An oscillatory neural network model for birdsong learning and generation

  • Authors:
  • Maya Manaithunai;Srinivasa Chakravarthy;Ravindran Balaraman

  • Affiliations:
  • Department of Biotechnology, Indian Institute of Technology Madras, Chennai, India;Department of Biotechnology, Indian Institute of Technology Madras, Chennai, India;Department of Computer Science, Indian Institute of Technology Madras, Chennai, India

  • Venue:
  • ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
  • Year:
  • 2010

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Abstract

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