A spiking neural network model of canonical babbling development

Canonical babbling, the production of vocalizations that contain mature-sounding syllables, is one of the most striking and important milestones prior to the production of first words. This study simulates the emergence of canonical babbling using a spiking neural network containing motor neurons that activate muscles in a vocal tract simulator. The spiking neural network periodically produces synthesized vocalizations and a human listener judges the vocalizations on the basis of their syllabicity, deciding whether or not to reward the model. If a reward is given, spike timing dependent plasticity is increased and the model becomes more likely to recreate a pattern of neural firings similar to that which generated the reinforced vocalization. The model successfully increases its production of mature-sounding canonical syllables, whereas a yoked control simulation does not exhibit any such effect. This finding corresponds to results of experimental work with human infants in which consonant-vowel syllable production is selectively reinforced by the infants´ caregivers.