Publications

2022

Journal Articles

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

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

Neural Computing & Applications. 2022-12-05. DOI : 10.1007/s00521-022-07910-1.

A taxonomy of surprise definitions

A. Modirshanechi; J. Brea; W. Gerstner 

Journal of Mathematical Psychology. 2022-09-09. Vol. 110, p. 102712. DOI : 10.1016/j.jmp.2022.102712.

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

V. Schmutz 

Stochastic Processes And Their Applications. 2022-07-01. Vol. 149, p. 39-59. DOI : 10.1016/j.spa.2022.03.006.

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. 

Neuroimage. 2022-02-01. Vol. 246, p. 118780. DOI : 10.1016/j.neuroimage.2021.118780.

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. 

Elife. 2022-01-18. Vol. 11, p. e71850. DOI : 10.7554/eLife.71850.

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

C. Fonte; V. M. Schmutz 

SIAM Journal on Mathematical Analysis. 2022. Vol. 54, num. 4, p. 4721-4756. DOI : 10.1137/21M1428571.

Mesoscopic modeling of hidden spiking neurons

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

2022. DOI : 10.48550/arxiv.2205.13493.

Kernel Memory Networks: A Unifying Framework for Memory Modeling

G. Iatropoulos; J. Brea; W. Gerstner 

2022. DOI : 10.48550/arxiv.2208.09416.

Reviews

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 

British Journal Of Educational Technology. 2022-11-28. DOI : 10.1111/bjet.13289.

Theses

Taming neuronal noise with large networks

V. M. Schmutz / W. Gerstner; E. Löcherbach (Dir.)  

Lausanne, EPFL, 2022. 

2021

Journal Articles

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

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

PLOS Computational Biology. 2021-12-30. Vol. 17, num. 12, p. e1009691. DOI : 10.1371/journal.pcbi.1009691.

Spike frequency adaptation supports network computations on temporally dispersed information

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

Elife. 2021-07-26. Vol. 10, p. e65459. DOI : 10.7554/eLife.65459.

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. 

Neuron. 2021-07-07. Vol. 109, num. 13, p. 2183-2201. DOI : 10.1016/j.neuron.2021.05.005.

A functional model of adult dentate gyrus neurogenesis

O. Gozel; W. Gerstner 

eLife. 2021-06-17. Vol. 10, p. e66463. DOI : 10.7554/eLife.66463.

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

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

PLoS Computational Biology. 2021-06-03. Vol. 17, num. 6, p. 1-32,e1009070. DOI : 10.1371/journal.pcbi.1009070.

Learning in Volatile Environments With the Bayes Factor Surprise

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

Neural Computation. 2021-02-01. Vol. 33, num. 2, p. 269-340. DOI : 10.1162/neco_a_01352.

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

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

Scientific Reports. 2021-01-12. Vol. 11, num. 1, p. 835. DOI : 10.1038/s41598-020-80515-7.

Conference Papers

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

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

2021. 38 th International Conference on Machine Learning (ICML 2021), Virtual, July 18-24, 2021. p. 9722-9732.

Theses

Biologically plausible unsupervised learning in shallow and deep neural networks

B. A. Illing / W. Gerstner (Dir.)  

Lausanne, EPFL, 2021. 

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

C. Gastaldi / W. Gerstner (Dir.)  

Lausanne, EPFL, 2021. 

Learning music composition with recurrent neural networks

F. F. Colombo / W. Gerstner (Dir.)  

Lausanne, EPFL, 2021. 

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

V. Liakoni / W. Gerstner; K. Preuschoff (Dir.)  

Lausanne, EPFL, 2021. 

Posters

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

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

35th Conference on Neural Information Processing Systems (NeurIPS 2021), Online, December 6-14, 2021.

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

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

35th Conference on Neural Information Processing Systems (NeurIPS 2021), Online, December 6-14, 2021.

Student Projects

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

S. Volodin 

2021-04-21.

2020

Journal Articles

Dendritic Voltage Recordings Explain Paradoxical Synaptic Plasticity: A Modeling Study

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

Frontiers In Synaptic Neuroscience. 2020-11-02. Vol. 12, p. 585539. DOI : 10.3389/fnsyn.2020.585539.

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

B. Pietras; N. Gallice; T. Schwalger 

Physical Review E. 2020-08-18. Vol. 102, num. 2, p. 022407. DOI : 10.1103/PhysRevE.102.022407.

Data-driven integration of hippocampal CA1 synaptic physiology in silico

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

Hippocampus. 2020-06-10. Vol. 30, num. 11, p. 1129-1145. DOI : 10.1002/hipo.23220.

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

V. Schmutz; W. Gerstner; T. Schwalger 

Journal Of Mathematical Neuroscience. 2020-04-06. Vol. 10, num. 1, p. 5. DOI : 10.1186/s13408-020-00082-z.

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

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

Plos Computational Biology. 2020-04-01. Vol. 16, num. 4, p. e1007640. DOI : 10.1371/journal.pcbi.1007640.

Conference Papers

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

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

2020-01-01. IEEE International Conference on Acoustics, Speech, and Signal Processing, Barcelona, SPAIN, May 04-08, 2020. p. 8544-8548. DOI : 10.1109/ICASSP40776.2020.9053412.

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

G. Dellaferrera; F. Martinelli; M. Cernak 

2020-01-01. IEEE International Conference on Acoustics, Speech, and Signal Processing, Barcelona, SPAIN, May 04-08, 2020. p. 3207-3211. DOI : 10.1109/ICASSP40776.2020.9054761.

2019

Journal Articles

Optimal Stimulation Protocol in a Bistable Synaptic Consolidation Model

C. Gastaldi; S. Muscinelli; W. Gerstner 

Frontiers In Computational Neuroscience. 2019-11-13. Vol. 13, p. 78. DOI : 10.3389/fncom.2019.00078.

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. 

eLife. 2019-11-11. Vol. 8, p. 1-25,e47463. DOI : 10.7554/eLife.47463.

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

B. Illing; W. Gerstner; J. Brea 

Neural Networks. 2019-10-01. Vol. 118, p. 90-101. DOI : 10.1016/j.neunet.2019.06.001.

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. 

Communications Biology. 2019-09-02. Vol. 2, p. 326. DOI : 10.1038/s42003-019-0571-7.

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

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

PLoS Computational Biology. 2019-06-01. Vol. 15, num. 6, p. e1007122. DOI : 10.1371/journal.pcbi.1007122.

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

A. Seeholzer; M. Deger; W. Gerstner 

Plos Computational Biology. 2019-04-01. Vol. 15, num. 4, p. e1006928. DOI : 10.1371/journal.pcbi.1006928.

Theses

From adult dentate gyrus neurogenesis to pattern separation

O. Gozel / W. Gerstner (Dir.)  

Lausanne, EPFL, 2019. 

2018

Journal Articles

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 

Journal Of Physiology-London. 2018-10-15. Vol. 596, num. 20, p. 4945-4967. DOI : 10.1113/JP276012.

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

M. Martinolli; W. Gerstner; A. Gilra 

Frontiers in Computational Neuroscience. 2018. Vol. 12, p. 50. DOI : 10.3389/fncom.2018.00050.

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

M. Deger; A. Seeholzer; W. Gerstner 

CEREBRAL CORTEX. 2018. Vol. 28, num. 4, p. 1396-1415. DOI : 10.1093/cercor/bhx339.

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

H. Setareh; M. Deger; W. Gerstner 

PLOS Computational Biology. 2018. Vol. 14, num. 7, p. e1006216. DOI : 10.1371/journal.pcbi.1006216.

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 

Frontiers in Neural Circuits. 2018. Vol. 12, p. 53. DOI : 10.3389/fncir.2018.00053.

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

M. Faraji; K. Preuschoff; W. Gerstner 

NEURAL COMPUTATION. 2018. Vol. 30, num. 1, p. 34-83. DOI : 10.1162/NECO_a_01025.

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

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

Scientific Reports. 2018. Vol. 8, p. 6297. DOI : 10.1038/s41598-018-24339-6.

Conference Papers

Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation

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

2018. ICML 2018 35th International Conference on Machine Learning, Stockholm, SWEDEN, July 10-15, 2018.

Theses

One-shot learning and eligibility traces in sequential decision making

M. P. Lehmann / W. Gerstner; K. Preuschoff (Dir.)  

Lausanne, EPFL, 2018. 

Slow dynamics in structured neural network models

S. P. Muscinelli / W. Gerstner (Dir.)  

Lausanne, EPFL, 2018. 

Model-based reinforcement learning and navigation in animals and machines

D. S. Corneil / W. Gerstner (Dir.)  

Lausanne, EPFL, 2018. 

Patents

Simplification of neural network models

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

US2022230052; US11301750; US2018285716.

2018.

2017

Journal Articles

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

A. Gilra; W. Gerstner 

Elife. 2017. Vol. 6, p. e28295. DOI : 10.7554/eLife.28295.

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

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

Frontiers in Computational Neuroscience. 2017. Vol. 11, p. 52. DOI : 10.3389/fncom.2017.00052.

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. 

Elife. 2017. Vol. 6, p. e22152. DOI : 10.7554/eLife.22152.

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

T. Schwalger; M. Deger; W. Gerstner 

PLoS Computational Biology. 2017. Vol. 13, num. 4, p. e1005507. DOI : 10.1371/journal.pcbi.1005507.

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

F. Gerhard; M. Deger; W. Truccolo 

Plos Computational Biology. 2017. Vol. 13, num. 2, p. e1005390. DOI : 10.1371/journal.pcbi.1005390.

Exponentially long orbits in Hopfield neural networks

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

Neural Computation. 2017. Vol. 29, num. 2, p. 458-484. DOI : 10.1162/NECO_a_00919.

Reviews

The temporal paradox of Hebbian learning and homeostatic plasticity

F. Zenke; W. Gerstner; S. Ganguli 

Current Opinion In Neurobiology. 2017. Vol. 43, p. 166-176. DOI : 10.1016/j.conb.2017.03.015.

Synaptic patterning and the timescales of cortical dynamics

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

Current Opinion In Neurobiology. 2017. Vol. 43, p. 156-165. DOI : 10.1016/j.conb.2017.02.007.

Hebbian plasticity requires compensatory processes on multiple timescales

F. Zenke; W. Gerstner 

Philosophical Transactions Of The Royal Society B-Biological Sciences. 2017. Vol. 372, num. 1715, p. 20160259. DOI : 10.1098/rstb.2016.0259.

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. 

Philosophical Transactions Of The Royal Society B-Biological Sciences. 2017. Vol. 372, num. 1715, p. 20160158. DOI : 10.1098/rstb.2016.0158.

Theses

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

H. Setareh / W. Gerstner (Dir.)  

Lausanne, EPFL, 2017. 

Continuous attractor working memory and provenance of channel models

A. K. Seeholzer / W. Gerstner (Dir.)  

Lausanne, EPFL, 2017. 

Working Papers

Evidence for eligibility traces in human learning

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

2017

2016

Journal Articles

Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation

C. S. N. Brito; W. Gerstner 

PLoS Computational Biology. 2016. Vol. 12, num. 9, p. e1005070. DOI : 10.1371/journal.pcbi.1005070.

Prospective Coding by Spiking Neurons

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

Plos Computational Biology. 2016. Vol. 12, num. 6, p. e1005003. DOI : 10.1371/journal.pcbi.1005003.

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

Y. Burnier; C. Gastaldi 

Physical Review C. 2016. Vol. 93, num. 4, p. 044902. DOI : 10.1103/PhysRevC.93.044902.

Balancing New Against Old Information: The Role of Surprise

M. Faraji; K. Preuschoff; W. Gerstner 

arXiv. 2016. 

Does computational neuroscience need new synaptic learning paradigms?

J. M. Brea; W. Gerstner 

Current Opinion in Behavioral Sciences. 2016. Vol. 11, p. 61-66. DOI : 10.1016/j.cobeha.2016.05.012.

A Model of Synaptic Reconsolidation

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

Frontiers in Neuroscience. 2016. Vol. 10, p. 206. DOI : 10.3389/fnins.2016.00206.

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

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

Plos Computational Biology. 2016. Vol. 12, p. e1004761. DOI : 10.1371/journal.pcbi.1004761.

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

N. Fremaux; W. Gerstner 

Frontiers in Neural Circuits. 2016. Vol. 9, p. 85. DOI : 10.3389/fncir.2015.00085.

A Multiscale Pyramid Transform for Graph Signals

D. Shuman; M. Faraji; P. Vandergheynst 

IEEE Transactions on Signal Processing. 2016. Vol. 64, num. 8, p. 2119-2134. DOI : 10.1109/TSP.2015.2512529.

Conference Papers

Algorithmic Composition of Melodies with Deep Recurrent Neural Networks

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

2016. 1st Conference on Computer Simulation of Musical Creativity, University of Huddersfield, UK, 17 – 19 June 2016. DOI : 10.13140/RG.2.1.2436.5683.

Theses

Learning with Surprise

M. Faraji / W. Gerstner (Dir.)  

Lausanne, EPFL, 2016. 

Theory of representation learning in cortical neural networks

C. Stein Naves de Brito / W. Gerstner (Dir.)  

Lausanne, EPFL, 2016. 

Posters

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

F. Colombo 

Lemanic Neuroscience Annual Meeting 2016, Les Diablerets, Switzerland, September 2-3, 2016.

Algorithmic composition of melodies with deep recurrent neural networks

F. Colombo 

Machine Learning Machine Learning Summer School 2016, Cadiz, Spain, May 11-21, 2016.

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

M. Faraji; K. Preuschoff; W. Gerstner 

Computational Neuroscience Meeting (CNS), Jeju Island, South Korea, July 2-7, 2016.

A novel information theoretic measure of surprise

M. Faraji; K. Preuschoff; W. Gerstner 

International Conference on Mathematical Neuroscience (ICMNS), Antibes – Juan Les Pins, France, May 30 – June 1, 2016.

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

M. Faraji; K. Preuschoff; W. Gerstner 

Computational and Systems Neuroscience (COSYNE), Salt Lake City, Utah, USA, February 25 – March 1, 2016.

2015

Journal Articles

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

T. Schwalger; B. Lindner 

Physical Review E. 2015. Vol. 92, num. 6, p. 062703. DOI : 10.1103/PhysRevE.92.062703.

Slow fluctuations in recurrent networks of spiking neurons

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

Physical Review E. 2015. Vol. 92, num. 4, p. 040901(R). DOI : 10.1103/PhysRevE.92.040901.

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. 

Plos Computational Biology. 2015. Vol. 11, num. 4, p. e1004275. DOI : 10.1371/journal.pcbi.1004275.

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

Y. V. Zaytsev; A. Morrison; M. Deger 

Journal of Computational Neuroscience. 2015. Vol. 39, num. 1, p. 77-103. DOI : 10.1007/s10827-015-0565-5.

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

L. Shiau; T. Schwalger; B. Lindner 

Journal Of Computational Neuroscience. 2015. Vol. 38, num. 3, p. 589-600. DOI : 10.1007/s10827-015-0558-4.

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

T. Schwalger; F. Droste; B. Lindner 

Journal of Computational Neuroscience. 2015. Vol. 39, num. 1, p. 29-51. DOI : 10.1007/s10827-015-0560-x.

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

F. Zenke; E. J. Agnes; W. Gerstner 

Nature Communications. 2015. Vol. 6, p. 6922. DOI : 10.1038/ncomms7922.

Synaptic consolidation: from synapses to behavioral modeling

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

The Journal of neuroscience : the official journal of the Society for Neuroscience. 2015. Vol. 35, num. 3, p. 1319-34. DOI : 10.1523/JNEUROSCI.3989-14.2015.

Conference Papers

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

D. S. Corneil; W. Gerstner 

2015. Neural Information Processing Systems (NIPS 2015), Montreal, Canada, December 07- 12, 2015. p. 1675-1683.

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

T. Schwalger; M. Deger; W. Gerstner 

2015.  p. P79. DOI : 10.1186/1471-2202-16-S1-P79.

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

M. Faraji; K. Preuschoff; W. Gerstner 

2015. The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM), Edmonton, Alberta, Canada, June 7-10, 2015.

Posters

Surprise minimization as a learning strategy in neural networks

M. Faraji; K. Preuschoff; W. Gerstner 

Computational Neuroscience Meeting (CNS), Prague, Czech Republic, July 18-23, 2015.

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

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

Computational and Systems Neuroscience (COSYNE), Salt Lake City, Utah, USA, March 5-10, 2015.

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

D. S. Corneil; W. Gerstner 

COSYNE 2015, Salt Lake City, March 5 to 10, 2015.

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

F. Zenke; E. Agnes; W. Gerstner 

COSYNE 2015, Salt Lake City, March 5 to 10, 2015.

Learning associations with a neurally-computed global novelty signal

M. Faraji; K. Preuschoff; W. Gerstner 

Computational and Systems Neuroscience (COSYNE), Salt Lake City, Utah, USA, March 5-10, 2015.

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

H. Setareh; M. Deger; W. Gerstner 

COSYNE 2015, Salt Lake City, March 5 to 10, 2015.

Student Projects

Music Learning with Long Short Term Memory Networks

F. F. Colombo 

2015.

2014

Journal Articles

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

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

Journal Of Neurophysiology. 2014. Vol. 112, num. 8, p. 1801-1814. DOI : 10.1152/jn.00629.2013.

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

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

Physical Review E. 2014. Vol. 90, num. 6, p. 062704. DOI : 10.1103/PhysRevE.90.062704.

Periodic versus Intermittent Adaptive Cycles in Quasispecies Coevolution

A. Seeholzer; E. Frey; B. Obermayer 

Physical Review Letters. 2014. Vol. 113, num. 12, p. 128101. DOI : 10.1103/PhysRevLett.113.128101.

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

F. Zenke; W. Gerstner 

Frontiers in neuroinformatics. 2014. Vol. 8, p. 76. DOI : 10.3389/fninf.2014.00076.

Spike-timing prediction in cortical neurons with active dendrites

R. Naud; B. Bathellier; W. Gerstner 

Frontiers in Computational Neuroscience. 2014. Vol. 8, p. 90. DOI : 10.3389/fncom.2014.00090.

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

Y. Rochat; F. Kaplan 

Les Cahiers du Numérique. 2014. Vol. 10, num. 3, p. 109-133. DOI : 10.3166/LCN.10.3.109‐133.

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

G. Hennequin; T. Vogels; W. Gerstner 

Neuron. 2014. Vol. 82, p. 1394-1406. DOI : 10.1016/j.neuron.2014.04.045.

Stochastic variational learning in recurrent spiking networks

D. J. Rezende; W. Gerstner 

Frontiers In Computational Neuroscience. 2014. Vol. 8, p. 38. DOI : 10.3389/fncom.2014.00038.

Books

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

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

Cambridge University Press, 2014.

Theses

Computational principles of single neuron adaptation

C. A. Pozzorini / W. Gerstner (Dir.)  

Lausanne, EPFL, 2014. 

A new Mathematical Framework to Understand Single Neuron Computations

S. Mensi / W. Gerstner (Dir.)  

Lausanne, EPFL, 2014. 

Memory formation and recall in recurrent spiking neural networks

F. Zenke / W. Gerstner (Dir.)  

Lausanne, EPFL, 2014. 

Synaptic Learning Rules with Consolidation

L. Ziegler / W. Gerstner (Dir.)  

Lausanne, EPFL, 2014. 

Statistical models of effective connectivity in neural microcircuits

J. E. F. Gerhard / W. Gerstner (Dir.)  

Lausanne, EPFL, 2014. 

Posters

A biologically plausible model of the learning rate dynamics

M. Faraji; K. Preuschoff; W. Gerstner 

Gordon Research Conference on Neurobiology of Cognition (GRC), Sunday River Resort – Newry, Maine, USA, July 20-25, 2014.

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

M. Faraji; K. Preuschoff; W. Gerstner 

Computational Neuroscience Meeting (CNS), Quebec City, Canada, July 26-31, 2014.

The role of interconnected hub neurons in cortical dynamics

H. Setareh; M. Deger; W. Gerstner 

CNS 2014, Quebec City, Canada, July 26-31, 2014.

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

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

COSYNE 2014, Salt Lake City & Snowbird, Utah, USA, February 27 – March 4, 2014.

Second Order Phase Transition Describes Maximally Informative Encoding in the Retina

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

COSYNE 2014, Salt Lake City & Snowbird, Utah, USA, February 27 – March 4, 2014.

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

M. Faraji; K. Preuschoff; W. Gerstner 

Computational and Systems Neuroscience (COSYNE), Salt Lake City, Utah, USA, February 27 – March 4, 2014.

Learning Multi-Stability in Plastic Neural Networks

F. Zenke; E. Agnes 

COSYNE 2014, Salt Lake City & Snowbird, Utah, USA, February 27 – March 4, 2014.

Network dynamics of spiking neurons with adaptation

M. Deger; T. Schwalger; R. Naud 

COSYNE 2014, Salt Lake City & Snowbird, Utah, USA, February 27 – March 4, 2014.

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

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

COSYNE 2014, Salt Lake City & Snowbird, Utah, USA, February 27 – March 4, 2014.

Statistical structure of neural spiking under non-Poissonian stimulation

T. Schwalger; F. Droste; B. Lindner 

COSYNE 2014, Salt Lake City & Snowbird, Utah, USA, February 27 – March 4, 2014.

2013

Journal Articles

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

F. Zenke; G. Hennequin; W. Gerstner 

Plos Computational Biology. 2013. Vol. 9, num. 11, p. e1003330. DOI : 10.1371/journal.pcbi.1003330.

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

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

Frontiers In Neural Circuits. 2013. Vol. 7, p. 201. DOI : 10.3389/fncir.2013.00201.

Patterns of interval correlations in neural oscillators with adaptation

T. Schwalger; B. Lindner 

Frontiers In Computational Neuroscience. 2013. Vol. 7, p. 164. DOI : 10.3389/fncom.2013.00164.

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 

Elife. 2013. Vol. 2, p. e00012. DOI : 10.7554/eLife.00012.001.

Temporal whitening by power-law adaptation in neocortical neurons

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

Nature Neuroscience. 2013. Vol. 16, num. 7, p. 942-966. DOI : 10.1038/nn.3431.

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

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

Plos Computational Biology. 2013. Vol. 9, num. 7, p. 003138. DOI : 10.1371/journal.pcbi.1003138.

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

N. Frémaux; H. Sprekeler; W. Gerstner 

Plos Computational Biology. 2013. Vol. 9, num. 4, p. 1-21. DOI : 10.1371/journal.pcbi.1003024.

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

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

Neural Networks. 2013. Vol. 38, p. 52-63. DOI : 10.1016/j.neunet.2012.11.010.

The Silent Period of Evidence Integration in Fast Decision Making

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

PLoS ONE. 2013. Vol. 8, num. 1, p. e46525. DOI : 10.1371/journal.pone.0046525.

Reviews

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. 

Frontiers In Neural Circuits. 2013. Vol. 7. DOI : 10.3389/fncir.2013.00119.

Theses

Models of Reward-Modulated Spike-Timing-Dependent Plasticity

N. Frémaux / W. Gerstner (Dir.)  

Lausanne, EPFL, 2013. 

Stability and amplification in plastic cortical circuits

G. Hennequin / W. Gerstner (Dir.)  

Lausanne, EPFL, 2013. 

Book Chapters

Can We Predict Every Spike?

R. Naud; W. Gerstner 

Spike Timing: Mechanisms and Function; Boca Raton: CRC Press, 2013. p. 65-76.

Posters

Temporal decorrelation by power-law adaptation in pyramidal neurons

C. A. Pozzorini; R. Naud; S. Mensi 

COSYNE, Salt Lake City, USA, February 28 – March 3, 2013.

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

S. Mensi; C. A. Pozzorini; O. Hagens 

COSYNE, Salt Lake City, USA, February 28 – March 3, 2013.

2012

Journal Articles

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 

Neuron. 2012. Vol. 75, num. 5, p. 889-903. DOI : 10.1016/j.neuron.2012.06.036.

Theory and Simulation in Neuroscience

W. Gerstner; H. Sprekeler; G. Deco 

Science. 2012. Vol. 338, p. 60-65. DOI : 10.1126/science.1227356.

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

R. Naud; W. Gerstner 

Plos Computational Biology. 2012. Vol. 8, num. 10, p. e1002711. DOI : 10.1371/journal.pcbi.1002711.

Non-normal amplification in random balanced neuronal networks

G. Hennequin; T. Vogels; W. Gerstner 

Physical Review E. 2012. Vol. 86, num. 1, p. 011909. DOI : 10.1103/PhysRevE.86.011909.

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 

Journal Of Neurophysiology. 2012. Vol. 107, num. 11, p. 3116-3134. DOI : 10.1152/jn.00917.2011.

Perceptual learning, roving and the unsupervised bias

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

Vision Research. 2012. Vol. 61, p. 95-99. DOI : 10.1016/j.visres.2011.11.001.

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. 

Journal Of Neurophysiology. 2012. Vol. 107, num. 6, p. 1756-1775. DOI : 10.1152/jn.00408.2011.

Conference Papers

Firmware for ensuring realtime radio regulations compliance in WSN

M. Tanevski; A. Boegli; P-A. Farine 

2012. 37th Conference on Local Computer Networks Workshops (LCN Workshops), 2012 IEEE, Clearwater, Florida, USA, October 22-25, 2012. p. 917-920. DOI : 10.1109/LCNW.2012.6424082.

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

F. Gerhard; L. Szegletes 

2012. 34th Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS). p. 4611-4614. DOI : 10.1109/EMBC.2012.6346994.

Theses

Analysing Neuronal Network Architectures

C. Tomm / W. Gerstner (Dir.)  

Lausanne, EPFL, 2012. 

2011

Journal Articles

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

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

Frontiers In Computational Neuroscience. 2011. Vol. 5, p. 1-13. DOI : 10.3389/fncom.2011.00004.

Slow Feature Analysis

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

Scholarpedia Journal. 2011. Vol. 6, num. 4, p. 5282. DOI : 10.4249/scholarpedia.5282.

On the Relation of Slow Feature Analysis and Laplacian Eigenmaps

H. Sprekeler 

Neural Computation. 2011. Vol. 23, num. 12, p. 3287-3302. DOI : 10.1162/NECO_a_00214.

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

H. Sprekeler; L. Wiskott 

Neural Computation. 2011. Vol. 23, p. 303-335. DOI : 10.1162/NECO_a_00072.

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 

Proceedings Of The National Academy Of Sciences Of The United States Of America. 2011. Vol. 108, p. 19383-19388. DOI : 10.1073/pnas.1105933108.

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

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

Science. 2011. Vol. 334, num. 6062, p. 1569-1573. DOI : 10.1126/science.1211095.

Improved Similarity Measures for Small Sets of Spike Trains

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

Neural Computation. 2011. Vol. 23, num. 12, p. 3016-3069. DOI : 10.1162/NECO_a_00208.

A history of spike-timing-dependent plasticity

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

Frontiers in Synaptic Neuroscience. 2011. Vol. 3, num. 4, p. 1-24. DOI : 10.3389/fnsyn.2011.00004.

Neural mechanisms and computations underlying stress effects on learning and memory

G. Luksys; C. Sandi 

Current Opinion in Neurobiology. 2011. Vol. 21, num. 3, p. 502-508. DOI : 10.1016/j.conb.2011.03.003.

Applying the Multivariate Time-Rescaling Theorem to Neural Population Models

F. Gerhard; R. Haslinger; G. Pipa 

Neural Computation. 2011. Vol. 23, num. 6, p. 1452-1483. DOI : 10.1162/NECO_a_00126.

Conference Papers

Quantitative Analysis for Authentication of Low-cost RFID Tags

I. Paparrizos; S. Basagiannis; S. Petridou 

2011. 36th Annual IEEE Conference on Local Computer Networks (LCN), Bonn, GERMANY, Oct 04-07, 2011. p. 295-298. DOI : 10.1109/LCN.2011.6115307.

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

S. Mensi; R. Naud; W. Gerstner 

2011. Neural Information Processing Systems (NIPS) 2011, Granada, Spain, December 12-14, 2011.

Variational Learning for Recurrent Spiking Networks

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

2011. Neural Information Processing Systems, Granada, Spain, December 12-17, 2011. p. 136-144.

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

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

2011. IEEE International Symposium on Information Theory (ISIT), Saint-Petersburg, Russia, July, 31 – August, 5, 2011. p. 850-854. DOI : 10.1109/ISIT.2011.6034257.

Theses

The Dynamics of Adapting Neurons

R. Naud / W. Gerstner (Dir.)  

Lausanne, EPFL, 2011. 

Models of Evidence Integration in Rapid Decision Making Processes

N. Marcille / W. Gerstner (Dir.)  

Lausanne, EPFL, 2011. 

Posters

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

F. Gerhard; G. Pipa; W. Gerstner 

USGEB Annual Meeting 2011, Zürich, Switzerland, January 27-28, 2011.

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

F. Gerhard; G. Pipa; W. Gerstner 

Goettingen Meeting of the German Neuroscience Society, Goettingen, Germany, March 23-27, 2011.

Talks

Perceptual Learning, Roving and the Unsupervised Bias

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

34th European Conference on Visual Perception, Toulouse, France, August 28-September 1, 2011.

2010

Journal Articles

PyBrain

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

Journal of Machine Learning Research. 2010. Vol. 11, p. 743-746.

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

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

Frontiers in Computational Neuroscience. 2010. Vol. 4, p. 143. DOI : 10.3389/fncom.2010.00143.

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

W. Gerstner 

Frontiers in Synaptic Neuroscience. 2010. Vol. 2, p. 151. DOI : 10.3389/fnsyn.2010.00151.

Voltage and spike timing interact in STDP – a unified model

C. Clopath; W. Gerstner 

Frontiers in Synaptic Neuroscience. 2010. Vol. 2, p. 25. DOI : 10.3389/fnsyn.2010.00025.

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

N. Frémaux; H. Sprekeler; W. Gerstner 

Journal of Neuroscience. 2010. Vol. 30, num. 40, p. 13326-13337. DOI : 10.1523/JNEUROSCI.6249-09.2010.

Spike-timing dependent plasticity

W. Gerstner; J. Sjostrom 

Scholarpedia. 2010. Vol. 5, num. 2, p. 1362. DOI : 10.4249/scholarpedia.1362.

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

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

Nature Neuroscience. 2010. Vol. 13, num. 3, p. 344-352. DOI : 10.1038/nn.2479.

Conference Papers

A two stage model for perceptual decision making

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

2010.  p. 44-44.

Exponential Natural Evolution Strategies

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

2010. Genetic and Evolutionary Computation Conference, Portland, Oregon, USA, July 7-11, 2010. p. 393-400. DOI : 10.1145/1830483.1830557.

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

F. Gerhard; W. Gerstner 

2010. 24th Annual Conference on Neural Information Processing Systems (NIPS), Vancouver, Canada, December 6-9, 2010.

Complexity and performance in simple neuron models

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

2010. 2nd INCF Congress of Neuroinformatics, Pilsen, Czech Republic, September 06 – 08, 2009.

Posters

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

R. H. Haslinger; F. Gerhard; G. Pipa 

2010 Neuroscience Meeting: Society for Neuroscience, San Diego, CA, USA, November 13-17, 2010.

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

F. Gerhard; R. Haslinger; G. Pipa 

Nineteenth Annual Computational Neuroscience Meeting: CNS*2010, San Antonio, Texas, USA, July 24-30, 2010.

Improved Similarity Measures for Small Sets of Spike Trains

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

Bernstein Conference on Computational Neuroscience, Berlin, Germany, September 27 – October 1, 2010.

Talks

A two stage model for perceptual decision making

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

33rd European Conference on Visual Perception, Lausanne, Switzerland, August 22-26.

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

F. Gerhard; G. Pipa; W. Gerstner 

Bernstein Conference on Computational Neuroscience 2010, Berlin, Germany, 27 Sep – 1 Oct, 2010.

2009

Journal Articles

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

G. Luksys; W. Gerstner; C. Sandi 

Nat Neurosci. 2009. Vol. 12, num. 9, p. 1180-6. DOI : 10.1038/nn.2374.

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 

PLoS Computational Biology. 2009. Vol. 5, num. 12, p. e1000586. DOI : 10.1371/journal.pcbi.1000586.

How Good are Neuron Models?

W. Gerstner; R. Naud 

Science. 2009. Vol. 326, num. 5951, p. 379-380. DOI : 10.1126/science.1181936.

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

D. Bruederle 

Frontiers in Neuroinformatics. 2009. Vol. 3, num. 17, p. 1-10. DOI : 10.3389/neuro.11.017.2009.

NEURON and Python

M. Hines; A. P. Davison; E. Muller 

Frontiers in Neuroinformatics. 2009. Vol. 3, num. 1, p. 1-12. DOI : 10.3389/neuro.11.001.2009.

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

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

Psychological Review. 2009. Vol. 116, num. 3, p. 540-566. DOI : 10.1037/a0016170.

Conference Papers

Linear Compressive Networks

N. Goela; M. Gastpar 

2009. IEEE International Symposium on Information Theory (ISIT 2009), Seoul, SOUTH KOREA, Jun 28-Jul 03, 2009. p. 159-163. DOI : 10.1109/ISIT.2009.5205812.

Code-specific policy gradient rules for spiking neurons

H. Sprekeler; G. Hennequin; W. Gerstner 

2009. Neural Information Processing Systems 22, Vancouver, Canada, December 7-12, 2009. p. 1741-1749.

Spike-timing prediction in a neuron model with active dendrites

R. Naud; B. Bathellier; W. Gerstner 

2009. Eighteenth Annual Computational Neuroscience Meeting: CNS*2009, Berlin, Germany, 18–23 July 2009.

Theses

Synaptic plasticity across different time scales and its functional implications

C. Clopath / W. Gerstner (Dir.)  

Lausanne, EPFL, 2009. 

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

G. Luksys; C. Sandi / W. Gerstner (Dir.)  

Lausanne, EPFL, 2009. 

Interpretation of neuronal response properties with simplified neuron models

L. Badel / W. Gerstner (Dir.)  

Lausanne, EPFL, 2009. 

Student Projects

Functional shape of the spike-triggered adaptation

C. Pozzorini 

2009.

2008

Journal Articles

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

B. Bathellier; A. Carleton 

Neural Computation. 2008. Vol. 20, num. 12, p. 2973-3002. DOI : 10.1162/neco.2008.11-07-636.

Special issue on quantitative neuron modeling

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

Biological Cybernetics. 2008. Vol. 99, p. 237-239. DOI : 10.1007/s00422-008-0274-5.

Modeling spatial and temporal aspects of visual backward masking

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

Psychological review. 2008. Vol. 115, num. 1, p. 83-100. DOI : 10.1037/0033-295X.115.1.83.

PyNEST: a convenient interface to the NEST simulator

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

Frontiers in Neuroinformatics. 2008. Vol. 2, num. 12, p. 1-12. DOI : 10.3389/neuro.11.012.2008.

PyNN: a common interface for neuronal network simulators

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

Frontiers in Neuroinformatics. 2008. Vol. 2, num. 11, p. 1-10. DOI : 10.3389/neuro.11.011.2008.

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. 

Biological Cybernetics. 2008. Vol. 99, num. 4-5, p. 361-370. DOI : 10.1007/s00422-008-0259-4.

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

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

PLoS Comput Biol. 2008. Vol. 4, num. 12, p. e1000248. DOI : 10.1371/journal.pcbi.1000248.

The quantitative single-neuron modeling competition

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

Biological Cybernetics. 2008. Vol. 99, num. 4-5, p. 417-426. DOI : 10.1007/s00422-008-0261-x.

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

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

Biological Cybernetics. 2008. Vol. 99, num. 4-5, p. 335-347. DOI : 10.1007/s00422-008-0264-7.

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

F. Creutzig; H. Sprekeler 

Neural Computation. 2008. Vol. 20, num. 4, p. 1026-1041. DOI : 10.1162/neco.2008.01-07-455.

Phenomenological models of synaptic plasticity based on spike timing

A. Morrison; M. Diesmann; W. Gerstner 

Biological Cybernetics. 2008. Vol. 98, num. 6, p. 459-478. DOI : 10.1007/s00422-008-0233-1.

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

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

Physical Review E. 2008. Vol. 78, num. 1, p. 011914 1-12. DOI : 10.1103/PhysRevE.78.011914.

A benchmark test for a quantitative assessment of simple neuron models

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

Journal of Neuroscience Methods. 2008. Vol. 169, num. 2, p. 417-424. DOI : 10.1016/j.jneumeth.2007.11.006.

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. 

Journal of Neurophysiology. 2008. Vol. 99, p. 656-666. DOI : 10.1152/jn.01107.2007.

Conference Papers

Modelling feature-integration in human vision with drift diffusion models

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

2008. 6th FENS: Forum of European Neuroscience, Genève, Switzerland, July 12-16, 2008. p. 220.9.

Quantitative single-neuron modeling: competition 2008

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

2008. Neuroinformatics 2008, Stockholm, Sweden, September 07 – 09, 2008.

An online Hebbian learning rule that performs Independent Component Analysis

C. Clopath; A. Longtin; W. Gerstner 

2008. NIPS, Vancouver, B.C., Canada, December 3-6, 2007. p. 321-328.

2007

Journal Articles

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

M. Franzius; H. Sprekeler; L. Wiskott 

PLoS Computational Biology. 2007. Vol. 3, num. 8, p. 1605-1622. DOI : 10.1371/journal.pcbi.0030166.

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

H. Sprekeler; C. Michaelis; L. Wiskott 

PLoS Computational Biology. 2007. Vol. 3, num. 6, p. 1136-1148. DOI : 10.1371/journal.pcbi.0030112.

Consciousness & the Small Network Argument

M. H. Herzog; M. Esfeld; W. Gerstner 

Neural Networks. 2007. Vol. 20, num. 9, p. 1054-1056. DOI : 10.1016/j.neunet.2007.09.001.

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 

Neurocomputing. 2007. Vol. 70, num. 10-12, p. 1668-1673. DOI : 10.1016/j.neucom.2006.10.047.

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

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

Neural Computation. 2007. Vol. 19, num. 3, p. 639-671. DOI : 10.1162/neco.2007.19.3.639.

Theses

Spatial navigation in geometric mazes

D. Sheynikhovich / W. Gerstner (Dir.)  

Lausanne, EPFL, 2007. 

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

B. Bathellier / W. Gerstner; A. Carleton (Dir.)  

Lausanne, EPFL, 2007. 

2006

Journal Articles

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

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

Neurocomputing. 2006. Vol. 69, num. 10-12, p. 1062-1065. DOI : 10.1016/j.neucom.2005.12.046.

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

Y. Aviel; W. Gerstner 

Physical Review E. 2006. Vol. 73, num. 5, p. 051908 1-10. DOI : 10.1103/PhysRevE.73.051908.

Predicting spike timing of neocortical pyramidal neurons by simple threshold models

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

Journal of Computational Neuroscience. 2006. Vol. 21, num. 1, p. 35-49. DOI : 10.1007/s10827-006-7074-5.

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

J. P. Pfister; W. Gerstner 

Journal of Neuroscience. 2006. Vol. 26, num. 38, p. 9673-9682. DOI : 10.1523/JNEUROSCI.1425-06.2006.

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

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

Neural Computation. 2006. Vol. 18, num. 6, p. 1318-1348. DOI : 10.1162/neco.2006.18.6.1318.

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

M. J. E. Richardson; W. Gerstner 

Chaos. 2006. Vol. 16, num. 2, p. 26106. DOI : 10.1063/1.2203409.

Conference Papers

Integrate-and-Fire models with adaptation are good enough

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

2006.  p. 595-602.

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

J. P. Pfister; W. Gerstner 

2006.  p. 1083–1090.

Theses

Theory of non-linear spike-time-dependent plasticity

J-P. Pfister / W. Gerstner (Dir.)  

Lausanne, EPFL, 2006. 

Conference Proceedings

Dynamical principles for neuroscience and intelligent biomimetic devices

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

2006. EPFL LATSIS Symposium 2006.

2005

Journal Articles

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

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

NEUROCOMPUTING. 2005. num. 65-66, p. 685-690. DOI : 10.1016/j.neucom.2004.10.098.

Robust self-localisation and navigation based on hippocampal place cells

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

NEURAL NETWORKS. 2005. Vol. 18, num. 9, p. 1125-1140. DOI : 10.1016/j.neunet.2005.08.012.

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

R. Brette; W. Gerstner 

Journal of Neurophysiology. 2005. Vol. 94, p. 3637-3642. DOI : 10.1152/jn.00686.2005.

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

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

Connection Science. 2005. Vol. 17, num. 1-2, p. 167-183. DOI : 10.1080/09540090500138093.

A computational model of parallel navigation systems in rodents

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

Neuroinformatics. 2005. Vol. 3, num. 3, p. 223-241. DOI : 10.1385/NI:3:3:223.

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

J. Mayor; W. Gerstner 

Physical Review E. 2005. Vol. 72, num. 5, p. 051906. DOI : 10.1103/PhysRevE.72.051906.

Noise-enhanced computation in a model of a cortical column

J. Mayor; W. Gerstner 

Neuroreport. 2005. Vol. 16, num. 11, p. 1237-1240. DOI : 10.1097/00001756-200508010-00021.

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

M. J. E. Richardson; W. Gerstner 

Neural Computation. 2005. Vol. 17, num. 4, p. 923-947. DOI : 10.1162/0899766053429444.

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 

Journal of Computational Neuroscience. 2005. Vol. 18, p. 323-331. DOI : 10.1007/s10827-005-0434-8.

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

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

Proc. National Academy Sciences (USA). 2005. Vol. 102, num. 14, p. 5239-5244. DOI : 10.1073/pnas.0500495102.

Conference Papers

Modelling Path Integrator Recalibration Using Hippocampal Place cells

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

2005. ICANN 2005, International Conference on Artificial Neural Networks, Warsaw, Poland, September 11-15, 2005. p. 51-56. DOI : 10.1007/11550822_9.

Spatial Representation and Navigation in a Bio-inspired Robot

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

2005.  p. 245-264. DOI : 10.1007/11521082_15.

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

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

2005.  p. 1409-1416.

2004

Journal Articles

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

L. Badel; A. Tonnelier 

Physical Review E. 2004. Vol. 70, num. 1, p. 011906 1-7. DOI : 10.1103/PhysRevE.70.011906.

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

A. Arleo; F. Smeraldi; W. Gerstner 

IEEE Transactions on Neural Networks. 2004. Vol. 15, num. 3, p. 639-652. DOI : 10.1109/TNN.2004.826221.

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

R. Jolivet; W. Gerstner 

Journal of Physiology-Paris. 2004. Vol. 98, num. 4-6, p. 442-451. DOI : 10.1016/j.jphysparis.2005.09.010.

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 

Journal of Neurophysiology. 2004. Vol. 92, p. 959-976. DOI : 10.1152/jn.00190.2004.

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

J. Mayor; W. Gerstner 

Journal of Physiology (Paris). 2004. Vol. 98, num. 4-6, p. 417-428. DOI : 10.1016/j.jphysparis.2005.09.009.

Coding and Learning of behavioral sequences

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

Trends in Neurosciences. 2004. Vol. 27, num. 1, p. 11-14. DOI : 10.1016/j.tins.2003.10.014.

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

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

IEEE Transactions on Biomedical Engineering, Special Issue on Brain-Machine Interfaces. 2004. Vol. 51, num. 6, p. 1026-1033. DOI : 10.1109/TBME.2004.827086.

Brain-Actuated Interaction

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

Artificial Intelligence. 2004. Vol. 159, num. 1-2, p. 241-259. DOI : 10.1016/j.artint.2004.05.008.

2003

Journal Articles

Bifurcation properties of the average activity of interconnected neural populations

A. Tonnelier 

Biological Cybernetics. 2003. Vol. 89, num. 3, p. 179-189. DOI : 10.1007/s00422-003-0421-y.

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

A. Tonnelier; W. Gerstner 

Physical Review E. 2003. Vol. 67, num. 2, p. 21908. DOI : 10.1103/PhysRevE.67.021908.

Conference Papers

Optimal Hebbian Learning: a Probabilistic Point of View

J-P. Pfister; D. Barber; W. Gerstner 

2003. ICANN/ICONIP, Istanbul, Turkey, June 26-29, 2003. p. 92–98. DOI : 10.1007/3-540-44989-2_12.

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

J. Mayor; W. Gerstner 

2003. 13. ICANN / 10. ICONIP 2003, Istanbul, Turkey, June 26-29, 2003. p. 839-845. DOI : 10.1007/3-540-44989-2_100.

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

R. Jolivet; T. J. Lewis; W. Gerstner 

2003. Joint International Conference ICANN/ICONIP 2003, Istanbul, Turkey, June 26–29, 2003. p. 846-853. DOI : 10.1007/3-540-44989-2_101.

Non-Invasive Brain-Actuated Control of a Mobile Robot

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

2003. IJCAI-03, Acapulco, Mexico, p. 1121-1126.

Talks

Reinforcement Learning in Continuous State and Action Space

T. Strösslin; W. Gerstner 

Artificial Neural Networks – ICANN 2003.

Homeostasis and Learning through Spike-Timing Dependent Plasticity

L. Abbott; W. Gerstner 

Summer School in Neurophzsics, Les Houches, July 28 – August 29, 2003.

2002

Journal Articles

Mathematical Formulations of Hebbian Learning

W. Gerstner; W. K. Kistler 

Biological Cybernetics. 2002. Vol. 87, num. 5-6, p. 404-415. DOI : 10.1007/s00422-002-0353-y.

Stable Propagation of Activity Pulses in Populations of Spiking Neurons

W. M. Kistler; W. Gerstner 

Neural Computation. 2002. Vol. 14, num. 5, p. 987-997. DOI : 10.1162/089976602753633358.

Books

Spiking Neuron Models

W. Gerstner; W. K. Kistler 

Cambridge University Press, 2002.

2001

Journal Articles

Spatial orientation in navigating agents: Modeling head-direction cells

A. Arleo; W. Gerstner 

Neurocomputing. 2001. Vol. 38-40, num. 1-4, p. 1059-1065. DOI : 10.1016/S0925-2312(01)00572-0.

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

W. Gerstner 

Neural Networks. 2001. Vol. 14, num. 6-7, p. 599-610. DOI : 10.1016/S0893-6080(01)00053-3.

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

A. Herrmann; W. Gerstner 

Journal of Computational Neuroscience. 2001. Vol. 11, num. 2, p. 135-151. DOI : 10.1023/A:1012841516004.

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 

Journal of Computational Neuroscience. 2001. Vol. 12, num. 2, p. 83-95. DOI : 10.1023/A:1015739523224.

Intrinsic Stabilization of Output Rates by Spike-Based Hebbian Learning

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

Neural Computation. 2001. Vol. 13, num. 12, p. 2709-2741. DOI : 10.1162/089976601317098501.

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

M. Spiridon; W. Gerstner 

Network: Computation in Neural Systems. 2001. Vol. 12, num. 4, p. 409-421257-272. DOI : 10.1088/0954-898X/12/4/301.

Conference Papers

Hippocampal spatial model for state space representation in robotic reinforcement learning

A. Arleo; W. Gerstner 

2001.  p. 1-3.

Book Chapters

A framework for spiking neuron models – the spike response model

W. Gerstner 

Handbook of Biological Physics; Elsevier, 2001. p. 469-516.

2000

Journal Articles

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

A. Arleo; W. Gerstner 

Biological Cybernetics. 2000. Vol. 83, num. 3, p. 287-299. DOI : 10.1007/s004220000171.

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

W. Gerstner 

Neural Computation. 2000. Vol. 12, num. 1, p. 43-89. DOI : 10.1162/089976600300015899.

Effect of noise on neuron transient response

A. Herrmann; W. Gerstner 

Neurocomputing. 2000. Vol. 32-33, p. 147-154. DOI : 10.1016/S0925-2312(00)00156-9.

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

M. Spiridon; C. C. Chow; W. Gerstner 

Neurocomputing. 2000. Vol. 32-33, num. 1-4, p. 529-535. DOI : 10.1016/S0925-2312(00)00209-5.

1999

Journal Articles

Hebbian learning and spiking neurons

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

Physical Review E. 1999. Vol. 59, num. 4, p. 4498–4514. DOI : 10.1103/PhysRevE.59.4498.

Noise-shaping in populations of coupled model neurons

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

Proceedings of the National Academy of Sciences of the United States of America. 1999. Vol. 96, num. 18, p. 10450-10455. DOI : 10.1073/pnas.96.18.10450.

Noise spectrum and signal transmission trough a population of spiking neurons

M. Spiridon; W. Gerstner 

Network: Computation in Neural Systems. 1999. Vol. 10, num. 3, p. 257-272. DOI : 10.1088/0954-898X/10/3/304.

Conference Papers

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

A. Arleo; W. Gerstner 

1999.  p. 132-137. DOI : 10.1049/cp:19991097.

Rapid signal transmission by a population of spiking neurons

W. Gerstner 

1999.  p. 7-12. DOI : 10.1049/cp:19991076.

Understanding the PSTH response to synaptic input

A. Herrmann; W. Gerstner 

1999.  p. 1012-1017. DOI : 10.1049/cp:19991245.

Spike-Based Compared to Rate-Based Hebbian Learning

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

1999.  p. 125-131.

Book Chapters

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

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

Psychophysics, Physiology and Models of Hearing; Singapore: World Scientific, 1999. p. 185-192.

1998

Journal Articles

How the threshold of a neuron determines its capacity for coincidence detection

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

BioSystems. 1998. Vol. 48, num. 1-3, p. 105-112. DOI : 10.1016/S0303-2647(98)00055-0.

Extracting oscillations: Neuronal coincidence detection with noisy periodic spike input

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

Neural Comput.. 1998. Vol. 10, num. 8, p. 1987-2017. DOI : 10.1162/089976698300016945.

Analysis of a correlation-based model for the development of orientation-selective receptive fields in the visual cortex

S. Wimbauer; W. Gerstner; J. L. van Hemmen 

Network. 1998. Vol. 9, p. 449-466. DOI : 10.1088/0954-898X_9_4_004.

Conference Papers

Frequency spectrum of coupled stochastic neurons with refractoriness

M. Spiridon; C. Chow; W. Gerstner 

1998. 8th International Conference on Artificial Neural Networks, Skövde, Sweden, 2–4 September 1998. p. 337-342. DOI : 10.1007/978-1-4471-1599-1_49.

Book Chapters

Spiking Neurons

W. Gerstner 

Pulsed Neural Networks; MIT-Press, 1998. p. 3-53.

Populations of spiking neurons

W. Gerstner 

Pulsed Neural Networks; MIT-Press, 1998. p. 261-295.

Hebbian learning of Pulse timing in the Barn Owl auditory system

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

Pulsed Neural Networks; MIT-Press, 1998. p. 353-377.

1997

Journal Articles

Learning navigational maps through potentiation and modulation of hippocampal place cells

W. Gerstner; L. F. Abbott 

Journal of Comput. Neurosci.. 1997. Vol. 4, p. 79-94. DOI : 10.1023/A:1008820728122.

Neural codes: firing rates and beyond

W. Gerstner; A. K. Kreiter; H. Markram; A. V. M. Herz 

Proceedings of the National Academy of Sciences of the United States of America. 1997. Vol. 94, num. 24, p. 12740-12741. DOI : 10.1073/pnas.94.24.12740.

Reduction of Hodgkin-Huxley equations to a single-variable threshold model

W. M. Kistler; W. Gerstner; J. L. van Hemmen 

Neural Comput.. 1997. Vol. 9, num. 5, p. 1015-1045. DOI : 10.1162/neco.1997.9.5.1015.

Conference Papers

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

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

1997.  p. 665-669.

Conference Proceedings

Artificial Neural Networks – ICANN ’97

W. Gerstner; A. Germond; M. Hasler; J-D. Nicoud 

1997. 7th International Conference.

1996

Journal Articles

Vertical Signal Flow and Oscillations in a 3-Layer Model of the Cortex

U. Fuentes; R. Ritz; W. Gerstner; J. L. van Hemmen 

Journal of Computational Neuroscience. 1996. Vol. 3, num. 2, p. 125-136. DOI : 10.1007/BF00160808.

Rapid phase locking in systems of pulse-coupled oscillators with delays

W. Gerstner 

Physical Review Letters. 1996. Vol. 76, num. 10, p. 1755-1758. DOI : 10.1103/PhysRevLett.76.1755.

A neuronal learning rule for sub-millisecond temporal coding

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

Nature. 1996. Vol. 383, num. 6595, p. 76-78. DOI : 10.1038/383076a0.

What matters in neuronal locking?

W. Gerstner; J. L. van Hemmen; J. D. Cowan 

Neural Comput.. 1996. Vol. 8, num. 8, p. 1653-1676. DOI : 10.1162/neco.1996.8.8.1653.

Conference Papers

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

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

1996. 8th NIPS Conference, 1995. p. 124-130.

1995

Journal Articles

Intracellular Ca2+ stores can account for the time course of LTP induction: a model of Ca2+ dynamics in dendritic spines

A. Schiegg; W. Gerstner; R. Ritz; J. L. van Hemmen 

Journal of Neurophysiology. 1995. Vol. 74, num. 3, p. 1046-1055. DOI : 10.1152/jn.1995.74.3.1046.