Neuroinformatics - Publications

Video Presentations

Book Chapters

  • Schumacher, J., Toutounji, H., & Pipa, G.
    ’An Introduction to Delay-Coupled Reservoir Computing.’,
    in ’Artificial Neural Networks (pp. 63-90)’, Springer International Publishing (2015).
  • Pipa, G.
    ’Theoretical Neuroscience’, in ’Handbuch Kognitionswissenschaft (pp. 85-89)’, J.B. Metzler Verlag (2013).
  • Boccaletti, S., Cantero, J. L., Chávez, M., Egiazarian, K., Fischer, I., Gómez-Herrero, G., Mirasso, C., Pipa, G., Singer, W., Villa, A.E.P., and García-Ojalvo, J.
    ’Global Approach to Brain Activity: from Cognition to Disease.’,
    ’Success Stories of the Advances and Applications of Complex Systems Science.’, Springer 2010.
  • E. Balaban, S. Edelman, S. Grillner, U. Grodzinski, E. D. Jarvis, J. H. Kaas, G. Laurent, and G. Pipa
    ’Dynamic Coordination in the Brain - Evolution of Dynamic Coordination’., Ernst Strüngmann Forum, MIT press, 2010, ISBN 0262014718.
  • R. Vicente, L. L. Gollo, C. R. Mirasso, I. Fischer, and G. Pipa
    ’Far in space and yet in synchrony: neuronal mechanisms for zero-lag long-range synchronization. Coherent Behavior in Neuronal Networks.’,
    Springer Series in Computational Neuroscience, Vol 3, 2009.

Publications

2017

2016

2015

  • Schumacher, J. & Pipa, G. ’A statistical framework to infer delay and direction of information flow from measurements of complex systems' in Neural Computation (2015)
  • Gomez-Herrero, G., Wu, W., Rutanen, K., Cornelles Soriano, M., Pipa, G. & Vicente, R.
    ’Assessing coupling dynamics from an ensemble of time series' in Entropy (in press, 2015)
  • Toutounji, H., Schumacher, J. & Pipa, G.
    ’Homeostatic Plasticity for Single Node Delay Coupled Reservoir Computing' in Neural Computation (accepted 2015)
  • Aswolinskiy, W. & Pipa, G.
    ’RM-SORN: A Reward-Modulated Self-Organizing Recurrent Neural Network' in Frontiers in Computational Neuroscience, 9, 36 (2015)
  • Aru, J., Aru, J., Priesemann, V., Wibral, M., Lana, L., Pipa, G., ... & Vicente, R.
    ’Untangling cross-frequency coupling in neuroscience' in Current opinion in neurobiology, 31, 51-61 (2015)

2014

  • Toutounji, H., & Pipa, G.
    ’Spatiotemporal computations of an excitable and plastic brain: neuronal plasticity leads to noise-robust and noise-constructive computations'; PLoS computational biology 10.3 (2014): e1003512
  • Toutounji, H., & Pasemann, F.
    ’Behavior control in the sensorimotor loop with short-term synaptic dynamics induced by self-regulating neurons'; Frontiers in Neurorobotics 8 (2014): 19
  • Castellano, M., Plöchl, M., Vicente, R., & Pipa, G.
    ’Neuronal oscillations during contour integration of dynamic visual stimuli form parietal/frontal networks'; Frontiers in Integrative Neuroscience, 8, 64 (2014)
  • Waizel, M., Franke, F., Pipa, G., Chen, N. H., Muckli, L., Munk, M. H. J., ... & Munk, M. J. H. ’Neuronal coding challenged by memory load in prefrontal cortex'; Frontiers in Computational Neuroscience, 37(2014)
  • Ehinger, B. V., Fischer, P., Gert, A. L., Kaufhold, L., Weber, F., Pipa, G., & König, P.
    ’Kinesthetic and Vestibular Information Modulate Alpha Activity during Spatial Navigation: A Mobile EEG Study'; Frontiers in Human Neuroscience 8 (2014): 71

2013

  • Haslinger, R., Ba, D., Galuske, R. Williams, Z., & Pipa, G.
    ’Missing Mass Approximations for the Partition of Stimulus-Driven Ising Models.’, Frontiers in Computational Neuroscience; 2013
  • Schumacher, J., Toutounji, H., & Pipa, G.
    ’An Analytical Approach to Single Node Delay-Coupled Reservoir Computing.’ Artificial Neural Networks and Machine Learning–ICANN 2013 (pp. 26-33). Springer Berlin Heidelberg, 2013
  • Pipa, G., Grün, S., & van Vreeswijk, C.
    ’Impact of Spike Train Autostructure on Probability Distribution of Joint Spike Events.’, Neural Computation; 25.5, pp. 1123-1163, 2013
  • Haslinger, R., Pipa, G., Lewis, L., Nikolić, D., Williams, Z., & Brown, E.
    ’Encoding Through Patterns: Regression Tree–Based Neuronal Population Models.’, Neural Computation; pp. 1-41, 2013
  • Castellano, M., & Pipa, G.
    ’Memory Trace in Spiking Neural Networks’, In Artificial Neural Networks and Machine Learning–ICANN 2013 (pp. 264-271). Springer Berlin Heidelberg. 2013

2012

  • Haslinger, R., Pipa, G., Lima, B., Singer, W., Brown, E. N., & Neuenschwander, S.
    ’Context Matters: The Illusive Simplicity of Macaque V1 Receptive Fields.’, PloS one, 7(7), e39699, 2012
  • Schumacher, J., Haslinger, R. and Pipa, G.
    ’Statistical modeling approach for detecting generalized synchronization.’, Phys. Rev. E 85, 056215, 2012
  • Pipa, G., Chen, Z., Neuenschwander, S., Lima, B., Brown, E. N.
    ’Mapping of Visual Receptive Fields by Tomographic Reconstruction.’, Journal of Neural Computation; 24(10), 2012
  • Toutounji, H., Schumacher, J. & Pipa, G.
    ’Optimized Temporal Multiplexing for Reservoir Computing with a Single Delay-Coupled Node.’, 2012 International Symposium on Nonlinear Theory and its Applications (NOLTA 2012)

2011

  • Pérez, T. and Garcia, G. C. and Eguíluz, V. M. and Vicente, R. and Pipa, G. and Mirasso, C.
    ’Effect of the Topology and Delayed Interactions in Neuronal Networks Synchronization.’,
    Frontiers in Computational Neuroscience, vol. 5, Frontiers Media SA, 2011
  • Pipa, G. and Munk, M. H. J.
    ’Higher Order Spike Synchrony in Prefrontal Cortex during Visual Memory.’,
    PloS one, vol. 6, no. 1, 2011
  • Lazar, A. and Pipa, G. and Triesch, J.
    ’Emerging Bayesian Priors in a Self-Organizing Recurrent Network.’,
    Artificial Neural Networks and Machine Learning--ICANN 2011, pp. 127-134, Springer, 2011
  • Wu, W. and Wheeler, D. W. and Pipa, G.
    ’Frontiers: Bivariate and Multivariate NeuroXidence: A Robust and Reliable Method to Detect Modulations of Spike-Spike Synchronization Across Experimental Conditions.’,
    Frontiers in Neuroinformatics, vol. 5, 2011
  • Gerhard, F. and Haslinger, R. and Pipa, G.
    ’Applying the multivariate time-rescaling theorem to neural population models.’,
    Neural Computation, pp. 1-32, MIT Press, 2011
  • Gerhard, F., Pipa, G., Lima, B., Neuenschwander, S., and Gerstner, W.
    ’Extraction of network topology from multi-electrode recordings: Is there a small-world effect?’,
    Frontiers in Neuroscience, 2011
  • Ulhaas, P., Pipa, G., Neuenschwander, S., and Singer, W.
    ’A new look at gamma? High- (>60 Hz) γ-band activity in cortical networks: Functions, mechanisms and impairment.’,
    Progress in Biophysics and Molecular Biology, 105:1-2, 2011
  • Vicente, R., Wibral, M., Lindner, M. & Pipa, G.
    ’Transfer Entropy - A model-free measure of effective connectivity for the neurosciences.’,
    Journal of Computational Neuroscience, 2011
  • Scheller, B. and Pipa, G. and Kertscho, H. and Lauscher, P. and Ehrlich, J. and Habler, O. and Zacharowski, K. and Meier, J. and Kai, T.S.
    ’Low Hemoglobin Levels During Normovolemia Are Associated with Electrocardiographic Changes in Pigs.’,
    Shock, ISSN 1073-2322, 2011
  • Toutounji, H., Rothkopf, C. A. and Triesch, J.
    ’Scalable Reinforcement Learning through Hierarchical Decompositions for Weakly-Coupled Problems.’,
    ICDL-EpiRob, 2011
  • Scheller, B., Castellano, M., Vicente, R., & Pipa, G.
    ’Spike train auto-structure impacts post-synaptic firing and timing-based plasticity’,
    Frontiers in computational neuroscience, 5, 60-60. 2011

2010

  • Haslinger, R. and Pipa, G. and Brown, E.
    ’Discrete time rescaling theorem: Determining goodness of fit for discrete time statistical models of neural spiking.’,
    Neural Computation, vol. 22, no. 10, pp 2477-2506, ISSN 0899-7667, MIT Press, 2010
  • Gomez-Herrero, G. and Wu, W. and Rutanen, K. and Soriano, M. C. and Pipa, G. and Vicente, R.
    ’Assessing coupling dynamics from an ensemble of time series.’,
    Arxiv preprint arXiv:1008.0539, 2010
  • Evert, S. and Pipa, G.
    ’Probability Estimation of Rare Events in Linguistics and Computational Neuroscience .’,
    Proceedings of KogWis 2010: 10th Biannual Meeting of the German Society for Cognitive Science, 2010

2009

2008

  • R. Vicente, L. L. Golo, C. R. Mirasso, I. Fischer, G. Pipa
    ‘Dynamical relaying can yield zero time lag neuronal synchrony despite long conduction delays’,
    PNAS, November 4, 2008, vol. 105, no. 44, 17157–17162
    (download)
  • G. Pipa, R. Vicente, A. Tikhonov
    ‘Auto-structure of presynaptic activity defines postsynaptic firing statistics and can modulate STDP-based structure formation and learning’,
    Lecture Notes in Computer Science 5164, 413-422, Springer 2008, Artificial Neural Networks
    (download)
  • E. Ullner, R. Vicente, G. Pipa, J. Garcia-Ojalvo
    ‘Contour integration and synchronization in neuronal networks of the visual cortex’,
    Lecture Notes in Computer Science 5164, 703-712, Springer 2008, Artificial Neural Networks
    (download)
  • A. Lazar, G. Pipa, J. Triesch
    ‘Predictive Coding in Cortical Microcircuits’,
    Lecture Notes in Computer Science 5164, 386-395, Springer 2008, Artificial Neural Networks
  • Wu, W., Wheeler, D. W. , Staedler, E. S., Munk, M. H. J., Pipa, G.
    ’Behavioral performance modulates spike-field-coherence in monkey prefrontal cortex’,
    Neuro Report, Vol 19 No, 2008, 235-238,
    (download)
  • G. Pipa, D. W. Wheeler, W. Singer, D. Nikolic
    ’NeuroXidence: reliable and efficient analysis of an excess ordeficiency of joint-spike events’,
    J.Comput.Neurosci. PM:18219568, DOI 10.1007/s10827-007-0065-3.
    (download, download source code and example data)

2007

  • R. Vicente, G. Pipa, I. Fischer, C. Mirasso
    ‘Zero-Lag Long Range Synchronization of Neurons Is Enhanced by Dynamical Relaying’,
    ICANN2007
    (download)
  • Huang, D. and G. Pipa
    ‘Achieving synchronization of networks by an auxiliary hub’,
    Europhys. Lett. 77 5 (2007) 50010
    doi: 10.1209/0295-5075/77/50010
    (download)
  • A. Lazar, G. Pipa and J. Triesch (first and second author contributed equally)
    ‘Fading Memory and Time Series Prediction in Recurrent Networks with Different Forms of Plasticity’,
    Neural Networks, Volume 20, Issue 3, April 2007, Pages 312-322
    (download)
  • S. Timmer and M. Riedmiller
    ‘Fitted Q Iteration with CMACs’,
    Proceedings of the International Symposium on Approximate Dynamic Programming and Reinforcement Learning (ADPRL), Honolulu, USA, April 2007
  • S. Timmer and M. Riedmiller
    ‘Safe Q-Learning on Complete History Spaces’,
    Proceedings of the 18th European Conference on Machine Learning (ECML), Warsaw, September 2007
  • G. Pipa, A. Riehle, S. Grün
    ‘Validation of task-related excess of spike coincidences based on NeuroXidence’,
    Neurocomputing (2006), doi:10.1016/j.neucom.2007.10.142
    (download)
  • Lazar A., Muresan R.C., Stadler E., Munk M., Pipa G.
    ‘Importance of electrophysiological signal features assessed by classification trees’,
    Neurocomputing (2007), doi:10.1016/j.neucom.2006.10.136
    (download)

2006

  • G. Pipa
    Ph.D. thesis
    ‘Neuronal Code: Development of tools and hypotheses for understanding the role of synchronisation of neuronal activity’
    (download Ph.D. thesis)
  • Lazar A., Pipa G., Triesch J.
    ‘The combination of STDP and intrinsic plasticity yields complex dynamics in recurrent spiking networks’,
    Proceedings of the European Symposium on Artificial Neural Networks (ESANN 2006), Brugges, Belgium
  • S.Timmer and M.Riedmiller
    ‘Abstract State Spaces with History’,
    Proceedings of the 25th International Conference of NAFIPS, the North American Fuzzy Information Processing Society, Montreal, Canada, June 2006

2005

  • R. C. Muresan, G. Pipa, D. W. Wheeler
    ’Single-Unit Recordings Revisited: Activity in Recurrent Microcircuits’,
    Lecture Notes in Computer Science, Vol. 3696, Eds. W. Duch, J. Kacprzyk, E. Oja, et al., pp.153-160
    ISSN:E0302-9743, Muresan ICANN 2005
    (download)
  • Muresan, R. C., G. Pipa, R. V. Florian and D. W. Wheeler
    Coherence, Memory and Conditioning. A Modern Viewpoint’,
    Neural Information Processing - Letters and Reviews, Vol. 7, No. 2, pp. 19-28
    (download)
  • S. Timmer and M. Riedmiller
    ‘Learning policies for abstract states’,
    Proceedings of the International Conference on Systems, Man and Cybernetics, 2005, Big Island, USA, October 2005

2003

  • G. Pipa and S. Grün
    ‘Non-Parametric significance estimation of joint-spike events by shuffling and resampling’
    Neural Computing, Neurocomputing 52–54 (2003) 31 – 37
    (download)
  • G. Pipa, M. Diesmann, S. Grün
    ’Significance of Joint-Spike Events Based on Trial-Shuffling by Efficient, Combinatorial Methods’
    Complexity 2003, Pages 79 – 86
    (download)
  • G.Pipa
    Diploma thesis
    ‘Entwicklung und Untersuchung einer nicht-parametrischen Methode zur Schätzung der Signifikanz zeitlich koordinierter Spike-Aktivität’
    (download)