| Erscheinungsdatum | Titel | Autor(en) |
| 2016 | Automated analysis of actimetry used for the detection of disease phenotypes in sleep medicine | Leenings, R.; Glatz, C.; Boentert, M.; Heidbreder, A.; Pipa, G. ; Young, P. |
| 2018 | Autonomous Vehicles Require Socio-Political Acceptance-An Empirical and Philosophical Perspective on the Problem of Moral Decision Making | Bergmann, Lasse T.; Schlicht, Larissa; Meixner, Carmen; Koenig, Peter ; Pipa, Gordon ; Boshammer, Susanne ; Stephan, Achim |
| 2021 | Bayesian hierarchical models can infer interpretable predictions of leaf area index from heterogeneous datasets | Stojanović, Olivera; Siegmann, Bastian; Jarmer, Thomas ; Pipa, Gordon ; Leugering, Johannes |
| 2021 | Biologically Inspired Deep Learning Model for Efficient Foveal-Peripheral Vision | Lukanov, Hristofor; Koenig, Peter ; Pipa, Gordon |
| 2019 | Bistable Perception in Conceptor Networks | Meyer zu Driehausen, F.; Busche, R.; Leugering, J.; Pipa, G. |
| 2017 | Classifying bio-inspired model of point-light human motion using Echo State networks | Tanisaro, P.; Lehman, C.; Sütfeld, L.; Pipa, G. ; Heidemann, G. |
| 2019 | Combining Deep Learning and (Structural) Feature-Based Classification Methods for Copyright-Protected PDF Documents | Garita Figueiredo, R.; Kühnberger, K.-U. ; Pipa, G. ; Thelen, T. |
| 2012 | Context Matters: The Illusive Simplicity of Macaque V1 Receptive Fields | Haslinger, Robert; Pipa, Gordon ; Lima, Bruss; Singer, Wolf; Brown, Emery N.; Neuenschwander, Sergio |
| 2017 | Cortical Spike Synchrony as a Measure of Input Familiarity | Korndoerfer, Clemens; Ullner, Ekkehard; Garcia-Ojalvo, Jordi; Pipa, Gordon |
| 2017 | Cortical Spike Synchrony as a Measure of Input Familiarity | Korndörfer, Clemens; Ullner, Ekkehard; Garcia-Ojalvo, Jordi; Pipa, Gordon |
| 2017 | Cortical Spike Synchrony as a Measure of Input Familiarity. | Korndörfer, Clemens; Ullner, Ekkehard; Garcia-Ojalvo, Jordi; Pipa, Gordon |
| 2023 | Deep learning models for generation of precipitation maps based on numerical weather prediction | Rojas-Campos, Adrian; Langguth, Michael; Wittenbrink, Martin; Pipa, Gordon |
| 2023 | Dendritic plateau potentials can process spike sequences across multiple time-scales | Leugering, Johannes; Nieters, Pascal ; Pipa, Gordon |
| 2023 | Development of Few-Shot Learning Capabilities in Artificial Neural Networks When Learning Through Self-Supervised Interaction | Clay, Viviane; Pipa, Gordon ; Kuhnberger, Kai-Uwe ; Konig, Peter |
| 2011 | Effect of the Topology and Delayed Interactions in Neuronal Networks Synchronization | Perez, Toni; Garcia, Guadalupe C.; Eguiluz, Victor M.; Vicente, Raul; Pipa, Gordon ; Mirasso, Claudio |
| 2011 | Emerging Bayesian priors in a self-organizing recurrent network | Lazar, A.; Pipa, G. ; Triesch, J. |
| 2017 | Encoding and Decoding Dynamic Sensory Signals with Recurrent Neural Networks: An Application of Conceptors to Birdsongs | Gast, Richard; P, Faion; Standvoss, Kai; A, Suckro; B, Lewis; Pipa, Gordon |
| 2013 | Encoding Through Patterns: Regression Tree-Based Neuronal Population Models | Haslinger, Robert; Pipa, Gordon ; Lewis, Laura D.; Nikolic, Danko; Williams, Ziv; Brown, Emery |
| 2019 | Event-based pattern detection in active dendrites | Leugering, Johannes; Nieters, Pascal ; Pipa, Gordon |
| 2011 | Extraction of network topology from multi-electrode recordings: is there a small-world effect? | Gerhard, Felipe; Pipa, Gordon ; Lima, Bruss; Neuenschwander, Sergio; Gerstner, Wulfram |
HOCHSCHULBIBLIOGRAPHIE