G. Pipa

发表

Behavioral performance modulates spike field coherence in monkey prefrontal cortex

D. W. Wheeler, M. Munk, Wei Wu, 2008, Neuroreport.

Dynamical relaying can yield zero time lag neuronal synchrony despite long conduction delays

Leonardo L. Gollo, G. Pipa, Raul Vicente, 2008, Proceedings of the National Academy of Sciences.

Neural Synchrony in Cortical Networks: History, Concept and Current Status

W. Singer, G. Pipa, D. Nikolic, 2009, Front. Integr. Neurosci..

General Anesthesia Increases Temporal Precision and Decreases Power of the Brainstem Auditory-evoked Response-related Segments of the Electroencephalogram

G. Pipa, B. Scheller, M. Daunderer, 2009, Anesthesiology.

Performance- and Stimulus-Dependent Oscillations in Monkey Prefrontal Cortex During Short-Term Memory

W. Singer, J. Waltz, R. Goebel, 2009, Front. Integr. Neurosci..

Kinesthetic and vestibular information modulate alpha activity during spatial navigation: a mobile EEG study

Anna L. Gert, Benedikt V. Ehinger, P. König, 2014, Front. Hum. Neurosci..

Progress in Biophysics and Molecular Biology

W. Singer, G. Pipa, S. Neuenschwander, 1965 .

Effect of the Topology and Delayed Interactions in Neuronal Networks Synchronization

Gordon Pipa, Víctor M. Eguíluz, Claudio Mirasso, 2011, PloS one.

NeuroXidence : A Non-parametric Test on Excess or Deficiency of Joint-spike Events

D. W. Wheeler, W. Singer, G. Pipa, 2006 .

Coherence, Memory and Conditioning : A Modern Viewpoint

D. W. Wheeler, G. Pipa, R. Muresan, 2005 .

Moral Judgements on the Actions of Self-Driving Cars and Human Drivers in Dilemma Situations From Different Perspectives

P. König, G. Pipa, A. Stephan, 2019, Frontiers in Psychology.

How does the method change what we measure? Comparing virtual reality and text-based surveys for the assessment of moral decisions in traffic dilemmas

Benedikt V. Ehinger, P. König, G. Pipa, 2019, PLoS ONE.

Autonomous Vehicles Require Socio-Political Acceptance—An Empirical and Philosophical Perspective on the Problem of Moral Decision Making

P. König, G. Pipa, A. Stephan, 2018, Front. Behav. Neurosci..

Using Virtual Reality to Assess Ethical Decisions in Road Traffic Scenarios: Applicability of Value-of-Life-Based Models and Influences of Time Pressure

P. König, G. Pipa, L. R. Sütfeld, 2017, Front. Behav. Neurosci..

Homeostatic Plasticity for Single Node Delay-Coupled Reservoir Computing

Gordon Pipa, Hazem Toutounji, Johannes Schumacher, 2015, Neural Computation.

Forced-choice decision-making in modified trolley dilemma situations: a virtual reality and eye tracking study

Gordon Pipa, Alexander Skulmowski, Kai Kaspar, 2014, Front. Behav. Neurosci..

Neuronal oscillations form parietal/frontal networks during contour integration

Gordon Pipa, Marta Castellano, Raul Vicente, 2014, Front. Integr. Neurosci..

Context Matters: The Illusive Simplicity of Macaque V1 Receptive Fields

Emery N. Brown, Robert Haslinger, Gordon Pipa, 2012, PloS one.

Discrete Time Rescaling Theorem: Determining Goodness of Fit for Discrete Time Statistical Models of Neural Spiking

Emery N. Brown, Robert Haslinger, Gordon Pipa, 2010, Neural Computation.

Reliability and comparability of human brain structural covariance networks

Gordon Pipa, Marcus Kaiser, Yujiang Wang, 2019, NeuroImage.

Cross-frequency coupling : How much is that in real money ?

W. Singer, G. Pipa, M. Wibral, 2014 .

Dendritic plateau potentials can process spike sequences across multiple time-scales

G. Pipa, Pascal Nieters, Johannes Leugering, 2022, bioRxiv.

NeuroXidence: reliable and efficient analysis of an excess or deficiency of joint-spike events

D. W. Wheeler, W. Singer, G. Pipa, 2008, BMC Neuroscience.

Auto-structure of spike-trains matters for testing on syn- chronous activity: Take it serious, do not ignore or change it!

S. Grün, G. Pipa, C. Vreeswijk, 2007 .

Deep learning models for generation of precipitation maps based on numerical weather prediction

G. Pipa, M. Langguth, M. Wittenbrink, 2023, Geoscientific Model Development.

Moral Judgements on the Actions of Self-Driving Cars and Human Drivers in Dilemma Situations From Different Perspectives

P. König, G. Pipa, A. Stephan, 2019, Front. Psychol..

Biologically Inspired Deep Learning Model for Efficient Foveal-Peripheral Vision

Gordon Pipa, Peter König, Hristofor Lukanov, 2021, Frontiers in Computational Neuroscience.

Fast Concept Mapping: The Emergence of Human Abilities in Artificial Neural Networks when Learning Embodied and Self-Supervised pdf

Kai-Uwe Kühnberger, Gordon Pipa, Peter König, 2021, ArXiv.

Using transfer entropy to measure the patterns of information flow though cortex: application to MEG recordings from a visual Simon task

G. Pipa, J. Triesch, M. Wibral, 2009, BMC Neuroscience.

Spike-based statistical learning explains human performance in non-adjacent dependency learning tasks

G. Pipa, Jutta L. Mueller, Sophie Lehfeldt, 2022, Frontiers in Cognition.

The Neuronal code: development of tools and hypotheses for understanding the role of synchronization of neuronal activity

Gordon Pipa, G. Pipa, 2006 .

2007 Special Issue: Fading memory and time series prediction in recurrent networks with different forms of plasticity

Gordon Pipa, Jochen Triesch, Andreea Lazar, 2007 .

Human Decisions in Moral Dilemmas are Largely Described by Utilitarianism: Virtual Car Driving Study Provides Guidelines for Autonomous Driving Vehicles

Anja K. Faulhaber, G. Pipa, A. Stephan, 2018, Science and Engineering Ethics.

Human decisions in moral dilemmas are largely described by Utilitarianism: virtual car driving study provides guidelines for ADVs pdf

Gordon Pipa, Peter König, Anja K. Faulhaber, 2017, ArXiv.

RM-SORN: a reward-modulated self-organizing recurrent neural network

Gordon Pipa, Witali Aswolinskiy, G. Pipa, 2015, Front. Comput. Neurosci..

Investigating consciousness in the sleep laboratory – an interdisciplinary perspective on lucid dreaming

G. Pipa, M. Dresler, K. Appel, 2018 .

Mapping of Visual Receptive Fields by Tomographic Reconstruction

Emery N. Brown, Gordon Pipa, Zhe Chen, 2012, Neural Computation.

A Minimal Model of Neural Computation with Dendritic Plateau Potentials

G. Pipa, Pascal Nieters, Johannes Leugering, 2019 .

Event-based pattern detection in active dendrites

Gordon Pipa, Johannes Leugering, Pascal Nieters, 2019 .

Investigating consciousness in the sleep laboratory – an Investigating consciousness in the sleep laboratory – an

G. Pipa, M. Dresler, K. Appel, 2018 .

No effect of α‑GPC on lucid dream induction or dream content

G. Pipa, M. Schredl, K. Appel, 2017, Somnologie.

Assessing coupling dynamics from an ensemble of time series

Gordon Pipa, Miguel C. Soriano, Wei Wu, 2010, Entropy.

Transfer entropy—a model-free measure of effective connectivity for the neurosciences

Gordon Pipa, Michael Lindner, Raul Vicente, 2010, Journal of Computational Neuroscience.

Contour Integration and Synchronization in Neuronal Networks of the Visual Cortex

Gordon Pipa, Jordi García-Ojalvo, Raul Vicente, 2008, ICANN.

A Unifying Framework of Synaptic and Intrinsic Plasticity in Neural Populations

Gordon Pipa, Johannes Leugering, G. Pipa, 2018, Neural Computation.

How does the method change what we measure? Comparing virtual reality and text-based surveys for the assessment of moral decisions in traffic dilemmas

Benedikt V. Ehinger, P. König, G. Pipa, 2019, PloS one.

Cortical Spike Synchrony as a Measure of Input Familiarity

Gordon Pipa, Jordi García-Ojalvo, Clemens Korndörfer, 2017, Neural Computation.

Importance of electrophysiological signal features assessed by classification trees

Gordon Pipa, Raul Cristian Muresan, Matthias H. J. Munk, 2007, Neurocomputing.

Human Decisions in Moral Dilemmas are Largely Described by Utilitarianism: Virtual Car Driving Study Provides Guidelines for Autonomous Driving Vehicles

Gordon Pipa, Peter König, Anja K. Faulhaber, 2018, Science and Engineering Ethics.

Response: Commentary: Using Virtual Reality to Assess Ethical Decisions in Road Traffic Scenarios: Applicability of Value-of-Life-Based Models and Influences of Time Pressure

Peter König, Gordon Pipa, Richard Gast, 2018, Front. Behav. Neurosci..

Is Deep-Learning and Natural Language Processing Transcending the Financial Forecasting? Investigation Through Lens of News Analytic Process

Gordon Pipa, Faisal Khalil, G. Pipa, 2021, Computational Economics.

Transforming the generative pretrained transformer into augmented business text writer

G. Pipa, Faisal Khalil, 2021, Journal of Big Data.

A trajectory-based loss function to learn missing terms in bifurcating dynamical systems

G. Pipa, Pascal Nieters, R. Vortmeyer-Kley, 2021, Scientific Reports.

Real-time dialogue between experimenters and dreamers during REM sleep

G. Pipa, K. Paller, M. Dresler, 2020, Current Biology.

A Statistical Framework to Infer Delay and Direction of Information Flow from Measurements of Complex Systems

Frank Jäkel, Gordon Pipa, Pascal Fries, 2015, Neural Computation.

Single-Unit Recordings Revisited: Activity in Recurrent Microcircuits

Gordon Pipa, Raul Cristian Muresan, Diek W. Wheeler, 2005, ICANN.

SORN: A Self-Organizing Recurrent Neural Network

Gordon Pipa, Jochen Triesch, Andreea Lazar, 2009, Front. Comput. Neurosci..

Combining Deep Learning and (Structural) Feature-Based Classification Methods for Copyright-Protected PDF Documents

Kai-Uwe Kühnberger, Gordon Pipa, Tobias Thelen, 2019, ICANN.

Predicting epileptic seizures using nonnegative matrix factorization

Gordon Pipa, Olivera Stojanovic, G. Pipa, 2019, PloS one.

Learning sparse and meaningful representations through embodiment

Peter König, Gordon Pipa, Viviane Clay, 2020, Neural Networks.

Spatiotemporal Computations of an Excitable and Plastic Brain: Neuronal Plasticity Leads to Noise-Robust and Noise-Constructive Computations

Gordon Pipa, Hazem Toutounji, G. Pipa, 2014, PLoS Comput. Biol..

Encoding Through Patterns: Regression Tree–Based Neuronal Population Models

Emery N. Brown, Robert Haslinger, Gordon Pipa, 2013, Neural Computation.

Bivariate and Multivariate NeuroXidence: A Robust and Reliable Method to Detect Modulations of Spike–Spike Synchronization Across Experimental Conditions

Gordon Pipa, Wei Wu, Diek W. Wheeler, 2011, Front. Neuroinform..

Neuromorphic computation in multi-delay coupled models

Gordon Pipa, Johannes Leugering, Pascal Nieters, 2017, IBM J. Res. Dev..

Westdrive X LoopAR: An Open-Access Virtual Reality Project in Unity for Evaluating User Interaction Methods during Takeover Requests

Gordon Pipa, Peter König, Farbod N. Nezami, 2021, Sensors.

Statistical modeling approach for detecting generalized synchronization.

Robert Haslinger, Gordon Pipa, Johannes Schumacher, 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

Application of Parallel Factor Analysis (PARAFAC) to electrophysiological data

Anja Klein, Gordon Pipa, Sarah Katharina Schmitz, 2015, Front. Neuroinform..

Extraction of Network Topology From Multi-Electrode Recordings: Is there a Small-World Effect?

Wulfram Gerstner, Gordon Pipa, Felipe Gerhard, 2011, Front. Comput. Neurosci..

Persistent Memory in Single Node Delay-Coupled Reservoir Computing

Gordon Pipa, Hazem Toutounji, André David Kovac, 2016, PloS one.

LOW HEMOGLOBIN LEVELS DURING NORMOVOLEMIA ARE ASSOCIATED WITH ELECTROCARDIOGRAPHIC CHANGES IN PIGS

G. Pipa, B. Scheller, O. Habler, 2011, Shock.

Far in Space and Yet in Synchrony: Neuronal Mechanisms for Zero-Lag Long-Range Synchronization

Gordon Pipa, Leonardo L. Gollo, Claudio R. Mirasso, 2009 .

Functional Properties of Circuits, Cellular Populations, and Areas

Gordon Pipa, Wolf Singer, John H. Reynolds, 2019, The Neocortex.

Higher Order Spike Synchrony in Prefrontal Cortex during Visual Memory

Gordon Pipa, Matthias H. J. Munk, M. Munk, 2009, Front. Comput. Neurosci..

Encoding and Decoding Dynamic Sensory Signals with Recurrent Neural Networks: An Application of Conceptors to Birdsongs

Gordon Pipa, Richard Gast, Brian Lewis, 2017, bioRxiv.

Discrete Time Rescaling Theorem: Determining Goodness of Fit for Statistical Models of Neural Spiking

G. Pipa, E. Brown, R. Haslinger, 2010 .

2D:4D and spatial abilities: From rats to humans

G. Pipa, M. Czisch, M. Dresler, 2017, Neurobiology of Learning and Memory.

Missing mass approximations for the partition function of stimulus driven Ising models

Robert Haslinger, Gordon Pipa, Ziv Williams, 2013, Front. Comput. Neurosci..

Neural Synchrony in Cortical Networks: History, Concept and Current Status

W. Singer, G. Pipa, S. Neuenschwander, 2009, Front. Integr. Neurosci..

Serial Spike Time Correlations Affect Probability Distribution of Joint Spike Events

Gordon Pipa, Carl van Vreeswijk, Mina Shahi, 2016, Front. Comput. Neurosci..

Spike Train Auto-Structure Impacts Post-Synaptic Firing and Timing-Based Plasticity

Gordon Pipa, Raul Vicente, Bertram Scheller, 2011, Front. Comput. Neurosci..

NeuroXidence: reliable and efficient analysis of an excess or deficiency of joint-spike events

Gordon Pipa, Danko Nikolić, Wolf Singer, 2009, Journal of Computational Neuroscience.

Validation of task-related excess of spike coincidences based on NeuroXidence

Sonja Grün, Gordon Pipa, Alexa Riehle, 2007, Neurocomputing.

Achieving synchronization of networks by an auxiliary hub

Gordon Pipa, D. Huang, D. Huang, 2007 .

Adaptive Blending Units: Trainable Activation Functions for Deep Neural Networks

Gordon Pipa, Holger Finger, Leon René Sütfeld, 2018, SAI.

A color-based visualization technique for multielectrode spike trains.

Gordon Pipa, Danko Nikolić, Wolf Singer, 2009, Journal of neurophysiology.

Real-time dialog between experimenters and dreamers during REM sleep

G. Pipa, K. Paller, M. Dresler, 2021 .

Introducing a New Mobile Electroencephalography System and Evaluating Its Quality in Comparison to Clinical Electroencephalography

G. Pipa, Laura Krieger, Paria Samimisabet, 2023, Sensors.

Impact of Spike Train Autostructure on Probability Distribution of Joint Spike Events

Sonja Grün, Gordon Pipa, Carl van Vreeswijk, 2013, Neural Computation.

Preoperative interleukin-22 values add valuable information for outcome prediction following orthotopic liver transplantation: a preliminary study.

G. Pipa, B. Scheller, W. Bechstein, 2014, Annals of transplantation.

Applying the Multivariate Time-Rescaling Theorem to Neural Population Models

Robert Haslinger, Gordon Pipa, Felipe Gerhard, 2011, Neural Computation.

Encoding Through Patterns: Regression Tree–Based Neuronal Population Models

Laura D. Lewis, Ziv M. Williams, G. Pipa, 2013, Neural Computation.

Goodness-of-fit tests for neural population models: the multivariate time-rescaling theorem

Robert Haslinger, Gordon Pipa, Felipe Gerhard, 2010, BMC Neuroscience.

Encoding through Patterns: Regression Tree–based Neuronal Population Models Citation Haslinger, Robert Et Al. " Encoding through Patterns: Regression Tree–based Neuronal Population Models. " Neural Computation Accessed Terms of Use Detailed Terms Encoding through Patterns: Regression Tree–based Neur

G. Pipa, R. Haslinger, Laura D Lewis, 2022 .

Bayesian hierarchical models can infer interpretable predictions of leaf area index from heterogeneous datasets

G. Pipa, T. Jarmer, B. Siegmann, 2021, bioRxiv.

Discrete Time Rescaling Theorem: Determining Goodness of Fit for Statistical Models of Neural Spiking

G. Pipa, E. Brown, R. Haslinger, 2010 .

An Introduction to Delay-Coupled Reservoir Computing

Gordon Pipa, Johannes Schumacher, Hazem Toutounji, 2015 .

EEG under anesthesia - Feature extraction with TESPAR

Gordon Pipa, Raul Cristian Muresan, Vasile V. Moca, 2009, Comput. Methods Programs Biomed..

An Analytical Approach to Single Node Delay-Coupled Reservoir Computing

Gordon Pipa, Hazem Toutounji, Johannes Schumacher, 2013, ICANN.

Discrete Time Rescaling Theorem: Determining Goodness of Fit for Discrete Time Statistical Models of Neural Spiking

G. Pipa, E. Brown, R. Haslinger, 2010, Neural Computation.

A Bayesian Monte Carlo approach for predicting the spread of infectious diseases

Gordon Pipa, Alexander Ullrich, Stéphane Ghozzi, 2019, bioRxiv.

Learning in the Single-Cell Organism Physarum polycephalum: Effect of Propofol

G. Pipa, S. Kippenberger, N. Zöller, 2023, International journal of molecular sciences.

Global Approach to Brain Activity: from Cognition to Disease

S. Boccaletti, W. Singer, G. Pipa, 2010 .

Non-parametric significance estimation of joint-spike events by shuffling and resampling

Sonja Grün, Gordon Pipa, S. Grün, 2003, Neurocomputing.

Significance of joint-spike events based on trial-shuffling by efficient combinatorial methods

Sonja Grün, Gordon Pipa, Markus Diesmann, 2003, Complex..

Non-parametric signicance estimation ofjoint-spike events by shu&ing and resampling

Gordon Pipa, Sonja Gr, G. Pipa, 2003 .

The public perception of lucid dreaming and its research

G. Pipa, M. Schredl, K. Appel, 2018 .

Human decisions in moral dilemmas are largely described by Utilitarianism: virtual car driving study provides guidelines for ADVs

Anja K. Faulhaber, P. König, G. Pipa, 2017, ArXiv.

Project Westdrive: Unity City With Self-Driving Cars and Pedestrians for Virtual Reality Studies

Peter König, Gordon Pipa, Farbod N. Nezami, 2020, Frontiers in ICT.