Probabilistic computation underlying sequence learning in a spiking attractor memory network

BMC Neuroscience

Poster Presentation

Anders Lansner¹  Henrik Lindén²  Philip Tully³  Matthias H Hennig⁴ 

[1] Department of Computational Biology, Royal Institute of Technology (KTH), S-10044, Stockholm, Sweden;Department of Numerical Analysis and Computing Science, Stockholm University, S-10044, Stockholm, Sweden;Stockholm Brain Institute, Karolinska Institutet, 171 77, Stockholm, Sweden;Department of Computational Biology, Royal Institute of Technology (KTH), S-10044, Stockholm, Sweden;Stockholm Brain Institute, Karolinska Institutet, 171 77, Stockholm, Sweden;Department of Computational Biology, Royal Institute of Technology (KTH), S-10044, Stockholm, Sweden;Stockholm Brain Institute, Karolinska Institutet, 171 77, Stockholm, Sweden;Institute for Adaptive and Neural Computation, University of Edinburgh, EH8 9AB, Edinburgh, UK;Institute for Adaptive and Neural Computation, University of Edinburgh, EH8 9AB, Edinburgh, UK;

关键词: Gamma Oscillation;    Spike Pattern;    Attractor Network;    Intrinsic Excitability;    Plasticity Rule;   

DOI  :  10.1186/1471-2202-14-S1-P236

来源: Springer

【 授权许可】

CC BY   
© Tully et al; licensee BioMed Central Ltd. 2013