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Olfactory learning without the mushroom bodies: Spiking neural network models of the honeybee lateral antennal lobe tract reveal its capacities in odour memory tasks of varied complexities.

, , , and . PLoS Comput. Biol., (2017)

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A Method for Selecting the Bin Size of a Time Histogram., and . Neural Comput., 19 (6): 1503-1527 (2007)Kernel bandwidth optimization in spike rate estimation., and . J. Comput. Neurosci., 29 (1-2): 171-182 (2010)Approximate Inference for Time-Varying Interactions and Macroscopic Dynamics of Neural Populations., , and . PLoS Comput. Biol., (2017)Online Estimation of Multiple Dynamic Graphs in Pattern Sequences., , and . IJCNN, page 1-8. IEEE, (2019)State-space analysis on time-varying correlations in parallel spike sequences., , , and . ICASSP, page 3501-3504. IEEE, (2009)State-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data., , , and . PLoS Comput. Biol., (2012)State-Space Analysis of an Ising Model Reveals Contributions of Pairwise Interactions to Sparseness, Fluctuation, and Stimulus Coding of Monkey V1 Neurons., and . ICANN (3), volume 11141 of Lecture Notes in Computer Science, page 641-651. Springer, (2018)How does transient signaling input affect the spike timing of postsynaptic neuron near the threshold regime: an analytical study., , , and . J. Comput. Neurosci., 44 (2): 147-171 (2018)Olfactory learning without the mushroom bodies: Spiking neural network models of the honeybee lateral antennal lobe tract reveal its capacities in odour memory tasks of varied complexities., , , and . PLoS Comput. Biol., (2017)A recipe for optimizing a time-histogram., and . NIPS, page 1289-1296. MIT Press, (2006)