Abstract | In an extensive simulation study , we show that NEMix improves learning of pathway structures over classical NEMs significantly in the presence of hidden pathway stimulation. |
Application to pathogen infection experiments | Like in the simulation study above, we compared the NEMix model to the NEM and the sc-NEM approach. |
Application to pathogen infection experiments | Like for the simulation study , we derived the cell population effects for the NEM from Wilcoxon tests, comparing the knockdown experiment to the control. |
Application to pathogen infection experiments | Like in the simulation study , p0 was initialized by drawing randomly from a uniform distribution. |
Simulation study | Simulation study |
Simulation study | To test our model, we performed a large simulation study . |
Supporting Information | To assess the edge recovery performance for larger NEMiX models, we ran a reduced simulation study . |
Author Summary | To begin to answer these questions, we conducted analytical and simulation studies examining the propagation of spike correlation in feedback neural circuits. |
Introduction | Several simulation studies further revealed that neurons acquire receptive field [17—19] or spike patterns [20] through STDP by introducing lateral inhibition; yet, those studies were limited to simplified cases for which a large population of independent neurons was suggested to be sufficient [5,21,22]. |
Model | Previous simulation studies showed lateral inhibition has critical effects on excitatory STDP learning [17—19]; however, it has not yet been well studied how a secondary correlation generated through the lateral circuits influences STDP at feedforward connections, and it is still largely unknown how lateral inhibition functions with various stimuli in different neural circuits. |
dev Ma Ma d: g ZLanfv’ VEGA? Z qquv’p — NaWZMa WYZL“ Wig", vi Z qquV/p v,=1 p v’=1 P | Therefore, both analytical and simulation studies indicate that lateral inhibition should be strong, fast and sharp to detect higher correlation structure. |