A computational approach towards the ontogeny of mirror neurons via Hebbian learning
AbstractIt has been proposed that Hebbian learning could be responsible for the ontogeny of predictive mirror neurons in the premotor cortex (Keysers and Gazzola, 2014). Here, we show that an artificial neural network (ANN) that evolves via variation of Oja’s rule (an implementation of Hebbian learning) is sufficient to account for the emergence of predictive mirror neurons. By extension, this work provides positive evidence for the association hypothesis, which states that mirror-like behavior in the motor cortex is a byproduct of associative learning.
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