Publications

LCN Publications

2018

Journal Articles

M. Martinolli; W. Gerstner; A. Gilra : Multi-Timescale Memory Dynamics Extend Task Repertoire in a Reinforcement Learning Network With Attention-Gated Memory; Front. Comput. Neurosci.. 2018. DOI : 10.3389/fncom.2018.00050.
M. Deger; A. Seeholzer; W. Gerstner : Multicontact Co-operativity in Spike-Timing-Dependent Structural Plasticity Stabilizes Networks; Cereb. Cortex. 2018. DOI : 10.1093/cercor/bhx339.
H. Setareh; M. Deger; W. Gerstner : Excitable neuronal assemblies with adaptation as a building block of brain circuits for velocity-controlled signal propagation; PLoS Comput. Biol.. 2018. DOI : 10.1371/journal.pcbi.1006216.
W. Gerstner; M. Lehmann; V. Liakoni; D. Corneil; J. Brea : Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules; Front. Neural Circuits. 2018. DOI : 10.3389/fncir.2018.00053.
M. Faraji; K. Preuschoff; W. Gerstner : Balancing New against Old Information: The Role of Puzzlement Surprise in Learning; Neural Comput.. 2018. DOI : 10.1162/NECO_a_01025.
S. Saberi-Moghadam; A. Simi; H. Setareh; C. Mikhail; M. Tafti : In vitro Cortical Network Firing is Homeostatically Regulated: A Model for Sleep Regulation; Sci Rep. 2018. DOI : 10.1038/s41598-018-24339-6.

Theses

D. S. Corneil / W. Gerstner (Dir.) : Model-based reinforcement learning and navigation in animals and machines. Lausanne, EPFL, 2018. DOI : 10.5075/epfl-thesis-8950.

2017

Journal Articles

A. Gilra; W. Gerstner : Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network; Elife. 2017. DOI : 10.7554/eLife.28295.
H. Setareh; M. Deger; C. C. Petersen; W. Gerstner : Cortical Dynamics in Presence of Assemblies of Densely Connected Weight-Hub Neurons; Frontiers in Computational Neuroscience. 2017. DOI : 10.3389/fncom.2017.00052.
W. F. Podlaski; A. Seeholzer; L. N. Groschner; G. Miesenbock; R. Ranjan et al. : Mapping the function of neuronal ion channels in model and experiment; Elife. 2017. DOI : 10.7554/eLife.22152.
T. Schwalger; M. Deger; W. Gerstner : Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size; PLoS Computational Biology. 2017. DOI : 10.1371/journal.pcbi.1005507.
F. Gerhard; M. Deger; W. Truccolo : On the stability and dynamics of stochastic spiking neuron models: Nonlinear Hawkes process and point process GLMs; Plos Computational Biology. 2017. DOI : 10.1371/journal.pcbi.1005390.
S. P. Muscinelli; W. Gerstner; J. M. Brea : Exponentially long orbits in Hopfield neural networks; Neural Computation. 2017. DOI : 10.1162/NECO_a_00919.

Reviews

F. Zenke; W. Gerstner; S. Ganguli : The temporal paradox of Hebbian learning and homeostatic plasticity; Current Opinion In Neurobiology. 2017. DOI : 10.1016/j.conb.2017.03.015.
R. Duarte; A. Seeholzer; K. Zilles; A. Morrison : Synaptic patterning and the timescales of cortical dynamics; Current Opinion In Neurobiology. 2017. DOI : 10.1016/j.conb.2017.02.007.
F. Zenke; W. Gerstner : Hebbian plasticity requires compensatory processes on multiple timescales; Philosophical Transactions Of The Royal Society B-Biological Sciences. 2017. DOI : 10.1098/rstb.2016.0259.
T. Keck; T. Toyoizumi; L. Chen; B. Doiron; D. E. Feldman et al. : Integrating Hebbian and homeostatic plasticity: the current state of the field and future research directions; Philosophical Transactions Of The Royal Society B-Biological Sciences. 2017. DOI : 10.1098/rstb.2016.0158.

Theses

H. Setareh / W. Gerstner (Dir.) : Neural assemblies as core elements for modeling neural networks in the brain. Lausanne, EPFL, 2017. DOI : 10.5075/epfl-thesis-8228.
A. K. Seeholzer / W. Gerstner (Dir.) : Continuous attractor working memory and provenance of channel models. Lausanne, EPFL, 2017. DOI : 10.5075/epfl-thesis-7845.

Working Papers

M. Lehmann; H. Xu; V. Liakoni; M. Herzog; W. Gerstner et al. : Evidence for eligibility traces in human learning. 2017.

2016

Journal Articles

C. S. N. Brito; W. Gerstner : Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation; PLoS Computational Biology. 2016. DOI : 10.1371/journal.pcbi.1005070.
J. Brea; A. T. Gaal; R. Urbanczik; W. Senn : Prospective Coding by Spiking Neurons; Plos Computational Biology. 2016. DOI : 10.1371/journal.pcbi.1005003.
Y. Burnier; C. Gastaldi : Contribution of next-to-leading order and Landau-Pomeranchuk-Migdal corrections to thermal dilepton emission in heavy-ion collisions; Physical Review C. 2016. DOI : 10.1103/PhysRevC.93.044902.
M. Faraji; K. Preuschoff; W. Gerstner : Balancing New Against Old Information: The Role of Surprise; arXiv. 2016.
J. M. Brea; W. Gerstner : Does computational neuroscience need new synaptic learning paradigms?; Current Opinion in Behavioral Sciences. 2016. DOI : 10.1016/j.cobeha.2016.05.012.
D. B. Kastner; T. Schwalger; L. Ziegler; W. Gerstner : A Model of Synaptic Reconsolidation; Frontiers in Neuroscience. 2016. DOI : 10.3389/fnins.2016.00206.
S. Mensi; O. Hagens; W. Gerstner; C. Pozzorini : Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons; Plos Computational Biology. 2016. DOI : 10.1371/journal.pcbi.1004761.
N. Fremaux; W. Gerstner : Neuromodulated-Spike-Timing-Dependent Pasticity, and Theory of Three-Factor Learning Rules; Frontiers in Neural Circuits. 2016. DOI : 10.3389/fncir.2015.00085.
D. Shuman; M. Faraji; P. Vandergheynst : A Multiscale Pyramid Transform for Graph Signals; IEEE Transactions on Signal Processing. 2016. DOI : 10.1109/TSP.2015.2512529.

Conference Papers

F. Colombo; S. P. Muscinelli; A. Seeholzer; J. Brea; W. Gerstner : Algorithmic Composition of Melodies with Deep Recurrent Neural Networks. 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

M. Faraji / W. Gerstner (Dir.) : Learning with Surprise. Lausanne, EPFL, 2016. DOI : 10.5075/epfl-thesis-7418.
C. Stein Naves de Brito / W. Gerstner (Dir.) : Theory of representation learning in cortical neural networks. Lausanne, EPFL, 2016. DOI : 10.5075/epfl-thesis-6948.

Posters

F. Colombo : Learning and generation of slow sequences: an application to music composition ; Lemanic Neuroscience Annual Meeting 2016, Les Diablerets, Switzerland, September 2-3, 2016.
F. Colombo : Algorithmic composition of melodies with deep recurrent neural networks ; Machine Learning Machine Learning Summer School 2016, Cadiz, Spain, May 11-21, 2016.
M. Faraji; K. Preuschoff; W. Gerstner : Surprise-modulated belief update: how to learn within changing environments? ; Computational Neuroscience Meeting (CNS), Jeju Island, South Korea, July 2-7, 2016.
M. Faraji; K. Preuschoff; W. Gerstner : A novel information theoretic measure of surprise ; International Conference on Mathematical Neuroscience (ICMNS), Antibes - Juan Les Pins, France, May 30 - June 1, 2016.
M. Faraji; K. Preuschoff; W. Gerstner : Surprise-based learning: a novel measure of surprise with applications for learning within changing environments ; Computational and Systems Neuroscience (COSYNE), Salt Lake City, Utah, USA, February 25 - March 1, 2016.

2015

Journal Articles

T. Schwalger; B. Lindner : Analytical approach to an integrate-and-fire model with spike-triggered adaptation; Physical Review E. 2015. DOI : 10.1103/PhysRevE.92.062703.
S. Wieland; D. Bernardi; T. Schwalger; B. Lindner : Slow fluctuations in recurrent networks of spiking neurons; Physical Review E. 2015. DOI : 10.1103/PhysRevE.92.040901.
C. A. Pozzorini; S. Mensi; O. Hagens; R. Naud; C. Koch et al. : Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models; Plos Computational Biology. 2015. DOI : 10.1371/journal.pcbi.1004275.
Y. V. Zaytsev; A. Morrison; M. Deger : Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity; Journal of Computational Neuroscience. 2015. DOI : 10.1007/s10827-015-0565-5.
L. Shiau; T. Schwalger; B. Lindner : Interspike interval correlation in a stochastic exponential integrate-and-fire model with subthreshold and spike-triggered adaptation; Journal Of Computational Neuroscience. 2015. DOI : 10.1007/s10827-015-0558-4.
T. Schwalger; F. Droste; B. Lindner : Statistical structure of neural spiking under non-Poissonian or other non-white stimulation; Journal of Computational Neuroscience. 2015. DOI : 10.1007/s10827-015-0560-x.
F. Zenke; E. J. Agnes; W. Gerstner : Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks; Nature Communications. 2015. DOI : 10.1038/ncomms7922.
L. Ziegler; F. Zenke; D. B. Kastner; W. Gerstner : Synaptic consolidation: from synapses to behavioral modeling; The Journal of neuroscience : the official journal of the Society for Neuroscience. 2015. DOI : 10.1523/JNEUROSCI.3989-14.2015.

Conference Papers

D. S. Corneil; W. Gerstner : Attractor Network Dynamics Enable Preplay and Rapid Path Planning in Maze–like Environments. 2015. Neural Information Processing Systems (NIPS 2015), Montreal, Canada, December 07- 12, 2015. p. 1675-1683.
T. Schwalger; M. Deger; W. Gerstner : Bridging spiking neuron models and mesoscopic population models - a general theory for neural population dynamics. 2015. p. P79. DOI : 10.1186/1471-2202-16-S1-P79.
M. Faraji; K. Preuschoff; W. Gerstner : A biologically plausible 3-factor learning rule for expectation maximization in reinforcement learning and decision making. 2015. The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM), Edmonton, Alberta, Canada, June 7-10, 2015.

Posters

M. Faraji; K. Preuschoff; W. Gerstner : Surprise minimization as a learning strategy in neural networks ; Computational Neuroscience Meeting (CNS), Prague, Czech Republic, July 18-23, 2015.
M. P. Lehmann; A. Aivazidis; M. Faraji; K. Preuschoff : Bayesian filtering, parallel hypotheses and uncertainty: a new, combined model for human learning ; Computational and Systems Neuroscience (COSYNE), Salt Lake City, Utah, USA, March 5-10, 2015.
D. S. Corneil; W. Gerstner : Rapid path planning and preplay in maze{like environments using attractor networks ; COSYNE 2015, Salt Lake City, March 5 to 10, 2015.
F. Zenke; E. Agnes; W. Gerstner : Hebbian and non-Hebbian plasticity orchestrated to form and retrieve memories in spiking networks ; COSYNE 2015, Salt Lake City, March 5 to 10, 2015.
M. Faraji; K. Preuschoff; W. Gerstner : Learning associations with a neurally-computed global novelty signal ; Computational and Systems Neuroscience (COSYNE), Salt Lake City, Utah, USA, March 5-10, 2015.
H. Setareh; M. Deger; W. Gerstner : Synaptic efficacy tunes speed of activity propagation through chains of bistable neural assemblies ; COSYNE 2015, Salt Lake City, March 5 to 10, 2015.

Student Projects

F. F. Colombo : Music Learning with Long Short Term Memory Networks ; 2015.

2014

Journal Articles

C. Tomm; M. Avermann; C. Petersen; W. Gerstner; T. P. Vogels : Connection-type-specific biases make uniform random network models consistent with cortical recordings; Journal Of Neurophysiology. 2014. DOI : 10.1152/jn.00629.2013.
M. Deger; T. Schwalger; R. Naud; W. Gerstner : Fluctuations and information filtering in coupled populations of spiking neurons with adaptation; Physical Review E. 2014. DOI : 10.1103/PhysRevE.90.062704.
A. Seeholzer; E. Frey; B. Obermayer : Periodic versus Intermittent Adaptive Cycles in Quasispecies Coevolution; Physical Review Letters. 2014. DOI : 10.1103/PhysRevLett.113.128101.
F. Zenke; W. Gerstner : Limits to high-speed simulations of spiking neural networks using general-purpose computers; Frontiers in neuroinformatics. 2014. DOI : 10.3389/fninf.2014.00076.
R. Naud; B. Bathellier; W. Gerstner : Spike-timing prediction in cortical neurons with active dendrites; Frontiers in Computational Neuroscience. 2014. DOI : 10.3389/fncom.2014.00090.
G. Hennequin; T. Vogels; W. Gerstner : Optimal Control of Transient Dynamics in Balanced Networks Supports Generation of Complex Movements; Neuron. 2014. DOI : 10.1016/j.neuron.2014.04.045.
D. J. Rezende; W. Gerstner : Stochastic variational learning in recurrent spiking networks; Frontiers In Computational Neuroscience. 2014. DOI : 10.3389/fncom.2014.00038.

Books

W. Gerstner; W. M. Kistler; R. Naud; L. Paninski : Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition ; Cambridge University Press.

Theses

C. A. Pozzorini / W. Gerstner (Dir.) : Computational principles of single neuron adaptation. Lausanne, EPFL, 2014. DOI : 10.5075/epfl-thesis-6461.
S. Mensi / W. Gerstner (Dir.) : A new Mathematical Framework to Understand Single Neuron Computations. Lausanne, EPFL, 2014. DOI : 10.5075/epfl-thesis-6242.
F. Zenke / W. Gerstner (Dir.) : Memory formation and recall in recurrent spiking neural networks. Lausanne, EPFL, 2014. DOI : 10.5075/epfl-thesis-6260.
L. Ziegler / W. Gerstner (Dir.) : Synaptic Learning Rules with Consolidation. Lausanne, EPFL, 2014. DOI : 10.5075/epfl-thesis-6126.
J. E. F. Gerhard / W. Gerstner (Dir.) : Statistical models of effective connectivity in neural microcircuits. Lausanne, EPFL, 2014. DOI : 10.5075/epfl-thesis-6095.

Posters

M. Faraji; K. Preuschoff; W. Gerstner : A biologically plausible model of the learning rate dynamics ; Gordon Research Conference on Neurobiology of Cognition (GRC), Sunday River Resort - Newry, Maine, USA, July 20-25, 2014.
M. Faraji; K. Preuschoff; W. Gerstner : Neuromodulation by surprise: a biologically plausible model of the learning rate dynamics ; Computational Neuroscience Meeting (CNS), Quebec City, Canada, July 26-31, 2014.
H. Setareh; M. Deger; W. Gerstner : The role of interconnected hub neurons in cortical dynamics ; CNS 2014, Quebec City, Canada, July 26-31, 2014.
W. F. Podlaski; A. Seeholzer; R. Rajnish; T. Vogels : Visualizing the similarity and pedigree of NEURON ion channel models available on ModelDB ; COSYNE 2014, Salt Lake City & Snowbird, Utah, USA, February 27 - March 4, 2014.
D. Kastner; S. A. Baccus; T. O. Sharpee : Second Order Phase Transition Describes Maximally Informative Encoding in the Retina ; COSYNE 2014, Salt Lake City & Snowbird, Utah, USA, February 27 - March 4, 2014.
M. Faraji; K. Preuschoff; W. Gerstner : Surprise-based learning: neuromodulation by surprise in multi-factor learning rules ; Computational and Systems Neuroscience (COSYNE), Salt Lake City, Utah, USA, February 27 - March 4, 2014.
F. Zenke; E. Agnes : Learning Multi-Stability in Plastic Neural Networks ; COSYNE 2014, Salt Lake City & Snowbird, Utah, USA, February 27 - March 4, 2014.
M. Deger; T. Schwalger; R. Naud : Network dynamics of spiking neurons with adaptation ; COSYNE 2014, Salt Lake City & Snowbird, Utah, USA, February 27 - March 4, 2014.
D. S. Corneil; E. Neftci; G. Indiveri; M. Pfeiffer : Learning, Inference, and Replay of Hidden State Sequences in Recurrent Spiking Neural Networks ; COSYNE 2014, Salt Lake City & Snowbird, Utah, USA, February 27 - March 4, 2014.
T. Schwalger; F. Droste; B. Lindner : Statistical structure of neural spiking under non-Poissonian stimulation ; COSYNE 2014, Salt Lake City & Snowbird, Utah, USA, February 27 - March 4, 2014.

2013

Journal Articles

F. Zenke; G. Hennequin; W. Gerstner : Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector; Plos Computational Biology. 2013. DOI : 10.1371/journal.pcbi.1003330.
H. Luetcke; F. Gerhard; F. Zenke; W. Gerstner; F. Helmchen : Inference of neuronal network spike dynamics and topology from calcium imaging data; Frontiers In Neural Circuits. 2013. DOI : 10.3389/fncir.2013.00201.
T. Schwalger; B. Lindner : Patterns of interval correlations in neural oscillators with adaptation; Frontiers In Computational Neuroscience. 2013. DOI : 10.3389/fncom.2013.00164.
V. Pawlak; D. S. Greenberg; H. Sprekeler; W. Gerstner; J. N. D. Kerr : Changing the responses of cortical neurons from sub- to suprathreshold using single spikes in vivo; Elife. 2013. DOI : 10.7554/eLife.00012.001.
C. A. Pozzorini; R. Naud; S. Mensi; W. Gerstner : Temporal whitening by power-law adaptation in neocortical neurons; Nature Neuroscience. 2013. DOI : 10.1038/nn.3431.
F. Gerhard; T. Kispersky; G. J. Gutierrez; E. Marder; U. Eden : Successful Reconstruction of a Physiological Circuit with Known Connectivity from Spiking Activity Alone; Plos Computational Biology. 2013. DOI : 10.1371/journal.pcbi.1003138.
N. Frémaux; H. Sprekeler; W. Gerstner : Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons; Plos Computational Biology. 2013. DOI : 10.1371/journal.pcbi.1003024.
E. Agliari; A. Barra; A. De Antoni; A. Galluzzi : Parallel retrieval of correlated patterns: From Hopfield networks to Boltzmann machines; Neural Networks. 2013. DOI : 10.1016/j.neunet.2012.11.010.
J. Rüter; H. Sprekeler; W. Gerstner; M. H. Herzog : The Silent Period of Evidence Integration in Fast Decision Making; PLoS ONE. 2013. DOI : 10.1371/journal.pone.0046525.

Reviews

T. P. Vogels; R. C. Froemke; N. Doyon; M. Gilson; J. S. Haas et al. : Inhibitory synaptic plasticity: spike timing-dependence and putative network function; Frontiers In Neural Circuits. 2013. DOI : 10.3389/fncir.2013.00119.

Theses

N. Frémaux / W. Gerstner (Dir.) : Models of Reward-Modulated Spike-Timing-Dependent Plasticity. Lausanne, EPFL, 2013. DOI : 10.5075/epfl-thesis-5683.
G. Hennequin / W. Gerstner (Dir.) : Stability and amplification in plastic cortical circuits. Lausanne, EPFL, 2013. DOI : 10.5075/epfl-thesis-5585.

Book Chapters

R. Naud; W. Gerstner : Can We Predict Every Spike?; Spike Timing: Mechanisms and Function; Boca Raton: CRC Press, 2013. p. 65-76.

Posters

C. A. Pozzorini; R. Naud; S. Mensi : Temporal decorrelation by power-law adaptation in pyramidal neurons ; COSYNE, Salt Lake City, USA, February 28 - March 3, 2013.
S. Mensi; C. A. Pozzorini; O. Hagens : Evidence for a nonlinear coupling between firing threshold and subthreshold membrane potential ; COSYNE, Salt Lake City, USA, February 28 - March 3, 2013.

2012

Journal Articles

C. Molter; J. O'Neill; Y. Yamaguchi; H. Hirase; X. Leinekugel : Rhythmic Modulation of Theta Oscillations Supports Encoding of Spatial and Behavioral Information in the Rat Hippocampus; Neuron. 2012. DOI : 10.1016/j.neuron.2012.06.036.
W. Gerstner; H. Sprekeler; G. Deco : Theory and Simulation in Neuroscience; Science. 2012. DOI : 10.1126/science.1227356.
R. Naud; W. Gerstner : Coding and Decoding with Adapting Neurons: A Population Approach to the Peri-Stimulus Time Histogram; Plos Computational Biology. 2012. DOI : 10.1371/journal.pcbi.1002711.
G. Hennequin; T. Vogels; W. Gerstner : Non-normal amplification in random balanced neuronal networks; Phys. Rev. E. 2012. DOI : 10.1103/PhysRevE.86.011909.
M. Avermann; C. Tomm; C. Mateo; W. Gerstner; C. C. H. Petersen : Microcircuits of excitatory and inhibitory neurons in layer 2/3 of mouse barrel cortex; Journal Of Neurophysiology. 2012. DOI : 10.1152/jn.00917.2011.
M. H. Herzog; K. C. Aberg; N. Frémaux; W. Gerstner; H. Sprekeler : Perceptual learning, roving and the unsupervised bias; Vision Research. 2012. DOI : 10.1016/j.visres.2011.11.001.

Conference Papers

F. Gerhard; L. Szegletes : Spline- and Wavelet-based Models of Neural Activity in Response to Natural Visual Stimulation. 2012. 34th Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS). p. 4611-4614.