Learning to Produce Syllabic Speech Sounds via Reward-Modulated Neural Plasticity.

ABSTRACT: At around 7 months of age, human infants begin to reliably produce well-formed syllables containing both consonants and vowels, a behavior called canonical babbling. Over subsequent months, the frequency of canonical babbling continues to increase. How the infant's nervous system supports the acquisition of this ability is unknown. Here we present a computational model that combines a spiking neural network, reinforcement-modulated spike-timing-dependent plasticity, and a human-like vocal tract to simulate the acquisition of canonical babbling. Like human infants, the model's frequency of canonical babbling gradually increases. The model is rewarded when it produces a sound that is more auditorily salient than sounds it has previously produced. This is consistent with data from human infan

SUBMITTER: Warlaumont AS 

PROVIDER: S-EPMC4726623 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature