An Oscillatory Neural Network Model for Birdsong Learning and Generation
We present a model of bird song production in which the motor control pathway is modeled by a trainable network of oscillators and the Anterior Forebrain Pathway (AFP) is modeled as a stochastic system. We hypothesize 1) that the songbird learns only evaluations of songs during the sensory phase; 2) that the AFP plays a role analogous to the Explorer, a key component in Reinforcement Learning (RL); 3) the motor pathway learns the song by combining the evaluations (Value information) stored from the sensory phase, and the exploratory inputs from the AFP in a temporal stage-wise manner. Model performance from real birdsong samples is presented.
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