 This naturally challenges the formulation of biologically inspired RSNNs in order to improve our understanding of biological intelligence and the efficiency of artificial ones . Several spiking network models have been proposed  but it remains a challenge to design RSNNs that use biologically plausible mechanisms  capable of solving complex temporal tasks.
<clarity>  This naturally challenges the formulation of biologically inspired RSNNs in order to improve our understanding of biological intelligence and the efficiency of artificial ones . Several spiking network models have been proposed  but it remains a challenge to design RSNNs that use biologically plausible mechanisms  capable of solving complex temporal tasks.
 This naturally challenges the formulation of biologically inspired RSNNs in order to improve our understanding of biological intelligence and the efficiency of artificial ones . Several spiking network models have been proposed but it remains a challenge to design RSNNs whose learning relies on biologically plausible mechanisms capable of solving complex temporal tasks.