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Journal Paper

Pending

M. Faraji, K. Preuschoff and W. Gerstner, Balancing New Against Old Information: The Role of Surprise, submitted to arXiv, 2016.
[detailed record] [bibtex]

2017

H. Setareh, M. Deger, C.C. Petersen and W. Gerstner, Cortical Dynamics in Presence of Assemblies of Densely Connected Weight-Hub Neurons, Frontiers in Computational Neuroscience, Vol. 11, Nr. 52, 2017.
[detailed record] [bibtex]

W.F. Podlaski, A. Seeholzer, L.N. Groschner, G. Miesenbock, R. Ranjan and T.P. Vogels, Mapping the function of neuronal ion channels in model and experiment, Elife, Vol. 6, pp. e22152, 2017.
[detailed record] [bibtex]

T. Schwalger, M. Deger and W. Gerstner, Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size, PLoS Computational Biology, Vol. 13, Nr. 4, pp. e1005507, 2017.
[detailed record] [bibtex]

F. Gerhard, M. Deger and W. Truccolo, On the stability and dynamics of stochastic spiking neuron models: Nonlinear Hawkes process and point process GLMs, Plos Computational Biology, Vol. 13, Nr. 2, pp. e1005390, 2017.
[detailed record] [bibtex]

S.P. Muscinelli, W. Gerstner and J.M. Brea, Exponentially long orbits in Hopfield neural networks, Neural Computation, Vol. 29, Nr. 2, pp. 458-484, 2017.
[detailed record] [bibtex]

2016

C.S.N. Brito and W. Gerstner, Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation, PLoS Computational Biology, Vol. 12, Nr. 9, pp. e1005070, 2016.
[detailed record] [bibtex]

J. Brea, A.T. Gaal, R. Urbanczik and W. Senn, Prospective Coding by Spiking Neurons, Plos Computational Biology, Vol. 12, Nr. 6, pp. e1005003, 2016.
[detailed record] [bibtex]

Y. Burnier and C. Gastaldi, Contribution of next-to-leading order and Landau-Pomeranchuk-Migdal corrections to thermal dilepton emission in heavy-ion collisions, Physical Review C, Vol. 93, Nr. 4, pp. 044902, 2016.
[detailed record] [bibtex]

J.M. Brea and W. Gerstner, Does computational neuroscience need new synaptic learning paradigms?, Current Opinion in Behavioral Sciences, 2016.
[detailed record] [bibtex]

D.B. Kastner, T. Schwalger, L. Ziegler and W. Gerstner, A Model of Synaptic Reconsolidation, Frontiers in Neuroscience, Vol. 10, pp. 206, 2016.
[detailed record] [bibtex]

S. Mensi, O. Hagens, W. Gerstner and C. Pozzorini, Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons, Plos Computational Biology, Vol. 12, pp. e1004761, 2016.
[detailed record] [bibtex]

N. Fremaux and W. Gerstner, Neuromodulated-Spike-Timing-Dependent Pasticity, and Theory of Three-Factor Learning Rules, Frontiers in Neural Circuits, Vol. 9, pp. 85, 2016.
[detailed record] [bibtex]

D. Shuman, M. Faraji and P. Vandergheynst, A Multiscale Pyramid Transform for Graph Signals, IEEE Transactions on Signal Processing, Vol. 64, Nr. 8, pp. 2119 - 2134, 2016.
[detailed record] [bibtex]

2015

T. Schwalger and B. Lindner, Analytical approach to an integrate-and-fire model with spike-triggered adaptation, Physical Review E, Vol. 92, Nr. 6, pp. 062703, 2015.
[detailed record] [bibtex]

S. Wieland, D. Bernardi, T. Schwalger and B. Lindner, Slow fluctuations in recurrent networks of spiking neurons, Physical Review E, Vol. 92, Nr. 4, 2015.
[detailed record] [bibtex]

C.A. Pozzorini, S. Mensi, O. Hagens, R. Naud, C. Koch and W. Gerstner, Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models, Plos Computational Biology, Vol. 11, Nr. 4, pp. e1004275, 2015.
[detailed record] [bibtex]

Y.V. Zaytsev, A. Morrison and M. Deger, Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity, Journal of Computational Neuroscience, Vol. 39, Nr. 1, pp. 77-103, 2015.
[detailed record] [bibtex]

L. Shiau, T. Schwalger and B. Lindner, Interspike interval correlation in a stochastic exponential integrate-and-fire model with subthreshold and spike-triggered adaptation, Journal Of Computational Neuroscience, Vol. 38, Nr. 3, pp. 589-600, 2015.
[detailed record] [bibtex]

T. Schwalger, F. Droste and B. Lindner, Statistical structure of neural spiking under non-Poissonian or other non-white stimulation, Journal of Computational Neuroscience, 2015.
[detailed record] [bibtex]

F. Zenke, E.J. Agnes and W. Gerstner, Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks, Nature Communications, Vol. 6, Nr. 6922, 2015.
[detailed record] [bibtex]

L. Ziegler, F. Zenke, D.B. Kastner and W. Gerstner, Synaptic consolidation: from synapses to behavioral modeling, The Journal of neuroscience : the official journal of the Society for Neuroscience, Vol. 35, Nr. 3, pp. 1319-34, 2015.
[detailed record] [bibtex]

2014

C. Tomm, M. Avermann, C. Petersen, W. Gerstner and T.P. Vogels, Connection-type-specific biases make uniform random network models consistent with cortical recordings, Journal Of Neurophysiology, Vol. 112, Nr. 8, pp. 1801-1814, 2014.
[detailed record] [bibtex]

M. Deger, T. Schwalger, R. Naud and W. Gerstner, Fluctuations and information filtering in coupled populations of spiking neurons with adaptation, Physical Review E, Vol. 90, Nr. 6, pp. 062704, 2014.
[detailed record] [bibtex]

A. Seeholzer, E. Frey and B. Obermayer, Periodic versus Intermittent Adaptive Cycles in Quasispecies Coevolution, Physical Review Letters, Vol. 113, Nr. 12, 2014.
[detailed record] [bibtex]

F. Zenke and W. Gerstner, Limits to high-speed simulations of spiking neural networks using general-purpose computers, Frontiers in neuroinformatics, Vol. 8, pp. 76, 2014.
[detailed record] [bibtex]

R. Naud, B. Bathellier and W. Gerstner, Spike-timing prediction in cortical neurons with active dendrites, Frontiers in Computational Neuroscience, Vol. 8, Nr. 90, 2014.
[detailed record] [bibtex]

G. Hennequin, T. Vogels and W. Gerstner, Optimal Control of Transient Dynamics in Balanced Networks Supports Generation of Complex Movements, Neuron, Vol. 82, pp. 1394–1406, 2014.
[detailed record] [bibtex]

D.J. Rezende and W. Gerstner, Stochastic variational learning in recurrent spiking networks, Frontiers In Computational Neuroscience, Vol. 8, 2014.
[detailed record] [bibtex]

2013

F. Zenke, G. Hennequin and W. Gerstner, Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector, Plos Computational Biology, Vol. 9, Nr. 11, 2013.
[detailed record] [bibtex]

H. Luetcke, F. Gerhard, F. Zenke, W. Gerstner and F. Helmchen, Inference of neuronal network spike dynamics and topology from calcium imaging data, Frontiers In Neural Circuits, Vol. 7, 2013.
[detailed record] [bibtex]

T. Schwalger and B. Lindner, Patterns of interval correlations in neural oscillators with adaptation, Frontiers In Computational Neuroscience, Vol. 7, 2013.
[detailed record] [bibtex]

V. Pawlak, D.S. Greenberg, H. Sprekeler, W. Gerstner and J.N.D. Kerr, Changing the responses of cortical neurons from sub- to suprathreshold using single spikes in vivo, Elife, Vol. 2, 2013.
[detailed record] [bibtex]

C.A. Pozzorini, R. Naud, S. Mensi and W. Gerstner, Temporal whitening by power-law adaptation in neocortical neurons, Nature Neuroscience, Vol. 16, Nr. 7, pp. 942-966, 2013.
[detailed record] [bibtex]

F. Gerhard, T. Kispersky, G.J. Gutierrez, E. Marder and U. Eden, Successful Reconstruction of a Physiological Circuit with Known Connectivity from Spiking Activity Alone, Plos Computational Biology, Vol. 9, Nr. 7, pp. 003138, 2013.
[detailed record] [bibtex]

N. Frémaux, H. Sprekeler and W. Gerstner, Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons, Plos Computational Biology, Vol. 9, Nr. 4, pp. 1-21, 2013.
[detailed record] [bibtex]

E. Agliari, A. Barra, A. De Antoni and A. Galluzzi, Parallel retrieval of correlated patterns: From Hopfield networks to Boltzmann machines, Neural Networks, Vol. 38, pp. 52-63, 2013.
[detailed record] [bibtex]

J. Rüter, H. Sprekeler, W. Gerstner and M.H. Herzog, The Silent Period of Evidence Integration in Fast Decision Making, PLoS ONE, Vol. 8, Nr. 1, pp. e46525, 2013.
[detailed record] [bibtex]

2012

C. Molter, J. O'Neill, Y. Yamaguchi, H. Hirase and X. Leinekugel, Rhythmic Modulation of Theta Oscillations Supports Encoding of Spatial and Behavioral Information in the Rat Hippocampus, Neuron, Vol. 75, Nr. 5, pp. 889-903, 2012.
[detailed record] [bibtex]

W. Gerstner, H. Sprekeler and G. Deco, Theory and Simulation in Neuroscience, Science, Vol. 338, pp. 60-65, 2012.
[detailed record] [bibtex]

R. Naud and W. Gerstner, Coding and Decoding with Adapting Neurons: A Population Approach to the Peri-Stimulus Time Histogram, Plos Computational Biology, Vol. 8, Nr. 10, pp. e1002711, 2012.
[detailed record] [bibtex]

G. Hennequin, T. Vogels and W. Gerstner, Non-normal amplification in random balanced neuronal networks, Phys. Rev. E, Vol. 86, Nr. 1, pp. 011909, 2012.
[detailed record] [bibtex]

M. Avermann, C. Tomm, C. Mateo, W. Gerstner and C.C.H. Petersen, Microcircuits of excitatory and inhibitory neurons in layer 2/3 of mouse barrel cortex, Journal Of Neurophysiology, Vol. 107, Nr. 11, pp. 3116-3134, 2012.
[detailed record] [bibtex]

M.H. Herzog, K.C. Aberg, N. Frémaux, W. Gerstner and H. Sprekeler, Perceptual learning, roving and the unsupervised bias, Vision Research, Vol. 61, pp. 95-99, 2012.
[detailed record] [bibtex]

S. Mensi, R. Naud, C. Pozzorini, M. Avermann, C.C.H. Petersen and W. Gerstner, Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms, Journal Of Neurophysiology, Vol. 107, Nr. 6, pp. 1756-1775, 2012.
[detailed record] [bibtex]

2011

F. Gerhard, G. Pipa, B. Lima, S. Neuenschwander and W. Gerstner, Extraction of network topology from multi-electrode recordings: is there a small-world effect?, Frontiers In Computational Neuroscience, Vol. 5, pp. 1-13, 2011.
[detailed record] [bibtex]

L. Wiskott, P. Berkes, M. Franzius, H. Sprekeler and N. Wilbert, Slow Feature Analysis , Scholarpedia Journal, Vol. 6, Nr. 4, pp. 5282, 2011.
[detailed record] [bibtex]

H. Sprekeler, On the Relation of Slow Feature Analysis and Laplacian Eigenmaps, Neural Computation, Vol. 23, Nr. 12, pp. 3287-3302, 2011.
[detailed record] [bibtex]

H. Sprekeler and L. Wiskott, A Theory of Slow Feature Analysis for Transformation-Based Input Signals with an Application to Complex Cells, Neural Computation, Vol. 23, pp. 303-335, 2011.
[detailed record] [bibtex]

J. Gjorgjieva, C. Clopath, J. Audet and J.-P. Pfister, A triplet spike-timing-dependent plasticity model generalizes the Bienenstock-Cooper-Munro rule to higher-order spatiotemporal correlations, Proceedings Of The National Academy Of Sciences Of The United States Of America, Vol. 108, pp. 19383-19388, 2011.
[detailed record] [bibtex]

T. Vogels, H. Sprekeler, F. Zenke, C. Clopath and W. Gerstner, Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks, Science, Vol. 334, Nr. 6062, pp. 1569-1573, 2011.
[detailed record] [bibtex]

R. Naud, F. Gerhard, S. Mensi and W. Gerstner, Improved Similarity Measures for Small Sets of Spike Trains, Neural Computation, Vol. 23, Nr. 12, pp. 3016-3069, 2011.
[detailed record] [bibtex]

H. Markram, W. Gerstner and P.J. Sjöström, A history of spike-timing-dependent plasticity, Frontiers in Synaptic Neuroscience, Vol. 3, Nr. 4, pp. 1-24, 2011.
[detailed record] [bibtex]

G. Luksys and C. Sandi, Neural mechanisms and computations underlying stress effects on learning and memory, Curr Opin Neurobiol, Vol. 21, Nr. 3, pp. 502-8, 2011.
[detailed record] [bibtex]

F. Gerhard, R. Haslinger and G. Pipa, Applying the Multivariate Time-Rescaling Theorem to Neural Population Models, Neural Computation, Vol. 23, Nr. 6, pp. 1452-1483, 2011.
[detailed record] [bibtex]

2010

T. Schaul, J. Bayer, D. Wierstra, Y. Sun, M. Felder, F. Sehnke, T. Rueckstieß and J. Schmidhuber, PyBrain, Journal of Machine Learning Research, Vol. 11, pp. 743-746, 2010.
[detailed record] [bibtex]

G. Hennequin, W. Gerstner and J.-P. Pfister, STDP in adaptive neurons gives close-to-optimal information transmission, frontiers in computational neuroscience, Vol. 4, Nr. 143, 2010.
[detailed record] [bibtex]

W. Gerstner, From Hebb rules to Spike-Timing-Dependent Plasticity: a personal account, frontiers in synaptic neuroscience, Vol. 2, Nr. 151, 2010.
[detailed record] [bibtex]

C. Clopath and W. Gerstner, Voltage and spike timing interact in STDP – a unified model, Frontiers in Synaptic Neuroscience, Vol. 2, Nr. 25, 2010.
[detailed record] [bibtex]

N. Frémaux, H. Sprekeler and W. Gerstner, Functional Requirements for Reward-Modulated Spike-Timing-Dependent Plasticity, Journal of Neuroscience, Vol. 30, Nr. 40, pp. 13326-13337, 2010.
[detailed record] [bibtex]

W. Gerstner and J. Sjostrom, Spike-timing dependent plasticity, Scholarpedia, Vol. On line, 2010.
[detailed record] [bibtex]

C. Clopath, L. Büsing, E. Vasilaki and W. Gerstner, Connectivity reflects coding: a model of voltage-based STDP with homeostasis, Nature Neuroscience, Vol. 13, Nr. 3, pp. 344-352, 2010.
[detailed record] [bibtex]

2009

G. Luksys, W. Gerstner and C. Sandi, Stress, genotype and norepinephrine in the prediction of mouse behavior using reinforcement learning, Nat Neurosci, Vol. 12, Nr. 9, pp. 1180-6, 2009.
[detailed record] [bibtex]

E. Vasilaki, N. Frémaux, R. Urbanczik, W. Senn and W. Gerstner, Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail, PLoS Computational Biology, Vol. 5, Nr. 12, pp. e1000586, 2009.
[detailed record] [bibtex]

W. Gerstner and R. Naud, How Good are Neuron Models?, Science, Vol. 326, Nr. 5951, pp. 379-380, 2009.
[detailed record] [bibtex]

D. Bruederle, Establishing a Novel Modeling Tool: A Python-based Interface for a Neuromorphic Hardware System, Frontiers in Neuroinformatics, Vol. 3, Nr. 17, pp. 1-10, 2009.
[detailed record] [bibtex]

M. Hines, A.P. Davison and E. Muller, NEURON and Python, Frontiers in Neuroinformatics, Vol. 3, Nr. 1, pp. 1-12, 2009.
[detailed record] [bibtex]

D. Sheynikhovich, R. Chavarriaga, T. Strösslin, A. Arleo and W. Gerstner, Is there a geometric module for spatial orientation? Insights from a rodent navigation model, Psychological Review, Vol. 116, Nr. 3, pp. 540-566, 2009.
[detailed record] [bibtex]

2008

B. Bathellier and A. Carleton, Gamma Oscillations in a Nonlinear Regime: A Minimal Model Approach Using Heterogeneous Integrate-and-Fire Networks, Neural Computation, Vol. 20, Nr. 12, pp. 2973-3002, 2008.
[detailed record] [bibtex]

R. Jolivet, A. Roth, F. Schuermann, W. Gerstner and W. Senn, Special issue on quantitative neuron modeling, Biological Cybernetics, Vol. 99, pp. 237-239, 2008.
[detailed record] [bibtex]

F. Hermens, G. Luksys, W. Gerstner, M.H. Herzog and U. Ernst, Modeling spatial and temporal aspects of visual backward masking, Psychological review, Vol. 115, Nr. 1, pp. 83-100, 2008.
[detailed record] [bibtex]

J.M. Eppler, M. Helias, E. Muller, M. Diesmann and M.-O. Gewaltig, PyNEST: a convenient interface to the NEST simulator, Frontiers in Neuroinformatics, Vol. 2, Nr. 12, pp. 1-12, 2008.
[detailed record] [bibtex]

A.P. Davison, D. Brüderle, J. Eppler, J. Kremkow, E. Muller, D. Pecevski, L. Perrinet and P. Yger, PyNN: a common interface for neuronal network simulators, Frontiers in Neuroinformatics, Vol. 2, Nr. 11, pp. 1-10, 2008.
[detailed record] [bibtex]

L. Badel, S. Lefort, T.K. Berger, C.C.H. Petersen, W. Gerstner and M.J.E. Richardson, Extracting non-linear integrate-and-fire models from experimental data using dynamic I – V curves , Biological Cybernetics, Vol. 99, Nr. 4-5, pp. 361-370, 2008.
[detailed record] [bibtex]

C. Clopath, L. Ziegler, E. Vasilaki, L. Buesing and W. Gerstner, Tag-Trigger-Consolidation: A Model of Early and Late Long-Term-Potentiation and Depression, PLoS Comput Biol, Vol. 4, Nr. 12, pp. e1000248, 2008.
[detailed record] [bibtex]

R. Jolivet, F. Schürmann, T.K. Berger, R. Naud, W. Gerstner and A. Roth, The quantitative single-neuron modeling competition, Biological Cybernetics, Vol. 99, Nr. 4-5, pp. 417-426, 2008.
[detailed record] [bibtex]

R. Naud, N. Marcille, C. Clopath and W. Gerstner, Firing patterns in the adaptive exponential integrate-and-fire model , Biological Cybernetics, Vol. 99, Nr. 4-5, pp. 335-347, 2008.
[detailed record] [bibtex]

F. Creutzig and H. Sprekeler, Predictive Coding and the Slowness Principle: An Information-Theoretic Approach, Neural Computation, Vol. 20, Nr. 4, pp. 1026-1041, 2008.
[detailed record] [bibtex]

A. Morrison, M. Diesmann and W. Gerstner, Phenomenological models of synaptic plasticity based on spike timing, Biological Cybernetics, Vol. 98, Nr. 6, pp. 459-478, 2008.
[detailed record] [bibtex]

L. Badel, W. Gerstner and J.E. Richardson, Spike-triggered averages for passive and resonant neurons receiving filtered excitatory and inhibitory synaptic drive, PHYSICAL REVIEW E, Vol. 78, Nr. 1, pp. 011914 1-12, 2008.
[detailed record] [bibtex]

R. Jolivet, R. Kobayashi, A. Rauch, R. Naud, S. Shinomoto and W. Gerstner, A benchmark test for a quantitative assessment of simple neuron models, Journal of Neuroscience Methods, Vol. 169, Nr. 2, pp. 417-424, 2008.
[detailed record] [bibtex]

L. Badel, S. Lefort, R. Brette, C.C.H. Petersen, W. Gerstner and M.J.E. Richardson, Dynamic I-V curves are reliable predictors of naturalistic pyramidal-neuron voltage traces, Journal of Neurophysiology, Vol. 99, pp. 656-666, 2008.
[detailed record] [bibtex]

2007

M. Franzius, H. Sprekeler and L. Wiskott, Slowness and Sparseness Lead to Place, Head-Direction, and Spatial-View Cells, PLoS Computational Biology, Vol. 3, Nr. 8, pp. 1605-1622, 2007.
[detailed record] [bibtex]

H. Sprekeler, C. Michaelis and L. Wiskott, Slowness: An Objective for Spike-Timing-Dependent Plasticity?, PLoS Computational Biology, Vol. 3, Nr. 6, pp. 1136-1148, 2007.
[detailed record] [bibtex]

M.H. Herzog, M. Esfeld and W. Gerstner, Consciousness & the Small Network Argument , Neural Networks, Vol. 20, Nr. 9, pp. 1054-1056, 2007.
[detailed record] [bibtex]

C. Clopath, R. Jolivet, A. Rauch, H.-R. Lüscher and W. Gerstner, Predicting neuronal activity with simple models of the threshold type: Adaptive Exponential Integrate-and-Fire model with two compartments, Neurocomputing, Vol. 70, Nr. 10-12, pp. 1668-1673, 2007.
[detailed record] [bibtex]

T. Toyoizumi, J.-P. Pfister, K. Aihara and W. Gerstner, Optimality Model of Unsupervised Spike-Timing Dependent Plasticity: Synaptic Memory and Weight Distribution, Neural Computation, Vol. 19, Nr. 3, pp. 639-671, 2007.
[detailed record] [bibtex]

2006

L. Badel, W. Gerstner and M.J.E. Richardson, Dependence of the spike-triggered average voltage on membrane response properties, Neurocomputing, Vol. 69, Nr. 10-12, pp. 1062-1065, 2006.
[detailed record] [bibtex]

Y. Aviel and W. Gerstner, From spiking neurons to rate models: a cascade model as an approximation to spiking neuron models with refractoriness, Phys. Rev. E, Vol. 73, Nr. 5, pp. 051908 1-10, 2006.
[detailed record] [bibtex]

R. Jolivet, A. Rauch, H.R. Lüscher and W. Gerstner, Predicting spike timing of neocortical pyramidal neurons by simple threshold models, J. Computational Neuroscience, Vol. 21, Nr. 1, pp. 35-49, 2006.
[detailed record] [bibtex]

J.P. Pfister and W. Gerstner, Triplets of Spikes in a Model of Spike Timing-Dependent Plasticity, J. Neuroscience, Vol. 26, Nr. 38, pp. 9673-9682, 2006.
[detailed record] [bibtex]

J.P. Pfister, T. Toyoizumi, D. Barber and W. Gerstner, Optimal Spike-Timing Dependent Plasticity for Precise Action Potential Firing in Supervised Learning, Neural Computation, Vol. 18, Nr. 6, pp. 1318-1348, 2006.
[detailed record] [bibtex]

M.J.E. Richardson and W. Gerstner, Statistics of subthreshold neuronal voltage fluctuations due to conductance-based synaptic shot noise, Chaos, Vol. 16, Nr. 2, pp. 26106, 2006.
[detailed record] [bibtex]

2005

O. Melamed, G. Silberberg, H. Markram, W. Gerstner and M.J.E. Richardson, Subthreshold cross-correlations between cortical neurons: A reference model with static synapses, NEUROCOMPUTING, Nr. 65-66, pp. 685-690, 2005.
[detailed record] [bibtex]

T. Strösslin, D. Sheynikhovich, R. Chavarriaga and W. Gerstner, Robust self-localisation and navigation based on hippocampal place cells, NEURAL NETWORKS , Vol. 18, Nr. (9), pp. 1125-1140, 2005.
[detailed record] [bibtex]

R. Brette and W. Gerstner, Adaptive Exponential Integrate-and-Fire Model as an Effective Description of Neuronal Activity, J. Neurophysiol., Vol. 94, pp. 3637 - 3642, 2005.
[detailed record] [bibtex]

R. Chavarriaga, T. Strösslin, D. Sheynikhovich and W. Gerstner, Competition between cue response and place response: A model of rat navigation behaviour, Connection Science, Vol. 17, Nr. 1-2, pp. 167-183, 2005.
[detailed record] [bibtex]

R. Chavarriaga, T. Strösslin, D. Sheynikhovich and W. Gerstner, A computational model of parallel navigation systems in rodents, Neuroinformatics, Vol. 3, Nr. 3, pp. 223-241, 2005.
[detailed record] [bibtex]

J. Mayor and W. Gerstner, Signal buffering in random networks of spiking neurons: microscopic vs. macroscopic phenomena, Phys. Rev. E, Vol. 72, Nr. 5, pp. 051906, 2005.
[detailed record] [bibtex]

J. Mayor and W. Gerstner, Noise-enhanced computation in a model of a cortical column, Neuroreport, Vol. 16, Nr. 11, pp. 1237-1240, 2005.
[detailed record] [bibtex]

M.J.E. Richardson and W. Gerstner, Synaptic Shot Noise and Conductance Fluctuations Affect the Membrane Voltage with Equal Significance, Neural Computation, Vol. 17, Nr. 4, pp. 923-947, 2005.
[detailed record] [bibtex]

Conference Paper

Pending

D. Jimenez Rezende, D. Wierstra and W. Gerstner, Variational Learning for Recurrent Spiking Networks, 2011.
[detailed record] [bibtex]

2016

F. Colombo, S.P. Muscinelli, A. Seeholzer, J. Brea and W. Gerstner, Algorithmic Composition of Melodies with Deep Recurrent Neural Networks, Proceedings of the 1st Conference on Computer Simulation of Musical Creativity, 2016.
[detailed record] [bibtex]

2015

D.S. Corneil and W. Gerstner, Attractor Network Dynamics Enable Preplay and Rapid Path Planning in Maze–like Environments, Advances in Neural Information Processing Systems, Vol. 28, pp. 1675-1683, 2015.
[detailed record] [bibtex]

T. Schwalger, M. Deger and W. Gerstner, Bridging spiking neuron models and mesoscopic population models - a general theory for neural population dynamics, BMC Neuroscience, Vol. 16, Nr. Suppl 1, pp. P79, 2015.
[detailed record] [bibtex]

M. Faraji, K. Preuschoff and W. Gerstner, A biologically plausible 3-factor learning rule for expectation maximization in reinforcement learning and decision making, 2015.
[detailed record] [bibtex]

2012

F. Gerhard and L. Szegletes, Spline- and Wavelet-based Models of Neural Activity in Response to Natural Visual Stimulation, 2012 Annual International Conference Of The Ieee Engineering In Medicine And Biology Society (Embc), pp. 4611-4614, 2012.
[detailed record] [bibtex]

2011

S. Mensi, R. Naud and W. Gerstner, From Stochastic Nonlinear Integrate-and-Fire to Generalized Linear Models, 2011.
[detailed record] [bibtex]

A.H. Salavati, K.R. Kumar, A. Shokrollahi and W. Gerstner, Neural Pre-coding Increases the Pattern Retrieval Capacity of Hopfield and Bidirectional Associative Memories, Proceedings of the International Symposium On Information Theory Proceedings (ISIT), pp. 850-854, 2011.
[detailed record] [bibtex]

2010

N. Marcille, J. Rueter, M.H. Herzog and W. Gerstner, A two stage model for perceptual decision making, Perception, Vol. 39, pp. 44-44, 2010.
[detailed record] [bibtex]

T. Glasmachers, T. Schaul, S. Yi, D. Wierstra and J. Schmidhuber, Exponential Natural Evolution Strategies, Proceedings of the 12th annual conference on Genetic and evolutionary computation, pp. 393-400, 2010.
[detailed record] [bibtex]

F. Gerhard and W. Gerstner, Rescaling, thinning or complementing? On goodness-of-fit procedures for point process models and Generalized Linear Models, Advances in Neural Information Processing Systems, Vol. 23, 2010.
[detailed record] [bibtex]

S. Mensi, R. Naud, T.K. Becker and W. Gerstner, Complexity and performance in simple neuron models, , 2010.
[detailed record] [bibtex]

2009

H. Sprekeler, G. Hennequin and W. Gerstner, Code-specific policy gradient rules for spiking neurons, Advances in Neural Information Processing Systems, Vol. 22, pp. 1741-1749, 2009.
[detailed record] [bibtex]

R. Naud, B. Bathellier and W. Gerstner, Spike-timing prediction in a neuron model with active dendrites, , 2009.
[detailed record] [bibtex]

2008

N. Marcille, J. Rüter, M.H. Herzog and W. Gerstner, Modelling feature-integration in human vision with drift diffusion models, FENS Abstr., Vol. 4, pp. 220.9, 2008.
[detailed record] [bibtex]

R. Naud, T. Berger, L. Badel, A. Roth and W. Gerstner, Quantitative single-neuron modeling: competition 2008, , 2008.
[detailed record] [bibtex]

C. Clopath, A. Longtin and W. Gerstner, An online Hebbian learning rule that performs Independent Component Analysis, Advances in Neural Information Processing Systems 20, pp. 321-328, 2008.
[detailed record] [bibtex]

2006

R. Jolivet, A. Rauch, H.R. Lüscher and W. Gerstner, Integrate-and-Fire models with adaptation are good enough, Advances in Neural Information Processing Systems 18, pp. 595-602, 2006.
[detailed record] [bibtex]

J.P. Pfister and W. Gerstner, Beyond Pair-Based STDP: a Phenomenological Rule for Spike Triplet and Frequency Effects, Advances in Neural Information Processing Systems 18, pp. 1083--1090, 2006.
[detailed record] [bibtex]

2005

T. Strösslin, R. Chavarriaga, D. Sheynikhovich and W. Gerstner, Modelling Path Integrator Recalibration Using Hippocampal Place cells, , Vol. 3696, pp. Chapter: p. 51, 2005.
[detailed record] [bibtex]

D. Sheynikhovich, R. Chavarriaga, T. Strösslin and W. Gerstner, Spatial Representation and Navigation in a Bio-inspired Robot, Biomimetic Neural Learning for Intelligent Robots: Intelligent Systems, Cognitive Robotics, and Neuroscience, pp. 245-264, 2005.
[detailed record] [bibtex]

T. Toyoizumi, J.-P. Pfister, K. Aihara and W. Gerstner, Spike-timing Dependent Plasticity and Mutual Information Maximization for a Spiking Neuron Model, Advances in Neural Information Processing Systems 17, pp. 1409-1416, 2005.
[detailed record] [bibtex]

2003

J.-P. Pfister, D. Barber and W. Gerstner, Optimal Hebbian Learning: a Probabilistic Point of View, ICANN/ICONIP, 2003.
[detailed record] [bibtex]

R. Jolivet, T.J. Lewis and W. Gerstner, The Spike Response Model: A Framework to Predict Neuronal Spike Trains, Proceedings of the Joint International Conference ICANN/ICONIP 2003, pp. 846-853, 2003.
[detailed record] [bibtex]

J.d.R. Millán, F. Renkens, J. Mouriño and W. Gerstner, Non-Invasive Brain-Actuated Control of a Mobile Robot, Proceedings of the 18th International Joint Conference on Artificial Intelligence, pp. 1121-1126, 2003.
[detailed record] [bibtex]

2001

A. Arleo and W. Gerstner, Hippocampal spatial model for state space representation in robotic reinforcement learning, Proceedings of the fifth European Workshop on Reinforcement learning, pp. 1-3, 2001.
[detailed record] [bibtex]

1999

A. Arleo and W. Gerstner, A vision-driven model of hippocampal place cells and temporally asymmetric LTP-induction for action learning, ICANN'99 Artificial Neural Networks, pp. 132-137, 1999.
[detailed record] [bibtex]

W. Gerstner, Rapid signal transmission by a population of spiking neurons, ICANN'99 Artificial Neural Networks, Vol. 470, pp. 7-12, 1999.
[detailed record] [bibtex]

A. Herrmann and W. Gerstner, Understanding the PSTH response to synaptic input, ICANN'99 Artificial Neural Networks, Vol. 470, pp. 1012-1017, 1999.
[detailed record] [bibtex]

R. Kempter, W. Gerstner and J.L. van Hemmen, Spike-Based Compared to Rate-Based Hebbian Learning, Advances in Neural Information Processing Systems 11, pp. 125-131, 1999.
[detailed record] [bibtex]

1998

M. Spiridon, C. Chow and W. Gerstner, Frequency spectrum of coupled stochastic neurons with refractoriness, ICANN'98, pp. 337-342, 1998.
[detailed record] [bibtex]

1997

W. Gerstner, R. Kempter, J.L. van Hemmen and H. Wagner, A developmental learning rule for coincidence tuning in the barn owl auditory system, Computational Neuroscience: trends in research 1997, pp. 665-669, 1997.
[detailed record] [bibtex]

1996

R. Kempter, W. Gerstner, J.L. van Hemmen and H. Wagner, Temporal coding in the sub-millisecond range: Model of barn owl auditory pathway, Advances in Neural Information Processing Systems 8, pp. 124-130, 1996.
[detailed record] [bibtex]

1995

W. Gerstner, A. Schiegg, R. Ritz and J.L. van Hemmen, Long term potentiation in dendritic spines: a model study, Goettingen Neurobiology Report 1995; Proceedings of the 23rd Goettingen Neurobiology Conference 1995, Vol. 1, pp. 121, 1995.
[detailed record] [bibtex]

S. Wimbauer, W. Gerstner and J.L. van Hemmen, A developmental model of spatio-temporal receptive field properties for simple cells in the visual cortex, ICANN'95, Vol. xx, 1995.
[detailed record] [bibtex]

1994

W. Gerstner and J.L. van Hemmen, Coding and information processing in neural networks, Models of neural networks II, pp. 1-93, 1994.
[detailed record] [bibtex]

W. Gerstner and J.L. van Hemmen, How to describe neural activity -- spikes, rates, or assemblies?, Advances in Neural Information Processing Systems 6, pp. 463-470, 1994.
[detailed record] [bibtex]

R. Ritz, W. Gerstner and J.L. van Hemmen, Associative binding and segregation in a network of spiking neurons, Models of neural networks II, pp. 175-219, 1994.
[detailed record] [bibtex]

S. Wimbauer, W. Gerstner and J.L. van Hemmen, Motion detection in a Linsker network, ICANN'94, Proceedings of the International Conference on Artificial Neural Networks, Sorrento, Italy, 26-19 May 1994, pp. 1001-1004, 1994.
[detailed record] [bibtex]

1993

W. Gerstner and J.L. van Hemmen, Spikes or Rates? -- Stationary, oscillatory, and spatio-temporal states in an associative network of spiking neurons, ICANN'93, Proceedings of the International Conference on Artificial Neural Networks, Amsterdam, 13-16 September 1993, pp. 633-638, 1993.
[detailed record] [bibtex]

1991

W. Gerstner, Associative memory in a network of 'biological' neurons, Advances in Neural Information Processing Systems 3, pp. 84-90, 1991.
[detailed record] [bibtex]

Unknown year

J. Mayor and W. Gerstner, Online processing of multiple inputs in a sparsely-connected recurrent neural network, 13. ICANN / 10. ICONIP 2003.
[detailed record] [bibtex]

Other

2017

F. Zenke, W. Gerstner and S. Ganguli, The temporal paradox of Hebbian learning and homeostatic plasticity, Current Opinion In Neurobiology, Vol. 43, pp. 166-176, 2017.
[detailed record] [bibtex]

R. Duarte, A. Seeholzer, K. Zilles and A. Morrison, Synaptic patterning and the timescales of cortical dynamics, Current Opinion In Neurobiology, Vol. 43, pp. 156-165, 2017.
[detailed record] [bibtex]

F. Zenke and W. Gerstner, Hebbian plasticity requires compensatory processes on multiple timescales, Philosophical Transactions Of The Royal Society B-Biological Sciences, Vol. 372, Nr. 1715, pp. 20160259, 2017.
[detailed record] [bibtex]

T. Keck, T. Toyoizumi, L. Chen, B. Doiron, D.E. Feldman, K. Fox, W. Gerstner, P.G. Haydon, M. Huebener, H.-K. Lee, J.E. Lisman, T. Rose, F. Sengpiel, D. Stellwagen, M.P. Stryker, G.G. Turrigiano and M.C. Van Rossum, Integrating Hebbian and homeostatic plasticity: the current state of the field and future research directions, Philosophical Transactions Of The Royal Society B-Biological Sciences, Vol. 372, Nr. 1715, pp. 20160158, 2017.
[detailed record] [bibtex]

2013

T.P. Vogels, R.C. Froemke, N. Doyon, M. Gilson, J.S. Haas, R. Liu, A. Maffei, P. Miller, C.J. Wierenga, M.A. Woodin, F. Zenke and H. Sprekeler, Inhibitory synaptic plasticity: spike timing-dependence and putative network function, Frontiers In Neural Circuits, Vol. 7, 2013.
[detailed record] [bibtex]

Ph.D. Thesis

2016

M. Faraji, Learning with Surprise, Ph.D. Thesis, 2016.
[detailed record] [bibtex]

C. Stein Naves de Brito, Theory of representation learning in cortical neural networks, Ph.D. Thesis, 2016.
[detailed record] [bibtex]

2014

C.A. Pozzorini, Computational principles of single neuron adaptation, Ph.D. Thesis, 2014.
[detailed record] [bibtex]

S. Mensi, A new Mathematical Framework to Understand Single Neuron Computations, Ph.D. Thesis, 2014.
[detailed record] [bibtex]

F. Zenke, Memory formation and recall in recurrent spiking neural networks, Ph.D. Thesis, 2014.
[detailed record] [bibtex]

L. Ziegler, Synaptic Learning Rules with Consolidation, Ph.D. Thesis, 2014.
[detailed record] [bibtex]

J.E.F. Gerhard, Statistical models of effective connectivity in neural microcircuits, Ph.D. Thesis, 2014.
[detailed record] [bibtex]

2013

N. Frémaux, Models of Reward-Modulated Spike-Timing-Dependent Plasticity, Ph.D. Thesis, 2013.
[detailed record] [bibtex]

G. Hennequin, Stability and amplification in plastic cortical circuits, Ph.D. Thesis, 2013.
[detailed record] [bibtex]

2012

C. Tomm, Analysing Neuronal Network Architectures, Ph.D. Thesis, 2012.
[detailed record] [bibtex]

2011

R. Naud, The Dynamics of Adapting Neurons, Ph.D. Thesis, 2011.
[detailed record] [bibtex]

N. Marcille, Models of Evidence Integration in Rapid Decision Making Processes, Ph.D. Thesis, 2011.
[detailed record] [bibtex]

2009

C. Clopath, Synaptic plasticity across different time scales and its functional implications, Ph.D. Thesis, 2009.
[detailed record] [bibtex]

G. Luksys and C. Sandi, Stress, individual differences, and norepinephrine in reinforcement learning-based prediction of mouse behavior in conditioning and spatial learning, Ph.D. Thesis, 2009.
[detailed record] [bibtex]

L. Badel, Interpretation of neuronal response properties with simplified neuron models, Ph.D. Thesis, 2008.
[detailed record] [bibtex]

2007

D. Sheynikhovich, Spatial navigation in geometric mazes, Ph.D. Thesis, 2007.
[detailed record] [bibtex]

B. Bathellier, Analysis of information processing in the olfactory bulb by in vivo experiments and theoretical modelling, Ph.D. Thesis, 2007.
[detailed record] [bibtex]

2006

J.-P. Pfister, Theory of non-linear spike-time-dependent plasticity, Ph.D. Thesis, 2006.
[detailed record] [bibtex]

Book

2014

W. Gerstner, W.M. Kistler, R. Naud and L. Paninski, Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition, Cambridge University Press, 2014.
[detailed record] [bibtex]

2006

A.J. Ijspeert, J. Buchli, A. Selverston, M. Rabinovich, M. Hasler, W. Gerstner, A. Billard, H. Markram and D. Floreano, Dynamical principles for neuroscience and intelligent biomimetic devices, Lausanne : EPFL, 2006.
[detailed record] [bibtex]

2002

W. Gerstner and W.K. Kistler, Spiking Neuron Models, Cambridge University Press, 2002.
[detailed record] [bibtex]

1997

W. Gerstner, A. Germond, M. Hasler and J.-D. Nicoud, Lecture Notes in Computer Science - Artificial Neural Networks - ICANN '97, Berlin : Springer-Verlag GmbH, 1997.
[detailed record] [bibtex]

1993

W. Gerstner, Kodierung und Signalubertragung in Neuronalen Systemen: Assoziative Netzwerke mit stochastisch feuernden Neuronen, Harri--Deutsch Verlag, 1993.
[detailed record] [bibtex]

Book Chapter

2013

R. Naud and W. Gerstner, Can We Predict Every Spike?, in Spike Timing: Mechanisms and Function, Boca Raton : CRC Press, 2013.
[detailed record] [bibtex]

2001

W. Gerstner, A framework for spiking neuron models - the spike response model, in Handbook of Biological Physics, Elsevier, 2001.
[detailed record] [bibtex]

1999

R. Kempter, W. Gerstner, J.L. van Hemmen and H. Wagner, The quality of Coincidence detection and ITD-tuning: a theoretical framework, in Psychophysics, Physiology and Models of Hearing, World Scientific, Singapore, 1999.
[detailed record] [bibtex]

1998

W. Gerstner, Spiking Neurons, in Pulsed Neural Networks, MIT-Press, 1998.
[detailed record] [bibtex]

W. Gerstner, Populations of spiking neurons, in Pulsed Neural Networks, MIT-Press, 1998.
[detailed record] [bibtex]

W. Gerstner, R. Kempter and J.L. van Hemmen, Hebbian learning of Pulse timing in the Barn Owl auditory system, in Pulsed Neural Networks, MIT-Press, 1998.
[detailed record] [bibtex]

Technical Report

1995

W. Gerstner, A framework for spiking model neurons: The spike response method, Technical Report, 1995.
[detailed record] [bibtex]

Other

2016

F. Colombo, Learning and generation of slow sequences: an application to music composition, Presented at: Lemanic Neuroscience Annual Meeting 2016, Les Diablerets, Switzerland, September 2-3, 2016.
[detailed record] [bibtex]

F. Colombo, Algorithmic composition of melodies with deep recurrent neural networks, Presented at: Machine Learning Machine Learning Summer School 2016, Cadiz, Spain, May 11-21, 2016.
[detailed record] [bibtex]

M. Faraji, K. Preuschoff and W. Gerstner, Surprise-modulated belief update: how to learn within changing environments?, Presented at: Computational Neuroscience Meeting (CNS), Jeju Island, South Korea, July 2-7, 2016.
[detailed record] [bibtex]

M. Faraji, K. Preuschoff and W. Gerstner, A novel information theoretic measure of surprise, Presented at: International Conference on Mathematical Neuroscience (ICMNS), Antibes - Juan Les Pins, France, May 30 - June 1, 2016.
[detailed record] [bibtex]

M. Faraji, K. Preuschoff and W. Gerstner, Surprise-based learning: a novel measure of surprise with applications for learning within changing environments, Presented at: Computational and Systems Neuroscience (COSYNE), Salt Lake City, Utah, USA, February 25 - March 1, 2016.
[detailed record] [bibtex]

2015

M. Faraji, K. Preuschoff and W. Gerstner, Surprise minimization as a learning strategy in neural networks, Presented at: Computational Neuroscience Meeting (CNS), Prague, Czech Republic, July 18-23, 2015.
[detailed record] [bibtex]

M.P. Lehmann, A. Aivazidis, M. Faraji and K. Preuschoff, Bayesian filtering, parallel hypotheses and uncertainty: a new, combined model for human learning, Presented at: Computational and Systems Neuroscience (COSYNE), Salt Lake City, Utah, USA, March 5-10, 2015.
[detailed record] [bibtex]

D.S. Corneil and W. Gerstner, Rapid path planning and preplay in maze{like environments using attractor networks, Presented at: COSYNE 2015, Salt Lake City, March 5 to 10, 2015.
[detailed record] [bibtex]

F. Zenke, E. Agnes and W. Gerstner, Hebbian and non-Hebbian plasticity orchestrated to form and retrieve memories in spiking networks, Presented at: COSYNE 2015, Salt Lake City, March 5 to 10, 2015.
[detailed record] [bibtex]

M. Faraji, K. Preuschoff and W. Gerstner, Learning associations with a neurally-computed global novelty signal, Presented at: Computational and Systems Neuroscience (COSYNE), Salt Lake City, Utah, USA, March 5-10, 2015.
[detailed record] [bibtex]

H. Setareh, M. Deger and W. Gerstner, Synaptic efficacy tunes speed of activity propagation through chains of bistable neural assemblies, Presented at: COSYNE 2015, Salt Lake City, March 5 to 10, 2015.
[detailed record] [bibtex]

2014

M. Faraji, K. Preuschoff and W. Gerstner, A biologically plausible model of the learning rate dynamics, Presented at: Gordon Research Conference on Neurobiology of Cognition (GRC), Sunday River Resort - Newry, Maine, USA, July 20-25, 2014.
[detailed record] [bibtex]

M. Faraji, K. Preuschoff and W. Gerstner, Neuromodulation by surprise: a biologically plausible model of the learning rate dynamics, Presented at: Computational Neuroscience Meeting (CNS), Quebec City, Canada, July 26-31, 2014.
[detailed record] [bibtex]

H. Setareh, M. Deger and W. Gerstner, The role of interconnected hub neurons in cortical dynamics, Presented at: CNS 2014, Quebec City, Canada, July 26-31, 2014.
[detailed record] [bibtex]

W.F. Podlaski, A. Seeholzer, R. Rajnish and T. Vogels, Visualizing the similarity and pedigree of NEURON ion channel models available on ModelDB, Presented at: COSYNE 2014, Salt Lake City & Snowbird, Utah, USA, February 27 - March 4, 2014.
[detailed record] [bibtex]

D. Kastner, S.A. Baccus and T.O. Sharpee, Second Order Phase Transition Describes Maximally Informative Encoding in the Retina, Presented at: COSYNE 2014, Salt Lake City & Snowbird, Utah, USA, February 27 - March 4, 2014.
[detailed record] [bibtex]

M. Faraji, K. Preuschoff and W. Gerstner, Surprise-based learning: neuromodulation by surprise in multi-factor learning rules, Presented at: Computational and Systems Neuroscience (COSYNE), Salt Lake City, Utah, USA, February 27 - March 4, 2014.
[detailed record] [bibtex]

F. Zenke and E. Agnes, Learning Multi-Stability in Plastic Neural Networks, Presented at: COSYNE 2014, Salt Lake City & Snowbird, Utah, USA, February 27 - March 4, 2014.
[detailed record] [bibtex]

M. Deger, T. Schwalger and R. Naud, Network dynamics of spiking neurons with adaptation, Presented at: COSYNE 2014, Salt Lake City & Snowbird, Utah, USA, February 27 - March 4, 2014.
[detailed record] [bibtex]

D.S. Corneil, E. Neftci, G. Indiveri and M. Pfeiffer, Learning, Inference, and Replay of Hidden State Sequences in Recurrent Spiking Neural Networks, Presented at: COSYNE 2014, Salt Lake City & Snowbird, Utah, USA, February 27 - March 4, 2014.
[detailed record] [bibtex]

T. Schwalger, F. Droste and B. Lindner, Statistical structure of neural spiking under non-Poissonian stimulation, Presented at: COSYNE 2014, Salt Lake City & Snowbird, Utah, USA, February 27 - March 4, 2014.
[detailed record] [bibtex]

2013

C.A. Pozzorini, R. Naud and S. Mensi, Temporal decorrelation by power-law adaptation in pyramidal neurons, Presented at: COSYNE, Salt Lake City, USA, February 28 - March 3, 2013.
[detailed record] [bibtex]

S. Mensi, C.A. Pozzorini and O. Hagens, Evidence for a nonlinear coupling between firing threshold and subthreshold membrane potential, Presented at: COSYNE, Salt Lake City, USA, February 28 - March 3, 2013.
[detailed record] [bibtex]

2011

F. Gerhard, G. Pipa and W. Gerstner, Estimating small-world topology of neural networks from multi-electrode recordings, Presented at: USGEB Annual Meeting 2011, Zürich, Switzerland, January 27-28, 2011.
[detailed record] [bibtex]

F. Gerhard, G. Pipa and W. Gerstner, Estimation of small-world topology of cortical networks using Generalized Linear Models, Presented at: Goettingen Meeting of the German Neuroscience Society, Goettingen, Germany, March 23-27, 2011.
[detailed record] [bibtex]

2010

R.H. Haslinger, F. Gerhard and G. Pipa, Statistical tests for neural population models - The multivariate time rescaling theorem, Presented at: 2010 Neuroscience Meeting: Society for Neuroscience, San Diego, CA, USA, November 13-17, 2010.
[detailed record] [bibtex]

F. Gerhard, R. Haslinger and G. Pipa, Goodness-of-fit tests for neural population models: the multivariate time-rescaling theorem, Presented at: Nineteenth Annual Computational Neuroscience Meeting: CNS*2010, San Antonio, Texas, USA, July 24-30, 2010.
[detailed record] [bibtex]

R. Naud, F. Gerhard, S. Mensi and W. Gerstner, Improved Similarity Measures for Small Sets of Spike Trains, Presented at: Bernstein Conference on Computational Neuroscience, Berlin, Germany, September 27 - October 1, 2010 .
[detailed record] [bibtex]

2011

A.M. Clarke, H. Sprekeler, W. Gerstner and M.H. Herzog, Perceptual Learning, Roving and the Unsupervised Bias, 2011.
[detailed record] [bibtex]

2010

N. Marcille, J. Rüter, M.H. Herzog and W. Gerstner, A two stage model for perceptual decision making, 2010.
[detailed record] [bibtex]

F. Gerhard, G. Pipa and W. Gerstner, Estimating small-world topology of neural networks from multi-electrode recordings, 2010.
[detailed record] [bibtex]

2003

T. Strösslin and W. Gerstner, Reinforcement Learning in Continuous State and Action Space, 2003.
[detailed record] [bibtex]

L.F. Abbott and W. Gerstner, Homeostasis and Learning through Spike-Timing Dependent Plasticity, 2003.
[detailed record] [bibtex]

2017

M. Lehmann, H. Xu, V. Liakoni, M. Herzog, W. Gerstner and K. Preuschoff, Evidence for eligibility traces in human learning, 2017.
[detailed record] [bibtex]



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