Klefenz Frank, Harczos Tamas
Fraunhofer Institute for Digital Media Technology IDMT, Ilmenau, Germany.
Auditory Neuroscience and Optogenetics Laboratory, German Primate Center, Göttingen, Germany.
Front Neurosci. 2020 Jun 4;14:486. doi: 10.3389/fnins.2020.00486. eCollection 2020.
This study presents a computational model to reproduce the biological dynamics of "listening to music." A biologically plausible model of periodicity pitch detection is proposed and simulated. Periodicity pitch is computed across a range of the auditory spectrum. Periodicity pitch is detected from subsets of activated auditory nerve fibers (ANFs). These activate connected model octopus cells, which trigger model neurons detecting onsets and offsets; thence model interval-tuned neurons are innervated at the right interval times; and finally, a set of common interval-detecting neurons indicate pitch. Octopus cells rhythmically spike with the pitch periodicity of the sound. Batteries of interval-tuned neurons stopwatch-like measure the inter-spike intervals of the octopus cells by coding interval durations as first spike latencies (FSLs). The FSL-triggered spikes synchronously coincide through a monolayer spiking neural network at the corresponding receiver pitch neurons.
本研究提出了一种计算模型，以再现“听音乐”的生物动力学过程。提出并模拟了一种具有生物学合理性的周期性音高检测模型。在一系列听觉频谱范围内计算周期性音高。从激活的听神经纤维（ANF）子集中检测周期性音高。这些激活相连的模型章鱼细胞，进而触发检测起始和终止的模型神经元；从而在正确的间隔时间内支配模型间隔调谐神经元；最后，一组共同的间隔检测神经元指示音高。章鱼细胞以声音的音高周期性有节奏地放电。一系列间隔调谐神经元像秒表一样，通过将间隔持续时间编码为首次放电潜伏期（FSL）来测量章鱼细胞的峰峰间隔。FSL触发的放电通过单层脉冲神经网络在相应的接收音高神经元处同步重合。
