Oprisan Sorinel A, Novo Dereck, Buhusi Mona, Buhusi Catalin V
Department of Physics and Astronomy, College of Charleston, Charleston, SC 29424, USA.
Department of Psychology, Utah State University, Logan, UT 84322, USA.
Timing Time Percept. 2023;11(1-4):103-123. doi: 10.1163/22134468-bja10056. Epub 2022 Jul 21.
The Striatal Beat Frequency (SBF) model of interval timing uses many neural oscillators, presumably located in the frontal cortex (FC), to produce beats at a specific criterion time Tc. The coincidence detection produces the beats in the basal ganglia spiny neurons by comparing the current state of the FC neural oscillators against the long-term memory values stored at reinforcement time Tc. The neurobiologically realistic SBF model has been previously used for producing precise and scalar timing in the presence of noise. Here we simplified the SBF model to gain insight into the problem of resource allocation in interval timing networks. Specifically, we used a noise-free SBF model to explore the lower limits of the number of neural oscillators required for producing accurate timing. Using abstract sine-wave neural oscillators in the SBF-sin model, we found that the lower limit of the number of oscillators needed is proportional to the criterion time Tc and the frequency span (fmax - fmin) of the FC neural oscillators. Using biophysically realistic Morris-Lecar model neurons in the SBF-ML model, the lower bound increased by one to two orders of magnitude compared to the SBF-sin model.
间隔计时的纹状体节拍频率（SBF）模型使用许多可能位于额叶皮质（FC）的神经振荡器，在特定标准时间Tc产生节拍。通过将FC神经振荡器的当前状态与强化时间Tc存储的长期记忆值进行比较，重合检测在基底神经节棘状神经元中产生节拍。具有神经生物学现实意义的SBF模型此前已被用于在存在噪声的情况下产生精确的标量计时。在这里，我们简化了SBF模型，以深入了解间隔计时网络中的资源分配问题。具体而言，我们使用无噪声的SBF模型来探索产生准确计时所需的神经振荡器数量的下限。在SBF-sin模型中使用抽象的正弦波神经振荡器，我们发现所需振荡器数量的下限与标准时间Tc和FC神经振荡器的频率跨度（fmax - fmin）成正比。在SBF-ML模型中使用具有生物物理现实意义的Morris-Lecar模型神经元，与SBF-sin模型相比，下限增加了一到两个数量级。
