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Thomas Wennekers
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2020 – today
- 2022
- [j53]Damir Dobric
, Andreas Pech, Bogdan Ghita, Thomas Wennekers:
On the Importance of the Newborn Stage When Learning Patterns with the Spatial Pooler. SN Comput. Sci. 3(2): 179 (2022) - [i2]Mina Marmpena, Fernando Garcia, Angelica Lim, Nikolas Hemion, Thomas Wennekers:
Data-driven emotional body language generation for social robotics. CoRR abs/2205.00763 (2022) - 2021
- [c11]Damir Dobric, Andreas Pech, Bogdan Ghita, Thomas Wennekers:
Improved HTM Spatial Pooler with Homeostatic Plasticity Control. ICPRAM 2021: 98-106 - 2020
- [c10]Damir Dobric, Andreas Pech, Bogdan Ghita, Thomas Wennekers:
On the Relationship Between Input Sparsity and Noise Robustness in Hierarchical Temporal Memory Spatial Pooler. ESSE 2020: 187-193
2010 – 2019
- 2018
- [j52]Rosario Tomasello
A Neurobiologically Constrained Cortex Model of Semantic Grounding With Spiking Neurons and Brain-Like Connectivity. Frontiers Comput. Neurosci. 12: 88 (2018) - [j51]Jack McKay Fletcher, Thomas Wennekers:
From Structure to Activity: Using Centrality Measures to Predict Neuronal Activity. Int. J. Neural Syst. 28(2): 1750013:1-1750013:16 (2018) - [j50]Daniel Hernández García
Visual attention and object naming in humanoid robots using a bio-inspired spiking neural network. Robotics Auton. Syst. 104: 56-71 (2018) - [j49]Alexander D. Rast
Behavioral Learning in a Cognitive Neuromorphic Robot: An Integrative Approach. IEEE Trans. Neural Networks Learn. Syst. 29(12): 6132-6144 (2018) - 2017
- [i1]Jack McKay Fletcher, Thomas Wennekers:
A natural approach to studying schema processing. CoRR abs/1705.04536 (2017) - 2016
- [j48]Max Garagnani
A Spiking Neurocomputational Model of High-Frequency Oscillatory Brain Responses to Words and Pseudowords. Frontiers Comput. Neurosci. 10: 145 (2016) - [c9]Ivana Kajic, Jan Gosmann, Terrence C. Stewart, Thomas Wennekers, Chris Eliasmith:
Towards a Cognitively Realistic Representation of Word Associations. CogSci 2016 - 2015
- [c8]Ivana Kajic, Thomas Wennekers:
Neural Network Model of Semantic Processing in the Remote Associates Test. CoCo@NIPS 2015 - 2014
- [j47]Friedemann Pulvermüller
Thinking in circuits: toward neurobiological explanation in cognitive neuroscience. Biol. Cybern. 108(5): 573-593 (2014) - [c7]Samantha V. Adams, Alexander D. Rast, Cameron Patterson, Francesco Galluppi
Towards Real-World Neurorobotics: Integrated Neuromorphic Visual Attention. ICONIP (3) 2014: 563-570 - 2013
- [j46]Salvador Dura-Bernal
Multimodal Integration of Micro-Doppler Sonar and auditory signals for Behavior Classification with convolutional Networks. Int. J. Neural Syst. 23(5) (2013) - [j45]Gleb Basalyga, Marcelo A. Montemurro
Information coding in a laminar computational model of cat primary visual cortex. J. Comput. Neurosci. 34(2): 273-283 (2013) - [j44]Samantha V. Adams, Thomas Wennekers, Sue L. Denham, Phil F. Culverhouse
Adaptive training of cortical feature maps for a robot sensorimotor controller. Neural Networks 44: 6-21 (2013) - 2012
- [j43]Timothy H. Rumbell
Emotions in autonomous agents: comparative analysis of mechanisms and functions. Auton. Agents Multi Agent Syst. 25(1): 1-45 (2012) - [j42]James Humble
Spatio-temporal pattern recognizers using spiking neurons and spike-timing-dependent plasticity. Frontiers Comput. Neurosci. 6: 84 (2012) - 2011
- [j41]Samantha V. Adams, Thomas Wennekers, Guido Bugmann, Sue L. Denham, Phil F. Culverhouse
Application of arachnid prey localisation theory for a robot sensorimotor controller. Neurocomputing 74(17): 3335-3342 (2011) - [j40]Robert Mill, Martin Coath
Abstract Stimulus-Specific Adaptation Models. Neural Comput. 23(2): 435-476 (2011) - [j39]Robert Mill, Martin Coath
A Neurocomputational Model of Stimulus-Specific Adaptation to Oddball and Markov Sequences. PLoS Comput. Biol. 7(8) (2011) - [c6]Salvador Dura-Bernal
Modelling object perception in cortex: Hierarchical Bayesian networks and belief propagation. CISS 2011: 1-6 - [c5]Julius Georgiou
A multimodal-corpus data collection system for cognitive acoustic scene analysis. CISS 2011: 1-6 - [c4]Salvador Dura-Bernal
Human Action Categorization Using Ultrasound Micro-Doppler Signatures. HBU 2011: 18-28
2000 – 2009
- 2009
- [j38]Thomas Wennekers:
On the Natural Hierarchical Composition of Cliques in Cell Assemblies. Cogn. Comput. 1(2): 128-138 (2009) - [j37]Max Garagnani
Recruitment and Consolidation of Cell Assemblies for Words by Way of Hebbian Learning and Competition in a Multi-Layer Neural Network. Cogn. Comput. 1(2): 160-176 (2009) - [j36]Vassilis Cutsuridis
Microcircuits - Their structure, dynamics and role for brain function. Neural Networks 22(8): 1037-1038 (2009) - [j35]Andrew Symes, Thomas Wennekers:
Spatiotemporal dynamics in the cortical microcircuit: A modelling study of primary visual cortex layer 2/3. Neural Networks 22(8): 1079-1092 (2009) - [j34]Vassilis Cutsuridis
Hippocampus, microcircuits and associative memory. Neural Networks 22(8): 1120-1128 (2009) - 2007
- [j33]Péter András
Cortical activity pattern computation. Biosyst. 87(2-3): 179-185 (2007) - [j32]Thomas Wennekers, Nihat Ay, Péter András
High-resolution multiple-unit EEG in cat auditory cortex reveals large spatio-temporal stochastic interactions. Biosyst. 89(1-3): 190-197 (2007) - [j31]Eckehard Olbrich
Dynamics of parameters of neurophysiological models from phenomenological EEG modeling. Neurocomputing 70(10-12): 1848-1852 (2007) - [j30]Max Garagnani
A neuronal model of the language cortex. Neurocomputing 70(10-12): 1914-1919 (2007) - [j29]Thomas Wennekers:
A cell assembly model for complex behaviour. Neurocomputing 70(10-12): 1988-1992 (2007) - 2006
- [j28]Thomas Wennekers, Nihat Ay:
A temporal learning rule in recurrent systems supports high spatio-temporal stochastic interactions. Neurocomputing 69(10-12): 1199-1202 (2006) - 2005
- [j27]Thomas Wennekers, Nihat Ay:
Stochastic interaction in associative nets. Neurocomputing 65-66: 387-392 (2005) - [j26]Friedrich T. Sommer, Thomas Wennekers:
Synfire chains with conductance-based neurons: internal timing and coordination with timed input. Neurocomputing 65-66: 449-454 (2005) - [j25]Thomas Wennekers, Nihat Ay:
Finite State Automata Resulting from Temporal Information Maximization and a Temporal Learning Rule. Neural Comput. 17(10): 2258-2290 (2005) - 2004
- [j24]Thomas Wennekers, Katrin Suder:
Fitting of spatio-temporal receptive fields by sums of Gaussian components. Neurocomputing 58-60: 929-934 (2004) - [j23]Thomas Wennekers:
Separation of Spatio-Temporal Receptive Fields into Sums of Gaussian Components. J. Comput. Neurosci. 16(1): 27-38 (2004) - 2003
- [j22]Thomas Wennekers, Nihat Ay:
Temporal Infomax on Markov chains with input leads to finite state automata. Neurocomputing 52-54: 431-436 (2003) - [j21]Nihat Ay, Thomas Wennekers:
Temporal infomax leads to almost deterministic dynamical systems. Neurocomputing 52-54: 461-466 (2003) - [j20]Nihat Ay, Thomas Wennekers:
Dynamical properties of strongly interacting Markov chains. Neural Networks 16(10): 1483-1497 (2003) - [j19]Thomas Wennekers, Friedrich T. Sommer, Ad Aertsen:
Editorial: Cell assemblies. Theory Biosci. 122(1): 1-4 (2003) - [j18]Thomas Wennekers, Nihat Ay:
Spatial and temporal stochastic interaction in neuronal assemblies. Theory Biosci. 122(1): 5-18 (2003) - [j17]Friedrich T. Sommer, Thomas Wennekers:
Models of distributed associative memory networks in the brain. Theory Biosci. 122(1): 55-69 (2003) - 2002
- [j16]Andreas Knoblauch, Thomas Wennekers, Friedrich T. Sommer:
Is voltage-dependent synaptic transmission in NMDA receptors a robust mechanism for working memory? Neurocomputing 44-46: 19-24 (2002) - [j15]Thomas Wennekers:
Nonlinear analysis of spatio-temporal receptive fields: I. Dynamic approximation method. Neurocomputing 44-46: 201-206 (2002) - [j14]Thomas Wennekers:
Nonlinear analysis of spatio-temporal receptive fields: II. Dynamic properties of V1 simple cells. Neurocomputing 44-46: 207-212 (2002) - [j13]Thomas Wennekers:
Nonlinear analysis of spatio-temporal receptive fields: III. RF-reconstruction from mean-field approximations. Neurocomputing 44-46: 213-218 (2002) - [j12]Thomas Wennekers:
Nonlinear analysis of spatio-temporal receptive fields: IV. Generic tuning properties for rectifying rate-functions. Neurocomputing 44-46: 219-223 (2002) - [j11]Thomas Wennekers:
Dynamic Approximation of Spatiotemporal Receptive Fields in Nonlinear Neural Field Models. Neural Comput. 14(8): 1801-1825 (2002) - [c3]Thomas Wennekers:
Nonlinear Analysis of Simple Cell Tuning in Visual Cortex. ICANN 2002: 63-68 - 2001
- [j10]Thomas Wennekers:
Nonlinear analysis of orientation tuning: Stationary properties of simple cells in V1. Neurocomputing 38-40: 439-444 (2001) - [j9]Thomas Wennekers, Frank Pasemann:
Generalized types of synchronization in networks of spiking neurons. Neurocomputing 38-40: 1037-1042 (2001) - [j8]Friedrich T. Sommer, Thomas Wennekers:
Associative memory in a pair of cortical cell groups with reciprocal projections. Neurocomputing 38-40: 1575-1580 (2001) - [j7]Katrin Suder, Florentin Wörgötter, Thomas Wennekers:
Neural Field Model of Receptive Field Restructuring in Primary Visual Cortex. Neural Comput. 13(1): 139-159 (2001) - [j6]Thomas Wennekers:
Orientation Tuning Properties of Simple Cells in Area V1 Derived from an Approximate Analysis of Nonlinear Neural Field Models. Neural Comput. 13(8): 1721-1747 (2001) - [j5]Friedrich T. Sommer, Thomas Wennekers:
Associative memory in networks of spiking neurons. Neural Networks 14(6-7): 825-834 (2001) - 2000
- [j4]Katrin Suder, Florentin Wörgötter, Thomas Wennekers:
Neural field description of state-dependent visual receptive field changes. Neurocomputing 32-33: 545-551 (2000) - [j3]Thomas Wennekers:
Dynamics of spatio-temporal patterns in associative networks of spiking neurons. Neurocomputing 32-33: 597-602 (2000)
1990 – 1999
- 1999
- [b1]Thomas Wennekers:
Synchronisation und Assoziation in neuronalen Netzen. University of Ulm, Germany, Shaker 1999, ISBN 978-3-8265-4908-3, pp. 1-218 - [j2]Thomas Wennekers, Friedrich T. Sommer:
Gamma-oscillations support optimal retrieval in associative memories of two-compartment neurons. Neurocomputing 26-27: 573-578 (1999) - [j1]Thomas Wennekers, Günther Palm:
How imprecise is neuronal synchronization? Neurocomputing 26-27: 579-585 (1999) - [c2]Katrin Suder, Florentin Wörgötter, Thomas Wennekers:
Neural field description of state-dependent receptive field changes in the visual cortex. ESANN 1999: 171-176 - 1996
- [c1]Thomas Wennekers, Günther Palm:
Controlling the Speed of Synfire Chains. ICANN 1996: 451-456
Coauthor Index
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