Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
Department of Brain and Cognitive Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
PLoS Comput Biol. 2023 Aug 4;19(8):e1011343. doi: 10.1371/journal.pcbi.1011343. eCollection 2023 Aug.
Long-range horizontal connections (LRCs) are conspicuous anatomical structures in the primary visual cortex (V1) of mammals, yet their detailed functions in relation to visual processing are not fully understood. Here, we show that LRCs are key components to organize a "small-world network" optimized for each size of the visual cortex, enabling the cost-efficient integration of visual information. Using computational simulations of a biologically inspired model neural network, we found that sparse LRCs added to networks, combined with dense local connections, compose a small-world network and significantly enhance image classification performance. We confirmed that the performance of the network appeared to be strongly correlated with the small-world coefficient of the model network under various conditions. Our theoretical model demonstrates that the amount of LRCs to build a small-world network depends on each size of cortex and that LRCs are beneficial only when the size of the network exceeds a certain threshold. Our model simulation of various sizes of cortices validates this prediction and provides an explanation of the species-specific existence of LRCs in animal data. Our results provide insight into a biological strategy of the brain to balance functional performance and resource cost.
长程水平连接（LRCs）是哺乳动物初级视觉皮层（V1）中明显的解剖结构，但它们与视觉处理相关的详细功能尚未完全理解。在这里，我们表明 LRCs 是组织针对每个视觉皮层大小进行优化的“小世界网络”的关键组成部分，能够有效地整合视觉信息。我们使用生物启发模型神经网络的计算模拟发现，稀疏 LRCs 添加到网络中，与密集的局部连接相结合，构成了小世界网络，并显著提高了图像分类性能。我们证实，在各种条件下，网络的性能似乎与模型网络的小世界系数密切相关。我们的理论模型表明，构建小世界网络所需的 LRCs 数量取决于每个皮层的大小，并且只有当网络的大小超过某个阈值时，LRCs 才是有益的。我们对各种大小的皮层的模型模拟验证了这一预测，并为动物数据中 LRCs 的物种特异性存在提供了解释。我们的结果提供了对大脑平衡功能性能和资源成本的生物策略的深入了解。
