Spike-Timing-Dependent Plasticity?

H. Sprekeler; C. Michaelis; L. Wiskott 

Consciousness & the Small Network Argument

M. H. Herzog; M. Esfeld; W. Gerstner 

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

C. Clopath; R. Jolivet; A. Rauch; H-R. Lüscher; W. Gerstner 

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

T. Toyoizumi; J-P. Pfister; K. Aihara; W. Gerstner 

Spatial navigation in geometric mazes

Analysis of information processing in the olfactory bulb by in vivo experiments and theoretical modelling

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

L. Badel; W. Gerstner; M. J. E. Richardson 

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

Y. Aviel; W. Gerstner 

Predicting spike timing of neocortical pyramidal neurons by simple threshold models

R. Jolivet; A. Rauch; H. R. Lüscher; W. Gerstner 

Triplets of Spikes in a Model of Spike Timing-Dependent Plasticity

J. P. Pfister; W. Gerstner 

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

J. P. Pfister; T. Toyoizumi; D. Barber; W. Gerstner 

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

M. J. E. Richardson; W. Gerstner 

Integrate-and-Fire models with adaptation are good enough

R. Jolivet; A. Rauch; H. R. Lüscher; W. Gerstner 

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

J. P. Pfister; W. Gerstner 

Theory of non-linear spike-time-dependent plasticity

Dynamical principles for neuroscience and intelligent biomimetic devices

A. Ijspeert; J. Buchli; A. Selverston; M. Rabinovich; M. Hasler et al. 

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

O. Melamed; G. Silberberg; H. Markram; W. Gerstner; M. J. E. Richardson 

Robust self-localisation and navigation based on hippocampal place cells

T. Strösslin; D. Sheynikhovich; R. Chavarriaga; W. Gerstner 

Adaptive Exponential Integrate-and-Fire Model as an Effective Description of Neuronal Activity

R. Brette; W. Gerstner 

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

R. Chavarriaga; T. Strösslin; D. Sheynikhovich; W. Gerstner 

A computational model of parallel navigation systems in rodents

R. Chavarriaga; T. Strösslin; D. Sheynikhovich; W. Gerstner 

Signal buffering in random networks of spiking neurons: microscopic vs. macroscopic phenomena

J. Mayor; W. Gerstner 

Noise-enhanced computation in a model of a cortical column

J. Mayor; W. Gerstner 

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

M. J. E. Richardson; W. Gerstner 

Short-term-plasticity orchestrates the response of pyramidal cells and interneurons to population bursts

M. J. E. Richardson; O. Melamed; G. Silberberg; W. Gerstner; H. Markram 

Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission

T. Toyoizumi; J. P. Pfister; K. Aihara; W. Gerstner 

Modelling Path Integrator Recalibration Using Hippocampal Place cells

T. Strösslin; R. Chavarriaga; D. Sheynikhovich; W. Gerstner 

Spatial Representation and Navigation in a Bio-inspired Robot

D. Sheynikhovich; R. Chavarriaga; T. Strösslin; W. Gerstner 

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

T. Toyoizumi; J-P. Pfister; K. Aihara; W. Gerstner 

Pulse propagation in discrete excitatory networks of integrate-and-fire neurons

L. Badel; A. Tonnelier 

Cognitive navigation based on non-uniform Gabor space sampling, unsupervised growing networks, and reinforcement learning

A. Arleo; F. Smeraldi; W. Gerstner 

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

R. Jolivet; W. Gerstner 

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

R. Jolivet; T. J. Lewis; W. Gerstner 

Transient information flow in a network of excitatory and inhibitory model neurons: role of noise and signal autocorrelation

J. Mayor; W. Gerstner 

Coding and Learning of behavioral sequences

O. Melamed; W. Gerstner; W. Maass; M. Tsodyks; H. Markram 

Non-invasive Brain actuated control of a mobile robot by Human EEG

J. d. R. Millán; F. Renkens; J. Mouriño; W. Gerstner 

Brain-Actuated Interaction

J. d. R. Millán; F. Renkens; J. Mouriño; W. Gerstner 

Bifurcation properties of the average activity of interconnected neural populations

A. Tonnelier 

Piecewise linear differential equations and integrate-and-fire neurons : insights from two-dimensional membrane models

A. Tonnelier; W. Gerstner 

Optimal Hebbian Learning: a Probabilistic Point of View

J-P. Pfister; D. Barber; W. Gerstner 

Online processing of multiple inputs in a sparsely-connected recurrent neural network

J. Mayor; W. Gerstner 

The Spike Response Model: A Framework to Predict Neuronal Spike Trains

R. Jolivet; T. J. Lewis; W. Gerstner 

Non-Invasive Brain-Actuated Control of a Mobile Robot

J. d. R. Millán; F. Renkens; J. Mouriño; W. Gerstner 

Reinforcement Learning in Continuous State and Action Space

T. Strösslin; W. Gerstner 

Homeostasis and Learning through Spike-Timing Dependent Plasticity

L. Abbott; W. Gerstner 

Mathematical Formulations of Hebbian Learning

W. Gerstner; W. K. Kistler 

Stable Propagation of Activity Pulses in Populations of Spiking Neurons

W. M. Kistler; W. Gerstner 

Spiking Neuron Models

W. Gerstner; W. K. Kistler 

Spatial orientation in navigating agents: Modeling head-direction cells

A. Arleo; W. Gerstner 

Coding properties of spiking neurons: reverse- and cross-correlations

W. Gerstner 

Noise and the PSTH response to current transients: I. General theory and application to the integrate-and-fire neuron

A. Herrmann; W. Gerstner 

Noise and the PSTH response to current transients II. Integrate-and-fire model with slow recovery and application to motoneuron data

A. Herrmann; W. Gerstner 

Intrinsic Stabilization of Output Rates by Spike-Based Hebbian Learning

R. Kempter; W. Gerstner; J. L. van Hemmen 

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

M. Spiridon; W. Gerstner 

Hippocampal spatial model for state space representation in robotic reinforcement learning

A. Arleo; W. Gerstner 

A framework for spiking neuron models – the spike response model

W. Gerstner 

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

A. Arleo; W. Gerstner 

Population dynamics of spiking neurons: fast transients, asynchronous states and locking

W. Gerstner 

Effect of noise on neuron transient response

A. Herrmann; W. Gerstner 

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

M. Spiridon; C. C. Chow; W. Gerstner 

Hebbian learning and spiking neurons

R. Kempter; W. Gerstner; J. L. van Hemmen 

Noise-shaping in populations of coupled model neurons

E. J. Mar; C. C. Chow; W. Gerstner; R. W. Adams; J. J. Collins 

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

A. Arleo; W. Gerstner 

Rapid signal transmission by a population of spiking neurons

W. Gerstner 

Understanding the PSTH response to synaptic input

A. Herrmann; W. Gerstner 

Spike-Based Compared to Rate-Based Hebbian Learning

R. Kempter; W. Gerstner; J. L. van Hemmen 

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

R. Kempter; W. Gerstner; J. L. van Hemmen; H. Wagner