Rowekamp Ryan J, Sharpee Tatyana O
Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, California, United States of America.
Department of Physics, University of California - San Diego, La Jolla, California, United States of America.
PLoS Comput Biol. 2025 Jun 20;21(6):e1013075. doi: 10.1371/journal.pcbi.1013075. eCollection 2025 Jun.
Biological visual systems are celebrated for their ability to reliably and precisely recognize objects. However, the specific neural mechanisms responsible for this capability remain largely elusive. In this study, we investigate neural responses in the visual areas V1, V2, and V4 of the brain to natural stimuli using a framework that includes quadratic computations in order to capture local recurrent interactions, both excitatory and suppressive. We find that these quadratic computations and specific coordination between their elements strongly increase both the predictive power of the model and the neural selectivity to natural stimuli. Particularly important were (i) coordination between excitatory and suppressive features to represent mutually exclusive hypotheses regarding incoming stimuli, such as orthogonal orientations or opposing motion directions in area V4, (ii) balance in the contribution of excitatory and suppressive components and its maintenance at similar levels across stages of processing, and (iii) refinement of feature selectivity between stages, with earlier stages representing broader category of inputs. Overall, this work describes how the brain could use multiple nonlinear mechanisms to increase selectivity of neural responses to natural stimuli.
生物视觉系统因其可靠且精确地识别物体的能力而备受赞誉。然而，负责这种能力的具体神经机制在很大程度上仍然难以捉摸。在本研究中，我们使用一个包含二次计算的框架来研究大脑视觉区域V1、V2和V4对自然刺激的神经反应，以便捕捉局部循环相互作用，包括兴奋性和抑制性相互作用。我们发现，这些二次计算及其元素之间的特定协调极大地提高了模型的预测能力以及对自然刺激的神经选择性。特别重要的是：（i）兴奋性和抑制性特征之间的协调，以表示关于传入刺激的相互排斥的假设，例如V4区域中的正交方向或相反运动方向；（ii）兴奋性和抑制性成分贡献的平衡及其在处理阶段的相似水平上的维持；（iii）不同阶段之间特征选择性的细化，早期阶段代表更广泛的输入类别。总体而言，这项工作描述了大脑如何利用多种非线性机制来提高神经对自然刺激反应的选择性。
