Heterogeneous and higher-order cortical connectivity undergirds efficient, robust, and reliable neural codes

D. Egas Santander; C. Pokorny; A. Ecker; J. Lazovskis; M. Santoro et al. 

Emergent Rate-Based Dynamics in Duplicate-Free Populations of Spiking Neurons

V. Schmutz; J. Brea; W. Gerstner 

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

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

Fast learning without synaptic plasticity in spiking neural networks

A. Subramoney; G. Bellec; F. Scherr; R. Legenstein; W. Maass 

Computational models of intrinsic motivation for curiosity and creativity

S. Becker; A. Modirshanechi; W. Gerstner 

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

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

Auditory stimuli suppress contextual fear responses in safety learning independent of a possible safety meaning

E. Mombelli; D. Osypenko; S. Palchaudhuri; C. Sourmpis; J. Brea et al. 

Fast adaptation to rule switching using neuronal surprise

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

Expand-and-Cluster: Parameter Recovery of Neural Networks

F. Martinelli; B. Simsek; W. Gerstner; J. M. Brea 

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

A Computational Framework for Memory Engrams

C. Gastaldi; W. Gerstner 

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. 

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

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

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. 

Surprise and novelty in the brain

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

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. 

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

J. Brea; N. S. Clayton; W. Gerstner 

On a Finite-Size Neuronal Population Equation

V. Schmutz; E. Locherbach; T. Schwalger 

An exact mapping from ReLU networks to spiking neural networks

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

Speaker Embeddings as Individuality Proxy for Voice Stress Detection

Z. Wu; N. Scheidwasser-Clow; K. El Hajal; M. Cernak 

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 

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

Supervised learning and inference of spiking neural networks with temporal coding

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

B. Pietras; V. Schmutz; T. Schwalger 

Modulation of working memory duration by synaptic and astrocytic mechanisms

S. Becker; A. Nold; T. Tchumatchenko 

A taxonomy of surprise definitions

A. Modirshanechi; J. Brea; W. Gerstner 

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

V. Schmutz 

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. 

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

C. Fonte; V. M. Schmutz 

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

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

Kernel Memory Networks: A Unifying Framework for Memory Modeling

G. Iatropoulos; J. M. Brea; W. Gerstner 

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

Mesoscopic modeling of hidden spiking neurons

S. Wang; V. Schmutz; G. Bellec; 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. 

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

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

Biologically plausible unsupervised learning in shallow and deep neural networks

Learning music composition with recurrent neural networks

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

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

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

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

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

V. Schmutz; W. Gerstner; T. Schwalger 

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

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

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 

Optimal Stimulation Protocol in a Bistable Synaptic Consolidation Model

C. Gastaldi; S. Muscinelli; W. Gerstner 

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. 

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

B. Illing; W. Gerstner; J. Brea 

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 

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 

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

H. Setareh; M. Deger; W. Gerstner 

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

M. Faraji; K. Preuschoff; W. Gerstner 

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

M. Deger; A. Seeholzer; 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 

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 

Model-based reinforcement learning and navigation in animals and machines

One-shot learning and eligibility traces in sequential decision making

Slow dynamics in structured neural network models

Simplification of neural network models

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

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

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

Exponentially long orbits in Hopfield neural networks

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

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

F. Gerhard; M. Deger; W. Truccolo 

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

T. Schwalger; M. Deger; W. Gerstner 

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

A. Gilra; 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. 

The temporal paradox of Hebbian learning and homeostatic plasticity

F. Zenke; W. Gerstner; S. Ganguli 

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. 

Synaptic patterning and the timescales of cortical dynamics

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

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. 

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

Y. Burnier; C. Gastaldi 

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

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

Prospective Coding by Spiking Neurons

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

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

N. Fremaux; W. Gerstner 

Does computational neuroscience need new synaptic learning paradigms?

J. M. Brea; W. Gerstner 

Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation

C. S. N. Brito; W. Gerstner 

A Multiscale Pyramid Transform for Graph Signals

D. Shuman; M. Faraji; P. Vandergheynst 

A Model of Synaptic Reconsolidation

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

Algorithmic Composition of Melodies with Deep Recurrent Neural Networks

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

Learning with Surprise : Theory and Applications

Theory of representation learning in cortical neural networks

Balancing New Against Old Information: The Role 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 

Algorithmic composition of melodies with deep recurrent neural networks

F. Colombo 

A novel information theoretic measure of surprise

M. Faraji; K. Preuschoff; W. Gerstner 

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

F. Colombo 

Surprise-modulated belief update: how to learn 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 

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

Y. V. Zaytsev; A. Morrison; M. Deger 

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

T. Schwalger; F. Droste; B. Lindner 

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

L. Shiau; T. Schwalger; 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 

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. 

Slow fluctuations in recurrent networks of spiking neurons

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

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 

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

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

Surprise minimization as a learning strategy in neural networks

M. Faraji; K. Preuschoff; W. Gerstner 

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

F. Zenke; E. Agnes; W. Gerstner 

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

D. S. Corneil; W. Gerstner 

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

H. Setareh; M. Deger; W. Gerstner 

Learning associations with a neurally-computed global novelty signal

M. Faraji; K. Preuschoff; W. Gerstner 

Music Learning with Long Short Term Memory Networks

F. F. Colombo 

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

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

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 

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 

Periodic versus Intermittent Adaptive Cycles in Quasispecies Coevolution

A. Seeholzer; E. Frey; B. Obermayer 

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

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

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

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

A new Mathematical Framework to Understand Single Neuron Computations

Computational principles of single neuron adaptation

Memory formation and recall in recurrent spiking neural networks

Statistical models of effective connectivity in neural microcircuits

Synaptic Learning Rules with Consolidation

Statistical structure of neural spiking under non-Poissonian stimulation

T. Schwalger; F. Droste; B. Lindner 

A biologically plausible model of the learning rate dynamics

M. Faraji; K. Preuschoff; W. Gerstner 

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

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

The role of interconnected hub neurons in cortical dynamics

H. Setareh; M. Deger; W. Gerstner 

Learning Multi-Stability in Plastic Neural Networks

F. Zenke; E. Agnes 

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

M. Faraji; K. Preuschoff; W. Gerstner 

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

M. Faraji; K. Preuschoff; W. Gerstner 

Second Order Phase Transition Describes Maximally Informative Encoding in the Retina

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

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 

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 

Patterns of interval correlations in neural oscillators with adaptation

T. Schwalger; B. Lindner 

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

F. Zenke; G. Hennequin; 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 

Temporal whitening by power-law adaptation in neocortical neurons

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

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 

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

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

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

N. Frémaux; H. Sprekeler; W. Gerstner 

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 

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

S. Mensi; C. A. Pozzorini; O. Hagens 

Temporal decorrelation by power-law adaptation in pyramidal neurons

C. A. Pozzorini; R. Naud; S. Mensi 

Perceptual learning, roving and the unsupervised bias

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

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

R. Naud; W. Gerstner 

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 

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. 

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 

Theory and Simulation in Neuroscience

W. Gerstner; H. Sprekeler; G. Deco 

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

F. Gerhard; L. Szegletes 

Analysing Neuronal Network Architectures : From Weight Distributions to Structure and Back

Slow Feature Analysis

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

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

H. Sprekeler; L. Wiskott 

A history of spike-timing-dependent plasticity

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

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

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

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 

Neural mechanisms and computations underlying stress effects on learning and memory

G. Luksys; C. Sandi 

Improved Similarity Measures for Small Sets of Spike Trains

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

On the Relation of Slow Feature Analysis and Laplacian Eigenmaps

H. Sprekeler 

Applying the Multivariate Time-Rescaling Theorem to Neural Population Models

F. Gerhard; R. Haslinger; G. Pipa 

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 

Models of Evidence Integration in Rapid Decision Making Processes

The Dynamics of Adapting Neurons

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

F. Gerhard; G. Pipa; W. Gerstner 

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

F. Gerhard; G. Pipa; W. Gerstner 

Perceptual Learning, Roving and the Unsupervised Bias

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

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

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

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

N. Frémaux; H. Sprekeler; W. Gerstner 

Voltage and spike timing interact in STDP – a unified model

C. Clopath; W. Gerstner 

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 

PyBrain

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

Spike-timing dependent plasticity

W. Gerstner; J. Sjostrom 

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 

A two stage model for perceptual decision making

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

Complexity and performance in simple neuron models

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

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

F. Gerhard; R. Haslinger; G. Pipa 

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

R. H. Haslinger; F. Gerhard; 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 

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 

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 

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

G. Luksys; W. Gerstner; C. Sandi 

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

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

How Good are Neuron Models?

W. Gerstner; R. Naud 

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 

Interpretation of neuronal response properties with simplified neuron models

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

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. 

Phenomenological models of synaptic plasticity based on spike timing

A. Morrison; M. Diesmann; W. Gerstner 

PyNEST: a convenient interface to the NEST simulator

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

Special issue on quantitative neuron modeling

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

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. 

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

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

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

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

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

B. Bathellier; A. Carleton 

PyNN: a common interface for neuronal network simulators

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

Modeling spatial and temporal aspects of visual backward masking

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

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

F. Creutzig; H. Sprekeler 

The quantitative single-neuron modeling competition

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

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

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

An online Hebbian learning rule that performs Independent Component Analysis

C. Clopath; A. Longtin; W. Gerstner 

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 

Consciousness & the Small Network Argument

M. H. Herzog; M. Esfeld; W. Gerstner 

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

H. Sprekeler; C. Michaelis; L. Wiskott 

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

M. Franzius; H. Sprekeler; L. Wiskott 

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 : a computational model of rodent behavior

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 

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

J. P. Pfister; W. Gerstner 

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

M. J. E. Richardson; W. Gerstner 

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

Y. Aviel; W. Gerstner 

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

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

Predicting spike timing of neocortical pyramidal neurons by simple threshold models

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 

Integrate-and-Fire models with adaptation are good enough

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

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 

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

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

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 

A computational model of parallel navigation systems in rodents

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

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

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

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

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

Modelling Path Integrator Recalibration Using Hippocampal Place cells

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

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

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

Spatial Representation and Navigation in a Bio-inspired Robot

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

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

R. Jolivet; W. Gerstner 

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

L. Badel; A. Tonnelier 

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

J. Mayor; W. Gerstner 

Brain-Actuated Interaction

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

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 

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

A. Arleo; F. Smeraldi; W. Gerstner 

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

A. Tonnelier; W. Gerstner 

Bifurcation properties of the average activity of interconnected neural populations

A. Tonnelier 

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 

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

J. Mayor; W. Gerstner 

Optimal Hebbian Learning: a Probabilistic Point of View

J-P. Pfister; D. Barber; W. Gerstner 

Homeostasis and Learning through Spike-Timing Dependent Plasticity

L. Abbott; W. Gerstner 

Reinforcement Learning in Continuous State and Action Space

T. Strösslin; W. Gerstner 

Stable Propagation of Activity Pulses in Populations of Spiking Neurons

W. M. Kistler; W. Gerstner 

Mathematical Formulations of Hebbian Learning

W. Gerstner; W. K. Kistler 

Spiking Neuron Models

W. Gerstner; W. K. Kistler 

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

W. Gerstner 

Intrinsic Stabilization of Output Rates by Spike-Based Hebbian Learning

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

Spatial orientation in navigating agents: Modeling head-direction cells

A. Arleo; 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 

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

M. Spiridon; 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 

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 

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

M. Spiridon; C. C. Chow; 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 

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

A. Arleo; W. Gerstner 

Noise-shaping in populations of coupled model neurons

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

Hebbian learning and spiking neurons

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

Noise spectrum and signal transmission trough a population of spiking neurons

M. Spiridon; W. Gerstner 

Rapid signal transmission by a population of spiking neurons

W. Gerstner 

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

A. Arleo; 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