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Anders Lansner

Affiliations: 
CB KTH Royal Institute of Technology, Stockholm, Stockholms län, Sweden 
Area:
computational neuroscience, brain-like computing
Website:
http://www.nada.kth.se/~ala/
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"Anders Lansner"
Mean distance: 14.88 (cluster 17)
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Parents

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Sten Grillner grad student KTH Royal Institute of Technology

Children

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Benjamin Auffarth grad student KTH Royal Institute of Technology
Simon Benjaminsson grad student KTH Royal Institute of Technology
Fredrik Edin grad student KTH Royal Institute of Technology
Örjan Ekeberg grad student KTH Royal Institute of Technology
Florian Fiebig grad student KTH Royal Institute of Technology
Jeanette Hellgren Kotaleski grad student Royal Institute of Technology
Christopher Johansson grad student KTH Royal Institute of Technology
Roland Orre grad student KTH Royal Institute of Technology
Martin Rehn grad student KTH Royal Institute of Technology
Anders Sandberg grad student KTH Royal Institute of Technology
Malin Sandström grad student KTH Royal Institute of Technology
Johannes Hjorth grad student 2003-2009 Royal Institute of Technology

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Publications

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Fiebig F, Herman P, Lansner A. (2020) An Indexing Theory for Working Memory based on Fast Hebbian Plasticity. Eneuro
Chrysanthidis N, Fiebig F, Lansner A. (2019) Introducing double bouquet cells into a modular cortical associative memory model. Journal of Computational Neuroscience
Martinez RH, Lansner A, Herman P. (2019) Probabilistic associative learning suffices for learning the temporal structure of multiple sequences. Plos One. 14: e0220161
Iatropoulos G, Herman P, Lansner A, et al. (2018) The language of smell: Connecting linguistic and psychophysical properties of odor descriptors. Cognition. 178: 37-49
Fiebig F, Lansner A. (2017) A Spiking Working Memory Model Based on Hebbian Short-Term Potentiation. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 37: 83-96
Berthet P, Lindahl M, Tully PJ, et al. (2016) Functional Relevance of Different Basal Ganglia Pathways Investigated in a Spiking Model with Reward Dependent Plasticity. Frontiers in Neural Circuits. 10: 53
Mazzoni A, Lindén H, Cuntz H, et al. (2015) Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models. Plos Computational Biology. 11: e1004584
Krishnamurthy P, Silberberg G, Lansner A. (2015) Long-range recruitment of Martinotti cells causes surround suppression and promotes saliency in an attractor network model. Frontiers in Neural Circuits. 9: 60
Eriksson J, Vogel EK, Lansner A, et al. (2015) Neurocognitive Architecture of Working Memory. Neuron. 88: 33-46
Vogginger B, Schüffny R, Lansner A, et al. (2015) Reducing the computational footprint for real-time BCPNN learning. Frontiers in Neuroscience. 9: 2
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