| Year |
Citation |
Score |
| 2023 |
Shtyrov Y, Efremov A, Kuptsova A, Wennekers T, Gutkin B, Garagnani M. Breakdown of category-specific word representations in a brain-constrained neurocomputational model of semantic dementia. Scientific Reports. 13: 19572. PMID 37949997 DOI: 10.1038/s41598-023-41922-8 |
0.807 |
|
| 2021 |
Pulvermüller F, Tomasello R, Henningsen-Schomers MR, Wennekers T. Biological constraints on neural network models of cognitive function. Nature Reviews. Neuroscience. PMID 34183826 DOI: 10.1038/s41583-021-00473-5 |
0.806 |
|
| 2019 |
Tomasello R, Wennekers T, Garagnani M, Pulvermüller F. Visual cortex recruitment during language processing in blind individuals is explained by Hebbian learning. Scientific Reports. 9: 3579. PMID 30837569 DOI: 10.1038/S41598-019-39864-1 |
0.811 |
|
| 2018 |
Tomasello R, Garagnani M, Wennekers T, Pulvermüller F. A Neurobiologically Constrained Cortex Model of Semantic Grounding With Spiking Neurons and Brain-Like Connectivity. Frontiers in Computational Neuroscience. 12: 88. PMID 30459584 DOI: 10.3389/Fncom.2018.00088 |
0.811 |
|
| 2018 |
Rast AD, Adams SV, Davidson S, Davies S, Hopkins M, Rowley A, Stokes AB, Wennekers T, Furber S, Cangelosi A. Behavioral Learning in a Cognitive Neuromorphic Robot: An Integrative Approach. Ieee Transactions On Neural Networks and Learning Systems. PMID 29994007 DOI: 10.1109/Tnnls.2018.2816518 |
0.394 |
|
| 2018 |
Hernández García D, Adams S, Rast A, Wennekers T, Furber S, Cangelosi A. Visual attention and object naming in humanoid robots using a bio-inspired spiking neural network Robotics and Autonomous Systems. 104: 56-71. DOI: 10.1016/J.Robot.2018.02.010 |
0.409 |
|
| 2017 |
Kajić I, Gosmann J, Stewart TC, Wennekers T, Eliasmith C. A Spiking Neuron Model of Word Associations for the Remote Associates Test. Frontiers in Psychology. 8: 99. PMID 28210234 DOI: 10.3389/fpsyg.2017.00099 |
0.426 |
|
| 2017 |
Wennekers T, Emmett M, Denham SL. ConversationPiece II: Displaced and Rehacked Avant: Journal of the Philosophical-Interdisciplinary Vanguard. 8: 205-211. DOI: 10.26913/80S02017.0111.0019 |
0.364 |
|
| 2016 |
Garagnani M, Lucchese G, Tomasello R, Wennekers T, Pulvermüller F. A Spiking Neurocomputational Model of High-Frequency Oscillatory Brain Responses to Words and Pseudowords. Frontiers in Computational Neuroscience. 10: 145. PMID 28149276 DOI: 10.3389/Fncom.2016.00145 |
0.818 |
|
| 2016 |
Fletcher JM, Wennekers T. From Structure to Activity: Using Centrality Measures to Predict Neuronal Activity. International Journal of Neural Systems. 1750013. PMID 28076982 DOI: 10.1142/S0129065717500137 |
0.452 |
|
| 2016 |
Tomasello R, Garagnani M, Wennekers T, Pulvermüller F. Brain connections of words, perceptions and actions: A neurobiological model of spatio-temporal semantic activation in the human cortex. Neuropsychologia. PMID 27394150 DOI: 10.1016/J.Neuropsychologia.2016.07.004 |
0.806 |
|
| 2015 |
Kajic I, Wennekers T. A Hebbian cell assembly based neural field model for the remote associate task and creative search Bmc Neuroscience. 16. DOI: 10.1186/1471-2202-16-S1-P284 |
0.448 |
|
| 2014 |
Pulvermüller F, Garagnani M, Wennekers T. Thinking in circuits: toward neurobiological explanation in cognitive neuroscience. Biological Cybernetics. 108: 573-93. PMID 24939580 DOI: 10.1007/S00422-014-0603-9 |
0.789 |
|
| 2014 |
Rumbell T, Denham SL, Wennekers T. A spiking self-organizing map combining STDP, oscillations, and continuous learning. Ieee Transactions On Neural Networks and Learning Systems. 25: 894-907. PMID 24808036 DOI: 10.1109/TNNLS.2013.2283140 |
0.463 |
|
| 2014 |
Adams SV, Wennekers T, Cangelosi A, Garagnani M, Pulvermuller F. Learning visual-motor Cell Assemblies for the iCub robot using a neuroanatomically grounded neural network Ieee Ssci 2014 - 2014 Ieee Symposium Series On Computational Intelligence - Ccmb 2014: 2014 Ieee Symposium On Computational Intelligence, Cognitive Algorithms, Mind, and Brain, Proceedings. 1-8. DOI: 10.1109/CCMB.2014.7020687 |
0.784 |
|
| 2013 |
Coath M, Sheik S, Chicca E, Indiveri G, Denham SL, Wennekers T. A robust sound perception model suitable for neuromorphic implementation. Frontiers in Neuroscience. 7: 278. PMID 24478621 DOI: 10.3389/Fnins.2013.00278 |
0.466 |
|
| 2013 |
Dura-Bernal S, Garreau G, Georgiou J, Andreou AG, Denham SL, Wennekers T. Multimodal integration of micro-Doppler sonar and auditory signals for behavior classification with convolutional networks. International Journal of Neural Systems. 23: 1350021. PMID 23924412 DOI: 10.1142/S0129065713500214 |
0.676 |
|
| 2013 |
Adams SV, Wennekers T, Denham S, Culverhouse PF. Adaptive training of cortical feature maps for a robot sensorimotor controller. Neural Networks : the Official Journal of the International Neural Network Society. 44: 6-21. PMID 23545539 DOI: 10.1016/j.neunet.2013.03.004 |
0.324 |
|
| 2013 |
Basalyga G, Montemurro MA, Wennekers T. Information coding in a laminar computational model of cat primary visual cortex. Journal of Computational Neuroscience. 34: 273-83. PMID 22907135 DOI: 10.1007/s10827-012-0420-x |
0.325 |
|
| 2012 |
Dura-Bernal S, Wennekers T, Denham SL. Top-down feedback in an HMAX-like cortical model of object perception based on hierarchical Bayesian networks and belief propagation. Plos One. 7: e48216. PMID 23139765 DOI: 10.1371/Journal.Pone.0048216 |
0.748 |
|
| 2012 |
Humble J, Denham S, Wennekers T. Spatio-temporal pattern recognizers using spiking neurons and spike-timing-dependent plasticity. Frontiers in Computational Neuroscience. 6: 84. PMID 23087641 DOI: 10.3389/fncom.2012.00084 |
0.386 |
|
| 2012 |
Denham S, Bendixen A, Mill R, Tóth D, Wennekers T, Coath M, Bőhm T, Szalardy O, Winkler I. Characterising switching behaviour in perceptual multi-stability. Journal of Neuroscience Methods. 210: 79-92. PMID 22525854 DOI: 10.1016/J.Jneumeth.2012.04.004 |
0.302 |
|
| 2012 |
Sheik S, Coath M, Indiveri G, Denham SL, Wennekers T, Chicca E. Emergent Auditory Feature Tuning in a Real-Time Neuromorphic VLSI System. Frontiers in Neuroscience. 6: 17. PMID 22347163 DOI: 10.3389/Fnins.2012.00017 |
0.47 |
|
| 2012 |
Davies LA, Garcia-Lazaro JA, Schnupp JW, Wennekers T, Denham SL. Tell me something interesting: context dependent adaptation in somatosensory cortex. Journal of Neuroscience Methods. 210: 35-48. PMID 22186665 DOI: 10.1016/j.jneumeth.2011.12.003 |
0.362 |
|
| 2012 |
Mill R, Coath M, Wennekers T, Denham SL. Characterising stimulus-specific adaptation using a multi-layer field model. Brain Research. 1434: 178-88. PMID 21955728 DOI: 10.1016/j.brainres.2011.08.063 |
0.381 |
|
| 2011 |
Mill R, Coath M, Wennekers T, Denham SL. A neurocomputational model of stimulus-specific adaptation to oddball and Markov sequences. Plos Computational Biology. 7: e1002117. PMID 21876661 DOI: 10.1371/journal.pcbi.1002117 |
0.412 |
|
| 2011 |
Dura-Bernal S, Wennekers T, Denham SL. The role of feedback in a hierarchical model of object perception. Advances in Experimental Medicine and Biology. 718: 165-79. PMID 21744218 DOI: 10.1007/978-1-4614-0164-3_14 |
0.712 |
|
| 2011 |
Basalyga G, Gleiser PM, Wennekers T. Emergence of small-world structure in networks of spiking neurons through STDP plasticity. Advances in Experimental Medicine and Biology. 718: 33-9. PMID 21744208 DOI: 10.1007/978-1-4614-0164-3_4 |
0.431 |
|
| 2011 |
Coath M, Mill R, Denham SL, Wennekers T. Emergent feature sensitivity in a model of the auditory thalamocortical system. Advances in Experimental Medicine and Biology. 718: 7-17. PMID 21744206 DOI: 10.1007/978-1-4614-0164-3_2 |
0.408 |
|
| 2011 |
Mill R, Coath M, Wennekers T, Denham SL. Abstract stimulus-specific adaptation models. Neural Computation. 23: 435-76. PMID 21114400 DOI: 10.1162/NECO_a_00077 |
0.362 |
|
| 2011 |
Basalyga G, Montemurro M, Wennekers T. Phase-of-firing information coding in laminar cortical architecture Bmc Neuroscience. 12. DOI: 10.1186/1471-2202-12-S1-P369 |
0.314 |
|
| 2011 |
Dura-Bernal S, Wennekers T, Denham SL. Modelling object perception in cortex: Hierarchical Bayesian networks and belief propagation 2011 45th Annual Conference On Information Sciences and Systems, Ciss 2011. DOI: 10.1109/CISS.2011.5766096 |
0.699 |
|
| 2011 |
Garreau G, Andreou CM, Andreou AG, Georgiou J, Dura-Bernal S, Wennekers T, Denham S. Gait-based person and gender recognition using micro-doppler signatures 2011 Ieee Biomedical Circuits and Systems Conference, Biocas 2011. 444-447. DOI: 10.1109/BioCAS.2011.6107823 |
0.595 |
|
| 2011 |
Adams SV, Wennekers T, Bugmann G, Denham S, Culverhouse PF. Application of arachnid prey localisation theory for a robot sensorimotor controller Neurocomputing. 74: 3335-3342. DOI: 10.1016/j.neucom.2011.05.020 |
0.322 |
|
| 2011 |
Dura-Bernal S, Garreau G, Andreou C, Andreou A, Georgiou J, Wennekers T, Denham S. Human action categorization using ultrasound micro-Doppler signatures Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 7065: 18-28. DOI: 10.1007/978-3-642-25446-8_3 |
0.599 |
|
| 2009 |
Garagnani M, Wennekers T, Pulvermüller F. Recruitment and Consolidation of Cell Assemblies for Words by Way of Hebbian Learning and Competition in a Multi-Layer Neural Network. Cognitive Computation. 1: 160-176. PMID 20396612 DOI: 10.1007/S12559-009-9011-1 |
0.819 |
|
| 2009 |
Wennekers T, Palm G. Syntactic sequencing in Hebbian cell assemblies. Cognitive Neurodynamics. 3: 429-41. PMID 19760130 DOI: 10.1007/s11571-009-9095-z |
0.616 |
|
| 2009 |
Cutsuridis V, Wennekers T. Hippocampus, microcircuits and associative memory. Neural Networks : the Official Journal of the International Neural Network Society. 22: 1120-8. PMID 19647982 DOI: 10.1016/j.neunet.2009.07.009 |
0.44 |
|
| 2009 |
Symes A, Wennekers T. Spatiotemporal dynamics in the cortical microcircuit: a modelling study of primary visual cortex layer 2/3. Neural Networks : the Official Journal of the International Neural Network Society. 22: 1079-92. PMID 19635655 DOI: 10.1016/j.neunet.2009.07.017 |
0.412 |
|
| 2009 |
Cutsuridis V, Wennekers T, Graham BP, Vida I, Taylor JG. Microcircuits--their structure, dynamics and role for brain function. Neural Networks : the Official Journal of the International Neural Network Society. 22: 1037-8. PMID 19619981 DOI: 10.1016/J.Neunet.2009.07.006 |
0.457 |
|
| 2009 |
Wennekers T. Formal concepts expressed by compositional hierarchical Hebbian cell assemblies Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P91 |
0.471 |
|
| 2009 |
Basalyga G, Wennekers T. Large-scale computational model of cat primary visual cortex Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P358 |
0.323 |
|
| 2009 |
Dura S, Wennekers T, Denham S. Feedback in a hierarchical model of object recognition in cortex Bmc Neuroscience. 10. DOI: 10.1186/1471-2202-10-S1-P355 |
0.402 |
|
| 2009 |
Wennekers T. On the natural hierarchical composition of cliques in cell assemblies Cognitive Computation. 1: 128-138. DOI: 10.1007/s12559-008-9004-5 |
0.471 |
|
| 2008 |
Wennekers T. Tuned solutions in dynamic neural fields as building blocks for extended EEG models. Cognitive Neurodynamics. 2: 137-46. PMID 19003480 DOI: 10.1007/s11571-008-9045-1 |
0.415 |
|
| 2008 |
Garagnani M, Wennekers T, Pulvermüller F. A neuroanatomically grounded Hebbian-learning model of attention-language interactions in the human brain. The European Journal of Neuroscience. 27: 492-513. PMID 18215243 DOI: 10.1111/J.1460-9568.2008.06015.X |
0.816 |
|
| 2008 |
Olbrich E, Wennekers T. Multichannel analysis of neural oscillations in a simple model network – towards a better understanding of the spatiotemporal structure of brain oscillations Bmc Neuroscience. 9. DOI: 10.1186/1471-2202-9-S1-P153 |
0.331 |
|
| 2007 |
Wennekers T, Ay N, Andras P. High-resolution multiple-unit EEG in cat auditory cortex reveals large spatio-temporal stochastic interactions. Bio Systems. 89: 190-7. PMID 17188422 DOI: 10.1016/j.biosystems.2006.04.017 |
0.402 |
|
| 2007 |
Andras P, Wennekers T. Cortical activity pattern computation. Bio Systems. 87: 179-85. PMID 17046149 DOI: 10.1016/j.biosystems.2006.09.012 |
0.358 |
|
| 2007 |
Olbrich E, Wennekers T. Dynamics of parameters of neurophysiological models from phenomenological EEG modeling Neurocomputing. 70: 1848-1852. DOI: 10.1016/j.neucom.2006.10.108 |
0.384 |
|
| 2007 |
Wennekers T. A cell assembly model for complex behaviour Neurocomputing. 70: 1988-1992. DOI: 10.1016/j.neucom.2006.10.079 |
0.48 |
|
| 2007 |
Garagnani M, Wennekers T, Pulvermüller F. A neuronal model of the language cortex Neurocomputing. 70: 1914-1919. DOI: 10.1016/J.Neucom.2006.10.076 |
0.809 |
|
| 2006 |
Wennekers T, Garagnani M, Pulvermüller F. Language models based on Hebbian cell assemblies. Journal of Physiology, Paris. 100: 16-30. PMID 17081735 DOI: 10.1016/J.Jphysparis.2006.09.007 |
0.823 |
|
| 2006 |
Wennekers T, Ay N. A temporal learning rule in recurrent systems supports high spatio-temporal stochastic interactions Neurocomputing. 69: 1199-1202. DOI: 10.1016/j.neucom.2005.12.075 |
0.403 |
|
| 2005 |
Wennekers T, Ay N. Finite state automata resulting from temporal information maximization and a temporal learning rule. Neural Computation. 17: 2258-90. PMID 16105225 DOI: 10.1162/0899766054615671 |
0.388 |
|
| 2005 |
Wennekers T, Ay N. Stochastic interaction in associative nets Neurocomputing. 65: 387-392. DOI: 10.1016/j.neucom.2004.10.033 |
0.42 |
|
| 2005 |
Sommer FT, Wennekers T. Synfire chains with conductance-based neurons: Internal timing and coordination with timed input Neurocomputing. 65: 449-454. DOI: 10.1016/j.neucom.2004.10.015 |
0.753 |
|
| 2004 |
Wennekers T. Separation of spatio-temporal receptive fields into sums of gaussian components. Journal of Computational Neuroscience. 16: 27-38. PMID 14707542 DOI: 10.1023/B:JCNS.0000004839.49178.2d |
0.407 |
|
| 2004 |
Wennekers T, Suder K. Fitting of spatio-temporal receptive fields by sums of Gaussian components Neurocomputing. 58: 929-934. DOI: 10.1016/j.neucom.2004.01.148 |
0.375 |
|
| 2003 |
Sommer FT, Wennekers T. Models of distributed associative memory networks in the brain Theory in Biosciences. 122: 55-69. DOI: 10.1078/1431-7613-00074 |
0.779 |
|
| 2003 |
Wennekers T, Ay N. Spatial and temporal stochastic interaction in neuronal assemblies Theory in Biosciences. 122: 5-18. DOI: 10.1078/1431-7613-00071 |
0.399 |
|
| 2003 |
Wennekers T, Sommer F, Aertsen A. Editorial: Cell assemblies Theory in Biosciences. 122: 1-4. DOI: 10.1078/1431-7613-00070 |
0.708 |
|
| 2003 |
Wennekers T, Ay N. Temporal Infomax on Markov chains with input leads to finite state automata Neurocomputing. 52: 431-436. DOI: 10.1016/S0925-2312(02)00862-7 |
0.313 |
|
| 2002 |
Wennekers T. Dynamic approximation of spatiotemporal receptive fields in nonlinear neural field models. Neural Computation. 14: 1801-25. PMID 12180403 DOI: 10.1162/089976602760128027 |
0.375 |
|
| 2002 |
Suder K, Funke K, Zhao Y, Kerscher N, Wennekers T, Wörgötter F. Spatial dynamics of receptive fields in cat primary visual cortex related to the temporal structure of thalamocortical feedforward activity. Experiments and models. Experimental Brain Research. 144: 430-44. PMID 12037629 DOI: 10.1007/S00221-002-1061-5 |
0.393 |
|
| 2002 |
Wennekers T. Nonlinear analysis of spatio-temporal receptive fields: IV. Generic tuning properties for rectifying rate-functions Neurocomputing. 44: 219-223. DOI: 10.1016/S0925-2312(02)00435-6 |
0.378 |
|
| 2002 |
Wennekers T. Nonlinear analysis of spatio-temporal receptive fields: III. RF-reconstruction from mean-field approximations Neurocomputing. 44: 213-218. DOI: 10.1016/S0925-2312(02)00433-2 |
0.343 |
|
| 2002 |
Wennekers T. Nonlinear analysis of spatio-temporal receptive fields: II. Dynamic properties of V1 simple cells Neurocomputing. 44: 207-212. DOI: 10.1016/S0925-2312(02)00431-9 |
0.384 |
|
| 2002 |
Knoblauch A, Wennekers T, Sommer FT. Is voltage-dependent synaptic transmission in NMDA receptors a robust mechanism for working memory? Neurocomputing. 44: 19-24. DOI: 10.1016/S0925-2312(02)00364-8 |
0.766 |
|
| 2001 |
Sommer FT, Wennekers T. Associative memory in networks of spiking neurons. Neural Networks : the Official Journal of the International Neural Network Society. 14: 825-34. PMID 11665774 DOI: 10.1016/S0893-6080(01)00064-8 |
0.785 |
|
| 2001 |
Wennekers T. Orientation tuning properties of simple cells in area V1 derived from an approximate analysis of nonlinear neural field models. Neural Computation. 13: 1721-47. PMID 11506668 DOI: 10.1162/08997660152469323 |
0.409 |
|
| 2001 |
Suder K, Wörgötter F, Wennekers T. Neural field model of receptive field restructuring in primary visual cortex. Neural Computation. 13: 139-59. PMID 11177431 DOI: 10.1162/089976601300014664 |
0.411 |
|
| 2001 |
Sommer FT, Wennekers T. Associative memory in a pair of cortical cell groups with reciprocal projections Neurocomputing. 38: 1575-1580. DOI: 10.1016/S0925-2312(01)00563-X |
0.775 |
|
| 2001 |
Wennekers T, Pasemann F. Generalized types of synchronization in networks of spiking neurons Neurocomputing. 38: 1037-1042. DOI: 10.1016/S0925-2312(01)00389-7 |
0.427 |
|
| 2001 |
Wennekers T. Nonlinear analysis of orientation tuning: Stationary properties of simple cells in V1 Neurocomputing. 38: 439-444. DOI: 10.1016/S0925-2312(01)00372-1 |
0.325 |
|
| 2000 |
Sommer FT, Wennekers T. Modelling studies on the computational function of fast temporal structure in cortical circuit activity. Journal of Physiology, Paris. 94: 473-88. PMID 11165914 DOI: 10.1016/S0928-4257(00)01098-6 |
0.779 |
|
| 2000 |
Pasemann F, Wennekers T. Generalized and partial synchronization of coupled neural networks. Network (Bristol, England). 11: 41-61. PMID 10735528 DOI: 10.1088/0954-898X_11_1_303 |
0.425 |
|
| 2000 |
Wennekers T. Dynamics of spatio-temporal patterns in associative networks of spiking neurons Neurocomputing. 32: 597-602. DOI: 10.1016/S0925-2312(00)00217-4 |
0.383 |
|
| 2000 |
Suder K, Wörgötter F, Wennekers T. Neural field description of state-dependent visual receptive field changes Neurocomputing. 32: 545-551. DOI: 10.1016/S0925-2312(00)00211-3 |
0.351 |
|
| 1999 |
Wennekers T, Sommer FT. Gamma-oscillations support optimal retrieval in associative memories of two-compartment neurons Neurocomputing. 26: 573-578. DOI: 10.1016/S0925-2312(99)00036-3 |
0.766 |
|
| 1999 |
Wennekers T, Palm G. How imprecise is neuronal synchronization? Neurocomputing. 26: 579-585. DOI: 10.1016/S0925-2312(98)00168-4 |
0.416 |
|
| 1997 |
Palm G, Wennekers T. Synchronicity and its use in the brain Behavioral and Brain Sciences. 20: 695-696. DOI: 10.1017/S0140525X97361603 |
0.58 |
|
| 1996 |
Wennekers T, Pasemann F. Synchronous chaos in high-dimensional modular neural networks International Journal of Bifurcation and Chaos in Applied Sciences and Engineering. 6: 2055-2067. DOI: 10.1142/S0218127496001338 |
0.337 |
|
| 1996 |
Bibbig A, Wennekers T, Palm G. A neural-network model of the cortico-hippocampal interplay: Contexts and generalization Engineering Applications of Artificial Intelligence. 9: 145-151. DOI: 10.1016/0952-1976(96)00005-X |
0.505 |
|
| 1995 |
Bibbig A, Wennekers T, Palm G. A neural network model of the cortico-hippocampal interplay and the representation of contexts. Behavioural Brain Research. 66: 169-75. PMID 7755887 DOI: 10.1016/0166-4328(94)00137-5 |
0.681 |
|
| 1995 |
Bibbig A, Wennekers T, Palm G. Neural network model of the cortico-hippocampal interplay: Contexts and generalization . 12-18. |
0.301 |
|
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