ArticleiScience2026
Selective perturbation of mirror and non-mirror neurons in an in silico model of the action observation network.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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.
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