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

Wulfram Gerstner - Main Publications (and link to simulation code)

Code

Main publications

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

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

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

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

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
Link to simulation code on LCN github

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

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. Modirshanechi, J. Brea, and W. Gerstner (2022)
A taxonomy of surprise definitions
J. Mathem. Psychol. 110:102712, DOI: 10.1016/j.jmp.2022.102712
Link to simulation code on LCN github

S. Wang, V. Schmutz, G. Bellec and W. Gerstner (2022)
Mesoscopic modeling of hidden spiking neurons
NeurIPS 2022. arXiv:2205.13493
Link to simulation code on LCN github

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
Link to simulation code on github

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)
Link to simulation code on LCN github

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)
Link to simulation code on LCN github.

O. Gozel and W. Gerstner (2021)
A functional model of adult dentate gyrus neurogenesis
eLife 10:e66463 doi: 10.7554/eLife.66463
Link to simulation code on LCN github

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.
Link to code on LCN github

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
Link to simulation and analysis code on LCN github

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, 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
Link to simulation code on LCN github

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

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 ; and Link to simulation code on LCN github

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 ; and Link to simulation code on LCN github

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

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
Link to simulation code on LCN github
NEST Code on github see https://github.com/mdeger/nest-simulator/blob/stdp_structpl_synapse/models/stdp_structpl_connection_hom.h

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
Link to simulation code on LCN github; AND [ 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
Link to simulation code on LCN github
link to simulation code on LCN github

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
link to simulation code on LCN github

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

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

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
Link to simulation Code on LCN github and direct
link to code on github: GIFFitting Toolbox

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
Link to simulation code on LCN github
Link to AURYN simulator code

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
link to simulation code on LCN github
link to code on github

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
Link to simulation code on LCN github
link to code on github

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

G. Hennequin, T.P. Vogels and W. Gerstner (2014)
Optimal Control of Transient Dynamics in Balanced Networks Supports Generation of Complex Movements
NEURON 82: 1394-1406 PREPRINT version

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

F. Zenke, G. Hennequin and W. Gerstner (2013)
Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector
PLOS Comput. Biol. 9:e1003330 DOI: 10.1371/journal.pcbi.1003330

W. Gerstner and H. Sprekeler and G. Deco (2012)
Theory and Simulation in Neuroscience
Science 338:60-65 (2012) Preprint -- or -- Public link to Full Text in SCIENCE as html

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

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, 334:1569-1573, 2011.

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

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

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 published article

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

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

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

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

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)

BOOK: W. Gerstner and W. Kistler (2002)
`Spiking Neuron Models - Single Neurons, Populations, Plasticity'
Cambridge Univ. Press, Cambridge UK [ pdf file] (extracts from preprint version) and [ HTML Online Version ]

A. Arleo and W. Gerstner (2000)
Spatial Cognition and Neuro-Mimetic Navigation: A Model of Hippocampal Place Cell Activity
Biological Cybernetics, 83:287-299. [pdf file],

W. Gerstner (2000)
Population Dynamics of Spiking Neurons: Fast Transients, Asynchronous States, and Locking.
Neural Computation 12:43-89.
[ pdf file]

R. Kempter, W. Gerstner, and J. L. van Hemmen (1999)
Hebbian Learning and Spiking Neurons
Physical Review E, 59:4498-4514 Abstract [ pdf file]

Gerstner W, Kempter R, van Hemmen JL, and Wagner H (1996)
A neuronal learning rule for sub-millisecond temporal coding.
Nature, 383 :76-78
Abstract, [ figs.pdf ] [ reprint.pdf ]

Gerstner W, van Hemmen JL, and Cowan JD (1996)
What matters in neuronal locking
Neural Computation 8:1653-1676
- Abstract, - text.ps.Z, - figs.ps.Z,

Gerstner W, Ritz R, and van Hemmen JL (1993)
Why spikes? Hebbian learning and retrieval of time-resolved excitation patterns .
Biol. Cybern. 69 :503-515 [ pdf file]

Gerstner W and van Hemmen JL (1992)
Associative memory in a network of 'spiking' neurons .
Network 3 :139-164