Publications

Searchable database of lab publications

LCN Publications

2019

Journal Articles

Characterization of phosphorus species distribution in waste activated sludge after anaerobic digestion and chemical precipitation with Fe3+ and Mg2+

L. Li; H. Pang; J. He; J. Zhang 

Chemical Engineering Journal. 2019-10-01. Vol. 373, p. 1279-1285. DOI : 10.1016/j.cej.2019.05.146.

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.

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

B. Illing; W. Gerstner; J. Brea 

2019-03-01. 

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. 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. 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. 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 

Sci Rep. 2018. Vol. 8, num. 6297. DOI : 10.1038/s41598-018-24339-6.

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. 

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, num. 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. 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. 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, num. 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. 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, num. 90. DOI : 10.3389/fncom.2014.00090.

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. 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. 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. 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. 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. 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.