A bio-inspired model towards vocal gesture learning in songbird

Silvia Pagliarini, Xavier Hinaut, Arthur Leblois
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引用次数: 2

Abstract

The paper proposes a bio-inspired model for an imitative sensorimotor learning, which aims at building a map between the sensory representations of gestures (sensory targets) and their underlying motor pattern through random exploration of the motor space. An example of such learning process occurs during vocal learning in humans or birds, when young subjects babble and learn to copy previously heard adult vocalizations. Previous work has suggested that a simple Hebbian learning rule allows perfect imitation when sensory feedback is a purely linear function of the motor pattern underlying movement production. We aim at generalizing this model to the more realistic case where sensory responses are sparse and non-linear. To this end, we explore the performance of various learning rules and normalizations and discuss their biological relevance. Importantly, the proposed model is robust whatever normalization is chosen. We show that both the imitation quality and the convergence time are highly dependent on the sensory selectivity and dimension of the motor representation.
鸣禽声音手势学习的仿生模型
本文提出了一种仿生感觉运动学习模型,该模型旨在通过对运动空间的随机探索,在手势(感觉目标)的感觉表征与其潜在运动模式之间建立映射。这种学习过程的一个例子发生在人类或鸟类的声乐学习过程中,当年轻的受试者咿呀学语并学会模仿以前听到的成人发声时。先前的研究表明,当感觉反馈是运动模式的纯线性函数时,一个简单的Hebbian学习规则允许完美的模仿。我们的目标是将这个模型推广到更现实的情况下,其中感觉反应是稀疏和非线性的。为此,我们探讨了各种学习规则和规范化的表现,并讨论了它们的生物学相关性。重要的是,无论选择何种归一化,所提出的模型都具有鲁棒性。我们发现,模仿质量和收敛时间都高度依赖于感觉选择性和运动表征的维度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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