Third-order motifs are sufficient to fully and uniquely characterize spatiotemporal neural network activity

HIGHLIGHTS

• who: Sarita S. Deshpande from the The University have published the paper: Third-order motifs are sufficient to fully and uniquely characterize spatiotemporal neural network activity, in the Journal: Scientific Reports Scientific Reports
• what: In practice, the authors only calculate the triple correlation up to a certain maximum spatiotemporal lag which the authors determine based on experimental and computational considerations on a perexperiment basis (noted in each figure). The authors report Mi /Ec - 1 so that positive values indicate higher contribution than expected, negative indicate less, and zero indicates contributions in line with those expected . . .
  

   

  

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