Publication list

Video Presentations:

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Book chapters

  • 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:

 

2013

  • (50) 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; upcoming in 2013

  • (49) 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

  • (48) 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

2012

  • (47) 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

  • (46) Schumacher, J., Haslinger, R. and Pipa, G.
    ’Statistical modeling approach for detecting generalized synchronization.’, Phys. Rev. E 85, 056215, 2012

  • (45) 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

  • (44) 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

  • (43) 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

  • (42) Pipa, G. and Munk, M.H.J.
    ’Higher Order Spike Synchrony in Prefrontal Cortex during Visual Memory.’,PloS one, vol. 6, no. 1, 2011

  • (40) 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

  • (39) 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

  • (38) 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

  • (37) 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

  • (36) 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

  • (35) 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

  • (34) 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 Ecg Changes in Pigs.’, Shock, ISSN 1073-2322, 2011

2010

  • (33) 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

  • (32) Gerhard, F. and Haslinger, R. and Pipa, G.
    ’Goodness-of-fit tests for neural population models: the multivariate time-rescaling theorem.’, BMC Neuroscience, vol. 11, suppl. 10, p. 46, BioMed Central Ltd, 2010

  • (31) 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

  • (30) 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

  • (29) Gerhard, Pipa, G., und Gerstner, W.
    ’Estimating small-world topology of neural networks from multi-electrode recordings.’, Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience. doi: 10.3389/conf.fncom.2010.51.00088

2009


2009

 

 

2008

    (18) 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) 

    (17) 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) 

    (16) E. Ullner, R. Vicente, G. Pipa, J. Garcıa-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) 

    (15) A. Lazar, G. Pipa, J. Triesch

    ‘Predictive Coding in Cortical Microcircuits’, Lecture Notes in Computer Science 5164, 386-395, Springer 2008, Artificial Neural Networks

    (14) 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)

    (13) 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

    (12) R. Vicente, G. Pipa, I. Fischer, C. Mirasso

    ‘Zero-lag Long Range Synchronization of Neurons Is Enhanced by Dynamical Relaying’
    ICANN2007 (download) 

    (11) 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)

    (10) Lazar, A., 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)

 

2006

    (9) G. Pipa, A. Riehle, S. Grün

    ‘Validation of task-related excess of spike coincidences based on NeuroXidence’
    Neurocomputing (2006), doi:10.1016/j.neucom.2006.10.142 (download)

    (8) 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)

    (7) Lazar A., Muresan R.C., Stadler E., Munk M., Pipa G. (2006)

    ‘Importance of electrophysiological signal features assessed by classification trees’
    Neurocomputing (2006), doi:10.1016/j.neucom.2006.10.136 (download)

    (6) 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 (download)

 

2005

    (5) 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)

    (4) Muresan, R.C., G. Pipa, R.V. Florian and D.W. Wheeler (2005)‘Coherence, Memory and Conditioning. A Modern Viewpoint’ Neural Information Processing - Letters and Reviews, Vol. 7, No. 2, pp. 19-28 (download)

 

2003

    (3) 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)

    (2) 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)

    (1) G.Pipa Diploma thesis‘Entwicklung und Untersuchung einer nicht-parametrischen Methode zur Schätzung der Signifikanz zeitlich koordinierter Spike-Aktivät’, (download)

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