Shahidi Neda, Rozenblit Fernando, Khani Mohammad H, Schreyer Helene M, Mietsch Matthias, Protti Dario A, Gollisch Tim
Department of Ophthalmology, University Medical Center Göttingen, Göttingen, Germany.
Bernstein Center for Computational Neuroscience Göttingen, Göttingen, Germany.
PLoS Comput Biol. 2025 May 2;21(5):e1013031. doi: 10.1371/journal.pcbi.1013031. eCollection 2025 May.
The dichotomy of excitation and suppression is one of the canonical mechanisms explaining the complexity of neural activity. Computational models of the interplay of excitation and suppression in single neurons aim at investigating how this interaction affects a neuron's spiking responses and shapes the encoding of sensory stimuli. Here, we compare the performance of three filter-based stimulus-encoding models for predicting retinal ganglion cell responses recorded from axolotl, mouse, and marmoset retina to different types of temporally varying visual stimuli. Suppression in these models is implemented via subtractive or divisive interactions of stimulus filters or by a response-driven feedback module. For the majority of ganglion cells, the subtractive and divisive models perform similarly and outperform the feedback model as well as a linear-nonlinear (LN) model with no suppression. Comparison between the subtractive and the divisive model depends on cell type, species, and stimulus components, with the divisive model generalizing best across temporal stimulus frequencies and visual contrast and the subtractive model capturing in particular responses for slow temporal stimulus dynamics and for slow axolotl cells. Overall, we conclude that the divisive and subtractive models are well suited for capturing interactions of excitation and suppression in ganglion cells and perform best for different temporal regimes of these interactions.
兴奋与抑制的二分法是解释神经活动复杂性的典型机制之一。单个神经元中兴奋与抑制相互作用的计算模型旨在研究这种相互作用如何影响神经元的放电反应以及塑造感觉刺激的编码。在此，我们比较了三种基于滤波器的刺激编码模型在预测从蝾螈、小鼠和狨猴视网膜记录的视网膜神经节细胞对不同类型随时间变化的视觉刺激的反应时的性能。这些模型中的抑制是通过刺激滤波器的减法或除法相互作用或通过响应驱动的反馈模块来实现的。对于大多数神经节细胞而言，减法模型和除法模型的表现相似，并且优于反馈模型以及无抑制的线性-非线性（LN）模型。减法模型和除法模型之间的比较取决于细胞类型、物种和刺激成分，其中除法模型在时间刺激频率和视觉对比度方面的泛化能力最佳，而减法模型尤其能捕捉到对缓慢时间刺激动态以及对蝾螈慢速细胞的反应。总体而言，我们得出结论，除法模型和减法模型非常适合捕捉神经节细胞中兴奋与抑制的相互作用，并且在这些相互作用的不同时间范围内表现最佳。
