Department of Physics, FCEyN, University of Buenos Aires and IFIBA, CONICET, Intendente Güiraldes 2160 (C1428EGA), Pabellon 1, Ciudad Universitaria, Buenos Aires, Argentina.
Chaos. 2020 May;30(5):053134. doi: 10.1063/1.5145093.
Vocal production in songbirds is a key topic regarding the motor control of a complex, learned behavior. Birdsong is the result of the interaction between the activity of an intricate set of neural nuclei specifically dedicated to song production and learning (known as the "song system"), the respiratory system and the vocal organ. These systems interact and give rise to precise biomechanical motor gestures which result in song production. Telencephalic neural nuclei play a key role in the production of motor commands that drive the periphery, and while several attempts have been made to understand their coding strategy, difficulties arise when trying to understand neural activity in the frame of the song system as a whole. In this work, we report neural additive models embedded in an architecture compatible with the song system to provide a tool to reduce the dimensionality of the problem by considering the global activity of the units in each neural nucleus. This model is capable of generating outputs compatible with measurements of air sac pressure during song production in canaries (Serinus canaria). In this work, we show that the activity in a telencephalic nucleus required by the model to reproduce the observed respiratory gestures is compatible with electrophysiological recordings of single neuron activity in freely behaving animals.
鸣禽的发声是关于复杂的习得性行为的运动控制的一个关键主题。鸟鸣是由一组专门用于歌唱产生和学习的复杂神经核团（称为“歌唱系统”）、呼吸系统和发声器官之间的相互作用而产生的。这些系统相互作用，产生精确的生物力学运动手势，从而产生歌唱。端脑神经核团在产生驱动外围的运动指令方面起着关键作用，虽然已经有几次尝试来理解它们的编码策略，但当试图从整体的歌唱系统的角度来理解神经活动时，就会出现困难。在这项工作中，我们报告了嵌入到与歌唱系统兼容的架构中的神经附加模型，以提供一种工具，通过考虑每个神经核团中的单元的全局活动来降低问题的维数。该模型能够生成与金丝雀（Serinus canaria）歌唱时气囊压力测量结果兼容的输出。在这项工作中，我们表明，该模型重现观察到的呼吸动作所需的端脑核团的活动与自由行为动物的单个神经元活动的电生理记录兼容。
