Selective perturbation of mirror and non-mirror neurons in an in silico model of the action observation network

: The action observation network (AON) is a distributed system of brain areas containing mirror neurons (MNs), active during both action execution and observation, as well as purely motor, non-mirror neurons. Here, we present a biologically inspired recurrent neural network (RNN) model trained to reproduce single-neuron activity from three key macaque AON areas-anterior intraparietal area (AIP), areas F5 and F6. The model accurately captured experimental firing patterns and enabled the reconstruction of candidate functional connectivity. Cell-type-specific in silico silencing revealed that inhibitory interneurons exert a strong influence on network function, with those in F5 and F6 contributing to agent identity discrimination. Silencing excitatory non-mirror neurons in F5 and F6 produced larger reductions in self-action decoding performance, whereas silencing excitatory MNs in AIP preferentially impacted the decoding of others' actions. These findings provide a computational framework for linking cell-type-specific perturbations to population-level action representations in the AON.