Scholarlink

<p>Neural networks underlie complex brain information processing, yet the role of their single-neuron topology in governing computation and behavior remains unclear, particularly regarding how it shapes individual neuron function and activity evolution during learning. Using two-photon calcium imaging, we tracked functional connectivity in thousands of posterior parietal cortex neurons as monkeys learned sensorimotor associations across days. We identified small-world networks with densely connected hub neurons that dominated encoding key task variables, driving local dynamics and neural encoding evolution during the monkeys’ task performance and learning. Dynamic transitions in hub/non-hub status captured how inter-neuronal interactions shaped neuronal encoding evolution during association formation. Modular structures supported specialized neuron ensembles, enabling segregated representations and interactions within local networks. Importantly, small-world network properties predicted behavioral performance, with global information processing efficiency increasing as learning progressed. These findings reveal how single-neuron-resolution brain networks, through small-world organization, orchestrate both global and modular neural computations within local network to mediate behavior and shape learning.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Single-neuron network topology governs neural computation and learning in primate cortex

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月30日

查看全文
  • Zhuangyi Jiang,
  • Ziang Liu,
  • Li Shi,
  • Fang Fang,
  • Shiming Tang,
  • Yang Zhou

摘要

Neural networks underlie complex brain information processing, yet the role of their single-neuron topology in governing computation and behavior remains unclear, particularly regarding how it shapes individual neuron function and activity evolution during learning. Using two-photon calcium imaging, we tracked functional connectivity in thousands of posterior parietal cortex neurons as monkeys learned sensorimotor associations across days. We identified small-world networks with densely connected hub neurons that dominated encoding key task variables, driving local dynamics and neural encoding evolution during the monkeys’ task performance and learning. Dynamic transitions in hub/non-hub status captured how inter-neuronal interactions shaped neuronal encoding evolution during association formation. Modular structures supported specialized neuron ensembles, enabling segregated representations and interactions within local networks. Importantly, small-world network properties predicted behavioral performance, with global information processing efficiency increasing as learning progressed. These findings reveal how single-neuron-resolution brain networks, through small-world organization, orchestrate both global and modular neural computations within local network to mediate behavior and shape learning.