Laboratory of Computational Neuroscience LCN - Brain Mind - I&C - Life Sciences - EPFL

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Wulfram Gerstner - Publications

C. Sourmpis, C.C.H. Petersen, W. Gerstner, and G. Bellec (2024)
Biologically informed cortical models predict optogenetic perturbations
BioRxiv DOI 10.1101/2024.09.27.615361

A. Stanojevic, S. Wozniak, G. Bellec, G. Cherubini, A. Pantazi, and W. Gerstner (2024)
High-performance deep spiking neural networks with 0.3 spikes per neuron
Nature Comm. 15:6793 DOI 10.1038/s41467-024-51110-5

L. Pezon, V. Schmutz, and W. Gerstner (2024)
Linking Neural Manifolds to Circuit Structure in Recurrent Networks
BioRxiv, DOI 10.1101/2024.02.28.582565

V. Schmutz, J. Brea, and W. Gerstner (2024)
Emergent rate-based dynamics in duplicate-free populations of spiking neurons
arXiv preprint arXiv:2303.05174; accepted to appear in Physical Review Letters.

C. Gastaldi and W. Gerstner (2024)
A computational framework for memory engrams
In: J. Graff and S. Ramirez (eds) Engrams. Adv. Neurobiol. vol. 38 (Springer) :237-257; DOI 10.1007/978-3-031-62983-9_13

S. Becker, A. Modirshanechi and W. Gerstner (2024)
Computational models of intrinsic motivation for curiosity and creativity
Behavioral and Brain Science 47:e94; DOI 10.1017/S0140525X23003424

A. Oryshchuck, C. Sourmpis, ..., A. Modirshanechi, W. Gerstner, C.C.H. Petersen, and S. Crochet (2024)
Distributed and specific encoding of sensory, motor, and decision information in the mouse neocortex during goal-directed behavior
Cell Reports 43: 113618 ; DOI 10.1016/j.celrep.2023.113618

C.S.N. Brito and W. Gerstner (2024)
Learning what matters: synaptic plasticity with invariance to second-order input correlations
PLOS Comput. Biol. 20:e1011844 DOI 10.1371/journal.pcbi.1011844

M.L.L.R. Barry and W. Gerstner (2024)
Fast adaptation to rule switching using neuronal surprise
PLOS Comput. Biol. 20:e1011839 DOI 10.1371/journal.pcbi.1011839

F. Martinelli, B. Simsek, J. Brea, and W. Gerstner (2024)
Expand-and-Cluster: Parameter Recovery of Neural Networks
Proceedings of the 41st International Conference on Machine Learning (ICML), Vienna, Austria. PMLR 235, DOI 10.48550/arXiv.2304.12794

J. Brea, N. Clayton, and W. Gerstner (2023)
Computational models of episodic-like memory in food-caching birds
Nature Comm. 14:2979 DOI 10.1038/s41467-023-38570-x

A. Modirshanechi, K. Kondrakievicz, W. Gerstner, and S. Haesler (2023)
Curiosity-driven exploration: foundations in neuroscience and computational modeling
Trends Neurosci. 46:1054-1066; DOI: 10.1016/j.tins.2023.10.002

A. Modirshanechi, S. Becker, J. Brea, and W. Gerstner (2023)
Surprise and novelty in the brain
Curr. Opinion Neurobiol. 82:102758 DOI 10.1016/j.conb.2023.102758

A. Stanojevic, S. Wozniak, G. Bellec, G. Cherubini, A. Pantazi, W. Gerstner (2023)
An exact mapping from ReLU networks to spiking neural networks
Neural Networks 168: 74-88 DOI 10.1016/j.neunet.2023.09.011

C. Sourmpis, C. Petersen, W. Gerstner, G. Bellec (2023)
Trial matching: capturing variability with data-constrained spiking neural networks
NeurIPS 2023, arXiv:2306.03603; DOI 10.48550/arXiv.2306.03603.

B. Simsek, A. Bendjeddou, W. Gerstner, J. Brea (2023)
Should Under-parameterized Student Networks Copy or Average Teacher Weights?
NeurIPS 2023. arXiv:2311.01644; DOI 10.48550/arXiv.2311.01644

A. Modirshanechi*, H.A. Xu*, W.H. Lin, M.H. Herzog, and W. Gerstner (2022)
The curse of optimism: a persistent distraction by novelty
BioRxiv. DOI: 10.1101/2022.07.05.498835

A. Modirshanechi, J. Brea, and W. Gerstner (2022)
A taxonomy of surprise definitions
J. Mathem. Psychol. 110:102712, DOI: 10.1016/j.jmp.2022.102712

V. Liakoni, M.P. Lehmann, A. Modirshanechi, J. Brea, A. Lutti, W. Gerstner, and K. Preuschoff (2022)
Brain signals of a Surprise-Actor-Critic model: Evidence for multiple learning modules in human decision making
NeuroImage, 246:118780

S. Wang, V. Schmutz, G. Bellec and W. Gerstner
Mesoscopic modeling of hidden spiking neurons
NeurIPS 2022. arXiv:2205.13493

G. Iatropoulus, J. Brea, and W. Gerstner (2022)
Kernel Memory Networks: a unifying framework for memory modeling
NeurIPS 2022 arXiv:2208.09416

A. Modirshanechi, J. Brea, and W. Gerstner (2021)
Surprise: a unified theory and experimental predictions
BioRxiv, DOI 10.1101/2021.11.01.466796

C. Gastaldi, T. Schwalger, E. De Falco E, R.Q. Quiroga, and W. Gerstner (2021)
When shared concept cells support associations: Theory of overlapping memory engrams.
PLoS Comput Biol 17(12): e1009691. https://doi.org/10.1371/journal.pcbi.1009691

O. Gozel and W. Gerstner (2021)
A functional model of adult dentate gyrus neurogenesis
eLife 10:e66463 doi: 10.7554/eLife.66463

H.A. Xu, A. Modirshanechi, M.P. Lehmann, W. Gerstner, M.H. Herzog (2021)
Novelty is not Surprise: Human exploratory and adaptive behavior in sequential decision-making
PLoS Comput Biol 17: e1009070. doi: https://doi.org/10.1371/journal.pcbi.1009070

G. Bellec, S. Wang, A. Modirshanechi, J. Brea, and W. Gerstner (2021)
Fitting summary statistics of neural data with a differentiable spiking network simulator
35th Conference on Neural Information Processing Systems (NeurIPS 2021)
IN: Advances in Neural Information Processing, Vol. 34, edited by M. Ranzato et al., pp. 18552--18563

B. Illing, J. Ventura, G. Bellec, and W. Gerstner (2021)
Local plasticity rules can learn deep representations using self-supervised contrastive predictions
35th Conference on Neural Information Processing Systems (NeurIPS 2021).
IN: Advances in Neural Information Processing, Vol. 34, edited by M. Ranzato et al., pp. 30365--30379

B. Simsek, F. Ged, A. Jacot, F. Spadaro, C. Hongler, W. Gerstner, and J. Brea (2021)
Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances .
Proceedings of the 38th International Conference on Machine Learning (ICML), PMLR 139:9722-9732, 2021.

V. Esmaeili, K. Tamural, S.P. Muscinelli, A. Modirshanechi et al. (2021)
Rapid suppression and sustained activation of distinct cortical regions for a delayed sensory-triggered motor response
NEURON 109:1-19 doi: https://doi.org/10.1016/j.neuron.2021.05.005

V. Liakoni, A. Modirshanechi, W. Gerstner, and J. Brea (2021)
Learning in Volatile environments with the Bayes Factor Surprise
Neural Computation 33: 1-72 and link to simulation code

V. Schmutz, W. Gerstner, and T. Schwalger (2020)
Mesoscopic population equations for spiking neural networks with synaptic short-term plasticity.
J. Math. Neurosc. 10:5 doi: 10.1186/s13408-020-00082-z

B. Illing, W. Gerstner, G. Bellec (2020)
Towards truly local gradients with CLAPP: Contrastive, Local And Predictive Plasticity
arXiv 2010.08262 Presented at NeurIPS workshop 2020.

C. Meissner-Bernard, M. Tsai, L. Logiaco, and W. Gerstner (2020)
Dendritic voltage recordings explain paradoxical synaptic plasticity: a modeling study
Frontiers Synaptic Neuroscience. doi: 10.3389/fnsyn.2020.585539

S.C. Surace, J.-P. Pfister, W. Gerstner, and J. Brea (2020)
On the choice of metric in gradient-based theories of brain function
PLoS ComputBiol 16(4):e1007640,
doi: 10.1371/journal.pcbi.1007640

M.P. Lehmann, H.A. Xu, V. Liakoni, M.H. Herzog, W. Gerstner, and K. Preuschoff (2019)
One-shot learning and behavioral eligibility-traces in sequential decision making
eLife 8:e47463 doi: 10.7554/eLife.47463
preprint on arXiv: 1707.04192

C. Gastaldi, S.P. Muscinelli, and W. Gerstner (2019)
Optimal stimulation protocol in a bistable synaptic consolidation model.
Frontiers in Computational Neuroscience 13:78,
doi: 10.3389/fncom.2019.00078
preprint on arXiv:1805.10116.

S. Muscinelli, W. Gerstner, and T. Schwalger (2019)
How single neuron properties shape chaotic dynamics and signal transmission in random neural networks
PLOS Comput. Biol. 15:e1007122, doi:10.1371/journal.pcbi.1007122 pdf-file

B. Illing, W. Gerstner, and J. Brea (2019)
Biologically plausible deep learning, but how far can we go with shallow networks?
Neural Networks 118:90-101, DOI: 10.1016/j.neunet.2019.06.001 pdf-file

J. Brea, B. Simsek, B. Illing, and W. Gerstner (2019)
Weight-space symmetry in deep networks gives rise to permutation saddles, connected by equal-loss valleys across the loss landscape
arXiv:1907.02911

F. Colombo, J. Brea, and W. Gerstner (2019)
Learning to Generate Music with BachProp,
16th Sound and Music Computing Conference, pp. 380-386

A. Seeholzer, M. Deger, and W. Gerstner (2019)
Stability of working memory in continuous attractor networks under the control of short-term plasticity
PLOS Comput. Biol. 15:e1006928.
doi: 10.1371/journal.pcbi.1006928 pdf file

W. Gerstner, M. Lehmann, V. Liakoni, and J. Brea (2018)
Eligibility traces and plasticity on behavioral time scales: experimental support of NeoHebbian three-factor learning rules.
Front. Neural Circuits, 12:53 doi: 10.3389/fncir.2018.00053 pdf file

H. Setareh, M. Deger and W. Gerstner (2018)
Excitable neuronal assemblies with adaptation as a building block of brain circuits for velocity-controlled signal propagation.
PLoS Comput Biol 14(7): e1006216. doi: 10.1371/journal.pcbi.1006216

M. Faraji, K. Preuschoff and W. Gerstner (2018)
Balancing New Against Old Information: The Role of Puzzlement Surprise in Learning
Neural Computation 30: 34-83 preprint on ArXiv

M. Deger, A. Seeholzer and W. Gerstner (2018)
Multicontact Co-operativity in Spike-Timing-Dependent Structural Plasticity Stabilizes Networks.
Cerebral Cortex 28: 1396-1415 doi: 10.1093/cercor/bhx339
preprint on ArXiv (2017)
NEST Code on github see https://github.com/mdeger/nest-simulator/blob/stdp_structpl_synapse/models/stdp_structpl_connection_hom.h

D. Corneil, W. Gerstner, and J. Brea (2018)
Efficient Model - Based Deep Reinforcement Learning with Variational State Tabulation.
Proceedings of the International Conference on Machine Learning (ICML), Stockholm, Sweden, PMLR, 80:1049-1058

A. Gilra and W. Gerstner
Non-linear motor control by local learning in spiking neural networks
Proceedings of the International Conference on Machine Learning (ICML), Stockholm, Sweden, PMLR 80:1773-1782

M. Martinolli and W. Gerstner and A. Gilra (2018)
Multi-Timescale Memory Dynamics Extend Task Repertoire in a Reinforcement Learning Network With Attention-Gated Memory
Front. Comput. Neurosci. 12:50. doi: 10.3389/fncom.2018.00050

S.P. Muscinelli, W. Gerstner, and T. Schwalger (2018)
Single neuron properties shape chaotic dynamics in random neural networks
arXiv:1812.06925; journal version appeared in PLOS Comput. Biol. 15:e1007122 (2019)

A. Gilra and W. Gerstner (2017)
Predicting nonlinear dynamics by stable local learning in a recurrent spiking neural network
eLife 6:e28295 doi: 10.7554/eLife.28295 [ pdf file ]

T. Schwalger, M. Deger and W. Gerstner (2017)
Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size.
PLoS Comput Biol 13(4): e1005507 doi.org/10.1371/journal.pcbi.1005507

F. Zenke, W. Gerstner, and S. Ganguli (2017)
The temporal paradox of Hebbian learning and homeostatic plasticity
Current Opinion in Neurobiology 2017, 43 :166-176 http://doi.org/10.1016/j.conb.2017.03.015
and link to preprint on BioRXiv.

S.P. Muscinelli, W. Gerstner, and J. Brea
Exponentially Long Orbits in Hopfield Neural Networks
Neural Computation 29, 458 - 484 (2017)
doi:10.1162/NECO_a_00919

F. Zenke and W. Gerstner (2017)
Hebbian plasticity requires compensatory processes on multiple timescales
Phil. Trans. R. Soc. B 372: 20160259. --- http://dx.doi.org/10.1098/rstb.2016.0259

H. Setareh, M. Deger, C.C.H. Petersen, and W Gerstner (2017)
Cortical dynamics in presence of assemblies of densely connected weight-hub neurons
Frontiers in computational neuroscience 11:52 --- https://doi.org/10.3389/fncom.2017.00052

T. Keck, T. Toyoizumi, L. Chen, B. Doiron, D.E. Feldman, K. Fox, W. Gerstner, ... et al. (2017)
Integrating Hebbian and homeostatic plasticity: the current state of the field and future research directions
Phil. Trans. R. Soc. B 372 (1715), 20160158

F. Colombo, A. Seeholzer, and W. Gerstner (2017)
Deep artificial composer: A creative neural network model for automated melody generation.
International Conference on Evolutionary and Biologically Inspired Music and Art. EvoMUSART 2017. Lecture Notes in Computer Science, vol 10198. Springer, pages 81-96

C.S.N. Brito and W. Gerstner (2016)
Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation
PLoS Comput Biol 12: e1005070. doi:10.1371/journal.pcbi.1005070

J. Brea and W. Gerstner (2016)
Does computational neuroscience need new synaptic learning paradigms?
Current Opinion in Behavioral Sciences 11:61-66 doi: 10.1016/j.cobeha.2016.05.012

S. Mensi, O. Hagens, W. Gerstner, and C. Pozzorini (2016)
Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons
PLoS Comput Biol 12: e1004761. doi:10.1371/journal.pcbi.1004761

D.B. Kastner, T. Schwalger, L. Ziegler, and W. Gerstner
A model of synaptic reconsolidation
Frontiers in Neuroscience 10:206 doi: 10.3389/fnins.2016.00206

N. Fremaux and W. Gerstner (2016)
Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules
Front. Neural Circuits 9:85 doi: 10.3389/fncir.2015.00085

F. Zenke and E.J. Agnes and W. Gerstner (2015)
Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks
Nature Comm. 6: 6922

C. Pozzorini, S. Mensi, O. Hagens, R. Naud, C. Koch, and W. Gerstner (2015)
Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models
PLOS Comput. Biol. 11:e1004275

L. Ziegler, F. Zenke, D.B. Kastner, and W. Gerstner (2015)
Synpatic Consolidation: From Synapses to behavioral modeling
J. Neuroscience 35:1319-1334
link to Journal WEB page and link to Feature article in J. Neuroscience, Jan. 2015

D. Corneil and W. Gerstner (2015)
Attractor Network Dynamics Enable Preplay and Rapid Path Planning in Maze like Environments
in: Neural Information Processing Systems 28 (NIPS 2015), ed. by C. Cortes and N. D. Lawrence and D. D. Lee and M. Sugiyama and R. Garnett, pp. 1675-1683

D. J. Rezende and W. Gerstner (2014)
Stochastic variational learning in recurrent spiking networks
Frontiers In Computational Neuroscience 8:38 doi.org/10.3389/fncom.2014.00038
download pdf

R. Naud, B. Bathellier and W. Gerstner (2014)
Spike-timing prediction in cortical neurons with active dendrites
Front. Comput. Neurosci. 8:90 doi: 10.3389/fncom.2014.00090 download pdf

M. Deger, T. Schwalger, R. Naud, and W. Gerstner (2014)
Fluctuations and information filtering in coupled populations of spiking neurons with adaptation
Phys. Rev. E 90, 062704 download pdf

C. Tomm, M. Avermann, C. Petersen, W. Gerstner and T.P. Vogels (2014)
Connection-type-specific biases make uniform random network models consistent with cortical recordings ,
J. Neurophysiology 112:1801-1814

F. Zenke and W. Gerstner (2014)
Limits to high-speed simulations of spiking neural networks using general-purpose computers
Frontiers in neuroinformatics 8:76 doi:10.3389/fninf.2014.00076

C. Pozzorini, R. Naud, S. Mensi, and W. Gerstner (2013)
Temporal whitening by power-law adaptation in neocortical neurons
Nature Neuroscience 16:942 - 948. PREPRINT version

N. Fremaux and H. Sprekeler and W. Gerstner (2013)
Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons
PLOS Comput. Biol. 9: e1003024. doi:10.1371/journal.pcbi.1003024

V. Pawlak, D.S. Greenberg, H. Sprekeler, W. Gerstner, Jason ND Kerr (2013)
Changing the responses of cortical neurons from sub- to suprathreshold using single spikes in vivo
eLIFE - DOI: http://dx.doi.org/10.7554/eLife.00012
See also comment by: Costa, Watt, Sjostrom

R. Naud and W. Gerstner (2013)
Can we predict every spike?
IN: Spike Timing: Mechanisms and Function, P.M Dilorenzo and J.D. Victor (Editors) CRC Press

H. Lutcke, F. Gerhard, F. Zenke, W. Gerstner and F. Helmchen (2013)
Inference of neuronal network spike dynamics and topology from calcium imaging data
Front. Neural Circuits 7:201 doi: 10.3389/fncir.2013.00201

J. Ruter, H. Sprekeler, W. Gerstner, and M.H. Herzog (2013)
The Silent Period of Evidence Integration in Fast Decision Making
PLOS ONE, 8: e46525

W. Gerstner and H. Sprekeler and G. Deco (2012)
Theory and Simulation in Neuroscience
Science 338:60-65 (2012) Preprint

G. Hennequin and T.P. Vogels and W. Gerstner (2012)
Non-normal amplification in random balanced neuronal networks
PHYSICAL REVIEW E 86:011909

R. Naud and W. Gerstner (2012)
Coding and Decoding with Adapting Neurons: A Population Approach to the Peri-Stimulus Time Histogram
PLOS Comput. 8:e100271

R. Naud and W. Gerstner (2012)
The performance (and limits) of simple neuron models: Generalizations of the leaky integrate-and-fire model
IN: Computational Systems Neurobiology, Nicolas Le Novere (Editor), Springer ISBN-10: 9400738579 ISBN-13: 978-9400738577

M. Avermann, C. Tomm, C. Mateo, W. Gerstner and C.C.H. Petersen (2012)
Microcircuits of excitatory and inhibitory neurons in layer 2/3 of mouse barrel cortex.
J. Neurophysiol 107:3116-3134

S. Mensi, R. Naud, C. Pozzorini, M. Avermann, C.C.H. Petersen and W. Gerstner (2012)
Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms,
J. Neurophysiol., 107:1756-1775, 2012.

M.H. Herzog, K.C. Aberg, N. Fremaux W. Gerstner and H. Sprekeler,
Perceptual learning, roving and the unsupervised bias,
Vision Research, Vol. 61, pp. 95-99, 2012.

J. Ruter, N. Marcille, H. Sprekeler, W. Gerstner and M.H. Herzog
Paradoxical Evidence Integration in Rapid Decision Processes,
PLOS Comput. Biol., 8: e1002382

T. Vogels, H. Sprekeler, F. Zenke, C. Clopath and W. Gerstner (2011)
Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks,
Science, Vol. 334:1569-1573, 2011.

R. Naud, F. Gerhard, S. Mensi, and W. Gerstner (2011)
Improved similarity measures for small sets of spike trains
Neural Computation 23:3016-3069

S. Mensi, R. Naud, and W. Gerstner (2011)
From Stochastic Nonlinear Integrate-and-Fire to Generalized Linear Models
Advances in Neural Information Processing Systems 24 edited by J. Shawe-Taylor and R.S. Zemel and P. Bartlett and F. Pereira and K.Q. Weinberger (2011) NIPS 2011, 24:0794

H. Markram and J. Sjostrom and W. Gerstner (2011)
A history of spike-timing-dependent plasticity
Front. Syn. Neurosci. 3:4. doi: 10.3389/fnsyn.2011.00004

F. Gerhard, G. Pipa, B. Lima, S. Neuenschwander, and W. Gerstner (2011)
Extraction of network topology from multi-electrode recordings: Is there a small-world effect?
Front. Comput. Neurosci. 5:4. doi: 10.3389/fncom.2011.00004

W. Gerstner (2011)
Hebbian Learning and Plasticity
To appear in: From Neuron to Cognition via Computational Neuroscience, edited by Michael Arbib and Jimmy Bonaiuto, MIT Press Cambridge Chapter 9

R. Naud and W. Gerstner (2011)
Can We Predict Every Spike?
IN: Spike Timing: Mechanisms and Function, Ed. by P.M. Dilorenzo and J.D. Victor, CRC Press planned to appear in 2012, now scheduled for March 2013

N. Fremaux, H. Sprekeler and W. Gerstner (2010)
Functional Requirements for Reward-Modulated Spike-Timing-Dependent Plasticity,
Journal of Neuroscience, Vol. 30, Nr. 40, pp. 13326-13337

Claudia Clopath, Lars Busing, Eleni Vasilaki and Wulfram Gerstner (2010)
Connectivity reflects coding: A model of voltage-based spike-timing-dependent-plasticity with homeostasis.
Nature Neuroscience, 13:344 - 352
doi:10.1038/nn.2479. Public Preprint version
Link to NEWS and VIEWS of N. Spruston and J. Cang

Claudia Clopath and Wulfram Gerstner (2010)
Voltage and spike timing interact in STDP - a unified model
Front. Syn. Neurosci., 2:25 doi: 10.3389/fnsyn.2010.00025

Guillaume Hennequin, Wulfram Gerstner and Jean-Pascal Pfister (2010)
STDP in adaptive neurons gives close-to-optimal information transmission
Front. Comput. Neurosci. 4:143, doi: 10.3389/fncom.2010.00143

W. Gerstner (2010)
From Hebb rules to STDP: a personal account
Front. Syn. Neurosci. 2:151. doi: 10.3389/fnsyn.2010.00151

Jesper Sjostrom and Wulfram Gerstner (2010)
Spike-timing dependent plasticity .
Scholarpedia, 5(2):1362 Here the pdf file with high-resolution figures

F. Gerhard and W. Gerstner (2010)
Rescaling, thinning or complementing? On goodness-of-fit procedures for point process models and Generalized Linear Models,
IN: Advances in Neural Information Processing Systems 23 (NIPS2010), edited by J. Lafferty and C. K. I. Williams and J. Shawe-Taylor and R.S. Zemel and A. Culotta 23:767

Eleni Vasilaki1, Nicolas Fremaux, Robert Urbanczik, Walter Senn, Wulfram Gerstner (2009)
Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail
PLoS Comput Biol 5(12): e1000586. doi:10.1371/journal.pcbi.1000586

Wulfram Gerstner and Richard Naud (2009)
How good are neuron models? Science, vol. 326: 379-380

Gediminas Luksys, Wulfram Gerstner, and Carmen Sandi (2009)
Stress, genotype and norepinephrine in the prediction of mouse behavior using reinforcement learning
Nature Neuroscience 12:1180-1186
Published online: 16 August 2009 | doi:10.1038/nn.2374

Wulfram Gerstner and Romain Brette (2009)
Adaptive exponential integrate-and-fire model.
Scholarpedia, 4:8427

Denis Sheynikhovich, Ricardo Chavarriaga, Thomas Strosslin, Angelo Arleo and Wulfram Gerstner (2009)
Is there a geometric module for spatial orientation? Insights from a rodent navigation model
Psychological Review, 116:540-566.
link to journal WEB site of this article

Wulfram Gerstner (2009). Spiking Neuron Models (short review article).
IN: Encyclopedia of Neuroscience. (L.R. Squire, Editor), p. 277-280 Oxford: Academic Press. pdf-file

Wulfram Gerstner (2008)
Spike-response model
Scholarpedia, 3(12):1343

Claudia Clopath, Lorric Ziegler, Eleni Vasilaki, Lars Busing, Wulfram Gerstner (2008)
Tag-Trigger-Consolidation: A Model of Early and Late Long-Term-Potentiation and Depression
PLoS Computational Biology 4(12): e1000248 doi:10.1371/journal.pcbi.1000248 pdf - file

Abigail Morrison, Markus Diesmann, and Wulfram Gerstner (2008)
Phenomenological models of synaptic plasticity based on spike timing
Biological Cybernetics, 98:459-478
pdf-file

Richard Naud, Nicolas Marcille, Claudia Clopath and Wulfram Gerstner (2008)
Firing patterns in the adaptive exponential integrate-and-fire model
Biological Cybernetics 99:335-347 pdf-file

Laurent Badel, Sandrine Lefort, Thomas K. Berger, Carl C. H. Petersen, Wulfram Gerstner and Magnus J. E. Richardson (2008c)
Extracting non-linear integrate-and-fire models from experimental data using dynamic I-V curves
Biological Cybernetics 99:361-370 pdf-file

Laurent Badel, Wulfram Gerstner and Magnus J. E. Richardson (2008b).
Spike-triggered Averages for Passive and Resonant Neurons Receiving Filtered Excitatory and Inhibitory Synaptic Drive.
Physical Review E, 78:011914 .
pdf-file

Laurent Badel, Sandrine Lefort, Romain Brette, Carl Petersen, Wulfram Gerstner and Magnus J.E. Richardson (2008)
Dynamic I-V Curves Are Reliable Predictors of Naturalistic Pyramidal-Neuron Voltage Traces,
J Neurophysiol 99: 656 - 666, 2008. pdf-file

Renaud Jolivet, Felix Schuermann, Thomas K. Berger, Richard Naud, Wulfram Gerstnera, and Arnd Roth (2008b)
The quantitative single-neuron modeling competition
Biological Cybernetics 99:417-426

R. Jolivet, R. Kobayashi, A. Rauch, R. Naud, S. Shinomoto, W. Gerstner (2008)
A benchmark test for a quantitative assessment of simple neuron models.
Journal of Neuroscience Methods 169: 417-424 pdf-file

F. Hermens, G. Luksys, U. Ernst W. Gerstner, M. H. Herzog (2008)
Modeling Spatial and Temporal Aspects of Visual Backward Masking
Psych. Rev. 115: 83-100 pdf-file

T. Toyoizumi, J.-P. Pfister, K. Aihara, and W. Gerstner (2007)
Optimality Model of Unsupervised Spike-Timing Dependent Plasticity: Synaptic Memory and Weight Distribution
Neural Computation, 19: 639-671 pdf-file AND link to Neural Computation article

C. Clopath, R. Jolivet, A. Rauch, H.-R. Luscher and W. Gerstner (2007)
Predicting neuronal activity with simple models of the threshold type: Adaptive Exponential Integrate-and-Fire model with two compartments
Neurocomputing 70:1668-1673,
from www.sciencedirect.com and local pdf file

C. Clopath, A. Longtin and W. Gerstner (2007)
An online Hebbian learning rule that performs independent component analysis
NIPS'07 Proceedings of the 20th International Conference on Neural Information Processing Systems, Curran Assoc. Inc, ISBN: 978-1-60560-352-0 Pages 321-328 and local pdf file

Jean-Pascal Pfister and W. Gerstner (2006)
Triplets of Spikes in a Model of Spike Timing-Dependent Plasticity
J. Neurosci., 26: 9673 - 9682 pdf file and high-resolution figures

J.-P. Pfister, T. Toyoizumi, D. Barber, and W. Gerstner (2006)
Optimal Spike-Timing Dependent Plasticity for Precise Action Potential Firing in supervised learning
Neural Computation 18:1309-1339

J.-P. Pfister and W. Gerstner (2006)
Beyond Pair-Based STDP: a Phenomenological Rule for Spike Triplet and Frequency Effects.
Available from: NIPS*2005 Online papers http://books.nips.cc/nips18.html.
IN: Advances in Neural Information Processing Systems 18}, Y. Weiss and B. Schölkopf and J. Platt, MIT Press, Cambridge, pp 1083--1090

M.J.E. Richardson and W. Gerstner (2006)
Statistics of subthreshold neuronal voltage fluctuations due to conductance-based synaptic shot noise
Chaos 16:026106

R. Jolivet, A. Rauch, H.-R. Luscher and W. Gerstner (2006)
Integrate-and-Fire models with adaptation are good enough
IN: Advances in Neural Information Processing Systems 18, Y. Weiss and B. Scholkopf and J. Platt, MIT Press, Cambridge, pp. 595--602

R. Jolivet, A. Rauch, H.-R. Lucher and W. Gerstner (2006)
Predicting spike timing of neocortical pyramidal neurons by simple threshold models
Journal of Computational Neuroscience 21:35-49
pdf file (preprint version)

Y. Aviel and W. Gerstner (2006)
From spiking neurons to rate models: a cascade model as an approximation to spiking neuron models with refractoriness
Phys. Rev. E 73, 051908
link to PRE and pdf file

Laurent Badel, Wulfram Gerstner and Magnus J.E. Richardson (2006)
Dependence of the spike-triggered average voltage on membrane response properties, Neurocomputing, Volume 69:1062-1065
Link to ScienceDirect and pdf file

PUBLICATIONS in 2005

Romain Brette and Wulfram Gerstner (2005)
Adaptive Exponential Integrate-and-Fire Model as an Effective Description of Neuronal Activity
J Neurophysiol, 94: 3637 - 3642

T. Toyoizumi, J.-P. Pfister, K. Aihara, and W. Gerstner (2005)
Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission
Proc. Natl. Acad. Sci. USA, 102:5239-5244
pdf-file (main text) and supporting information

M.J.E. Richardson and W. Gerstner (2005)
Synaptic shot noise and conductance fluctuations affect the membrane voltage with equal significance
Neural Computation, 17:923-947
pdf file (preprint version) full text (printed version, MIT-press)

T. Toyoizumi, J.-P. Pfister, K. Aihara, and W. Gerstner (2005)
Spike-timing Dependent Plasticity and Mutual Information Maximization for a Spiking Neuron Model
Advances in Neural Information Processing Systems 17 , edited by L.K. Saul and Y. Weiss and L. Bottou (MIT-Press), pp. 1409-1416
pdf-file

D. Sheynikhovich, R. Chavarriaga, T. Strösslin and W. Gerstner (2005)
Spatial Representation and Navigation in a Bio-inspired Robot
In: Biomimetic Neural Learning for Intelligent Robots: Intelligent Systems, Cognitive Robotics, and Neuroscience, edited by Stefan Wermter, Günther Palm, Mark Elshaw , pp. 245-264.
pdf-file

T. Strösslin, D. Sheynikhovich, R. Chavarriaga, and W. Gerstner (2005)
Robust self-localisation and navigation based on hippocampal place cells
NEURAL NETWORKS 18 (9): 1125-1140
pdf-file

T. Strösslin, R. Chavarriaga, D. Sheynikhovich, and W. Gerstner (2005)
Modeling Path Integrator Recalibration Using Hippocampal Place cells
In: ICANN 2005, edited by W. Duch et al., Lecture Notes in Computer Science Vol. 3696 , pp. 51-56.
pdf-file

Ricardo Chavarriaga, Thomas Strösslin, Denis Sheynikhovich and Wulfram Gerstner (2005)
A computational model of parallel navigation systems in rodents
Neuroinformatics, 3:223-241
pdf-file

Ricardo Chavarriaga, Thomas Strösslin, Denis Sheynikhovich and Wulfram Gerstner (2005)
Competition between cue response and place response: A model of rat navigation behaviour
Connection Science, 17: 167-183.
pdf-file

J. Mayor and W. Gerstner (2005)
Noise-enhanced computation in a model of a cortical column
NEUROREPORT 16 (11): 1237-1240 pdf-file (preprint version)

J. Mayor and W. Gerstner (2005)
Signal buffering in random networks of spiking neurons: microscopic vs. macroscopic phenomena
Phys. Rev. E 72, 051906 (2005) pdf-file

M.J.E. Richardson, O. Melamed, G. Silberberg, W. Gerstner and H. Markram (2005)
Short-term synaptic plasticity orchestrates the response of pyramical cells and interneurons to population bursts
J. Computational Neuroscience 18:323-331 pdf file

O. Melamed and G. Silberberg and H. Markram and W. Gerstner and M.J.E. Richardson (2004)
Subthreshold cross-correlations between cortical neurons: A reference model with static synapses.
Neurocomputing 65-66 (special issue of the CNS'04 conference):685-690 pdf file

PUBLICATIONS 2004

R. Jolivet and T. J. Lewis and W. Gerstner (2004)
Generalized Integrate-and-Fire Models of Neuronal Activity Approximate Spike Trains of a Detailed Model to a High Degree of Accuracy.
J. Neurophysiology 92: 959-976 pdf file (preprint version)

J. del R. Millan and F. Renkens and J. Mourino and W. Gerstner (2004b)
Brain actuated interaction
Artificial Intelligence, 159:241-259 [ pdf file]

J. del R. Millan and F. Renkens and J. Mourino and W. Gerstner (2004)
Non-invasive Brain actuated control of a mobile robot by Human EEG
IEEE Transactions Biomedical Engineering, 51:1026-1033 [ pdf file]

O. Melamed and W. Gerstner and W. Maass and M. Tsodyks and H. Markram (2004)
Coding and Learning of behavioral sequences
Trends in Neurosciences, 27:11-14

A. Arleo and F. Smeraldi and W. Gerstner (2004)
Cognitive navigation based on non-uniform Gabor space sampling, unsupervised growing networks, and reinforcement learning.
IEEE Transactions on Neural Networks, 15:639-652.

J. Mayor and W. Gerstner (2004)
Transient information flow in a network of excitatory and inhibitory model neurons: role of noise and signal autocorrelation
J. Physiology (Paris) Vol. 98: 417-428 pdf file

R. Jolivet and W. Gerstner (2004)
Predicting spike times of a detailed conductance-based neuron model driven by stochastic spike arrival.
available from Q-Bio archive: q-bio.NC/0407010
J. Physiology (Paris) Vol. 98: 442-451 [ pdf file ], [ link to J. Physiology (Paris) ]

PUBLICATIONS 2003

A. Tonnelier and W. Gerstner (2003)
Piecewise linear differential equations and integrate-and-fire neurons : insights from two-dimensional membrane models,
Phys. Rev. E 67, 021908, [ pdf file]

J. Mayor and W. Gerstner (2003)
Online processing of multiple inputs in a sparsely-connected recurrent neural network
Proc. Joint International Conference ICANN/ICONIP 2003, Kaynak et al. (Eds.), Springer, LNCS 2714, pp. 839-845, [ pdf file]

R. Jolivet, T.J. Lewis and W. Gerstner (2003)
The Spike Response Model: a Framework to Predict Neuronal Spike Trains.
Proc. Joint International Conference ICANN/ICONIP 2003, Kaynak et al. (Eds.), Springer, LNCS 2714, pp. 846-853, [ pdf file]

J.-P. Pfister, D. Barber, and W. Gerstner (2003)
Optimal Hebbian Learning: a Probabilistic Point of View.
Proc. Joint International Conference ICANN/ICONIP 2003, Kaynak et al. (Eds.), Springer, LNCS 2714, pp. 92-98, [ pdf file]

J. del R. Millan, F. Renkens, J. Mourino, and W. Gerstner,
Non-Invasive Brain-Actuated Control of a Mobile Robot. "
P roceedings of the 18th International Joint Conference on Artificial Intelligence, pp. 1121--1126 [ pdf file]",

Thomas Strösslin and Wulfram Gerstner (2003)
Reinforcement Learning in Continuous State and Action Space
presented at: Artificial Neural Networks - ICANN 2003 pdf file See also: PhD thesis, EPFL, Thomas Stroesslin

PUBLICATIONS 2002