On April 4, Anil Yaman and Giovanni Iacca were interviewed by Ingrid Fadelli from Techxplore to discuss their recent conference publication entitled Learning with Delayed Synaptic Plasticity, to be presented at the Genetic and Evolutionary Computing Conference (GECCO) 2019. During the interview Yaman and Iacca discussed the contribution of their work, which is a biologically-inspired learning mechanism to perform synaptic changes (that is, plasticity) in artificial neural networks. The main novelty of this mechanism is that it is based on local activations of neurons and reinforcement signals received after a certain period of time. Their results demonstrate that the proposed mechanism achieves a more effective learning relative to a learning approach that does make use of information about the local interactions of neurons in the network. Their approach provides a novel framework to allow learning in autonomous agents in scenarios where the reinforcement signals are not immediately available.