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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/
Google:
"Anders Lansner"
Mean distance: 14.88 (cluster 17)
 		SNBCP
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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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Lansner A, Fiebig F, Herman P. (2023) Fast Hebbian plasticity and working memory. Current Opinion in Neurobiology. 83: 102809
Chrysanthidis N, Fiebig F, Lansner A, et al. (2022) Traces of semantization - from episodic to semantic memory in a spiking cortical network model. Eneuro
Yang Y, Stathis D, Jordão R, et al. (2020) Optimizing BCPNN Learning Rule for Memory Access. Frontiers in Neuroscience. 14: 878
Fiebig F, Herman P, Lansner A. (2020) An Indexing Theory for Working Memory based on Fast Hebbian Plasticity. Eneuro
Stathis D, Sudarshan C, Yang Y, et al. (2020) eBrainII: a 3 kW Realtime Custom 3D DRAM Integrated ASIC Implementation of a Biologically Plausible Model of a Human Scale Cortex Journal of Signal Processing Systems. 1-21
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
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
Tully PJ, Lindén H, Hennig MH, et al. (2016) Spike-Based Bayesian-Hebbian Learning of Temporal Sequences. Plos Computational Biology. 12: e1004954
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