Publications

2024

Journal Articles

Fast learning without synaptic plasticity in spiking neural networks

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

Scientific Reports. 2024-04-12. Vol. 14, num. 1, p. 8557. DOI : 10.1038/s41598-024-55769-0.

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

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

PLOS Computational Biology. 2024. Vol. 20, num. 2, p. e1011844. DOI : 10.1371/journal.pcbi.1011844.

Fast adaptation to rule switching using neuronal surprise

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

PLOS Computational Biology. 2024. Vol. 20, num. 2, p. e1011839. DOI : 10.1371/journal.pcbi.1011839.

Theses

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

A. Modirshanechi / W. Gerstner (Dir.)  

Lausanne, EPFL, 2024. 

2023

Journal Articles

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

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

Cell Reports. 2023-12-26. Vol. 43, num. 1, p. 113618. DOI : 10.1016/j.celrep.2023.113618.

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

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

Plos Computational Biology. 2023-12-01. Vol. 19, num. 12, p. e1011727. DOI : 10.1371/journal.pcbi.1011727.

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

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

Nature Methods. 2023-12-05. Vol. 21, num. 1. DOI : 10.1038/s41592-023-02096-3.

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

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

Proceedings Of The National Academy Of Sciences Of The United States Of America. 2023-08-08. Vol. 120, num. 32, p. e2300558120. DOI : 10.1073/pnas.2300558120.

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

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

Neural Networks. 2023-11-14. Vol. 169, p. 622-622. DOI : 10.1016/j.neunet.2023.10.057.

An exact mapping from ReLU networks to spiking neural networks

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

Neural Networks. 2023. Vol. 168, p. 74-88. DOI : 10.1016/j.neunet.2023.09.011.

Surprise and novelty in the brain

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

Current Opinion In Neurobiology. 2023-10-01. Vol. 82, p. 102758. DOI : 10.1016/j.conb.2023.102758.

On a Finite-Size Neuronal Population Equation

V. Schmutz; E. Locherbach; T. Schwalger 

Siam Journal On Applied Dynamical Systems. 2023-01-01. Vol. 22, num. 2, p. 996-1029. DOI : 10.1137/21M1445041.

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

J. Brea; N. S. Clayton; W. Gerstner 

Nature Communications. 2023-05-23. Vol. 14, num. 1. DOI : 10.1038/s41467-023-38570-x.

Reviews

Curiosity-driven exploration: foundations in neuroscience and computational modeling

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

Trends In Neurosciences. 2023-11-21. Vol. 46, num. 12, p. 1054-1066. DOI : 10.1016/j.tins.2023.10.002.

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. 2023. Vol. 54, num. 1, p. 192-221. DOI : 10.1111/bjet.13289.

Theses

Supervised learning and inference of spiking neural networks with temporal coding

A. Stanojevic / W. Gerstner; S. A. Wozniak (Dir.)  

Lausanne, EPFL, 2023. 

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

M. L. L. R. Barry / W. Gerstner (Dir.)  

Lausanne, EPFL, 2023. 

Patents

Automated music composition and generation system and method

F. Colombo 

US2023326436.

2023.

2022

Journal Articles

Modulation of working memory duration by synaptic and astrocytic mechanisms

S. Becker; A. Nold; T. Tchumatchenko 

Plos Computational Biology. 2022-10-01. Vol. 18, num. 10, p. e1010543. DOI : 10.1371/journal.pcbi.1010543.

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

B. Pietras; V. Schmutz; T. Schwalger 

Plos Computational Biology. 2022-12-01. Vol. 18, num. 12, p. e1010809. DOI : 10.1371/journal.pcbi.1010809.

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.

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.

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.

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.

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.

Conference Papers

Approximating Relu Networks By Single-Spike Computation

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

2022-01-01. IEEE International Conference on Image Processing (ICIP), Bordeaux, FRANCE, Oct 16-19, 2022. p. 1901-1905. DOI : 10.1109/ICIP46576.2022.9897692.

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

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.

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.

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.

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.

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.

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

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.

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.

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.

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.

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. 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.

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.