Laboratoire ETIS, CY Cergy Paris University, ENSEA, CNRS, UMR8051, Cergy, France.
PLoS Comput Biol. 2021 Feb 18;17(2):e1008566. doi: 10.1371/journal.pcbi.1008566. eCollection 2021 Feb.
We propose a developmental model inspired by the cortico-basal system (CX-BG) for vocal learning in babies and for solving the correspondence mismatch problem they face when they hear unfamiliar voices, with different tones and pitches. This model is based on the neural architecture INFERNO standing for Iterative Free-Energy Optimization of Recurrent Neural Networks. Free-energy minimization is used for rapidly exploring, selecting and learning the optimal choices of actions to perform (eg sound production) in order to reproduce and control as accurately as possible the spike trains representing desired perceptions (eg sound categories). We detail in this paper the CX-BG system responsible for linking causally the sound and motor primitives at the order of a few milliseconds. Two experiments performed with a small and a large audio database show the capabilities of exploration, generalization and robustness to noise of our neural architecture in retrieving audio primitives during vocal learning and during acoustic matching with unheared voices (different genders and tones).
我们提出了一个受皮质基底系统（CX-BG）启发的发展模型，用于婴儿的发声学习以及解决他们在听到陌生声音（不同音调和音高）时面临的对应不匹配问题。该模型基于神经架构 INFERNO，代表用于递归神经网络的迭代自由能优化。自由能最小化用于快速探索、选择和学习执行最佳动作选择（例如发声），以便尽可能准确地再现和控制表示期望感知（例如声音类别）的尖峰序列。本文详细介绍了 CX-BG 系统，该系统负责在几毫秒的时间内因果地连接声音和运动原语。使用小型和大型音频数据库进行的两项实验表明，我们的神经架构在发声学习期间以及与未听过的声音（不同性别和音高）进行声学匹配期间检索音频原语时具有探索、泛化和对噪声鲁棒性的能力。
