PDF(Pubmed)
Abstract：
Network structures of the brain have wiring patterns specialized for specific functions. These patterns are partially determined genetically or evolutionarily based on the type of task or stimulus. These wiring patterns are important in information processing; however, their organizational principles are not fully understood. This study frames the maximization of information transmission alongside the reduction of maintenance costs as a multi-objective optimization challenge, utilizing information theory and evolutionary computing algorithms with an emphasis on the visual system. The goal is to understand the underlying principles of circuit formation by exploring the patterns of wiring and information processing. The study demonstrates that efficient information transmission necessitates sparse circuits with internal modular structures featuring distinct wiring patterns. Significant trade-offs underscore the necessity of balance in wiring pattern development. The dynamics of effective circuits exhibit moderate flexibility in response to stimuli, in line with observations from prior visual system studies. Maximizing information transfer may allow for the self-organization of information processing functions similar to actual biological circuits, without being limited by modality. This study offers insights into neuroscience and the potential to improve reservoir computing performance.
摘要：
大脑的网络结构具有专门用于特定功能的布线模式。这些模式是根据任务或刺激的类型在遗传或进化上部分确定的。这些布线图案在信息处理中很重要；然而，他们的组织原则没有得到充分理解。这项研究将信息传输的最大化以及维护成本的降低作为多目标优化挑战，利用信息论和进化计算算法，重点是视觉系统。目标是通过探索布线和信息处理的模式来理解电路形成的基本原理。研究表明，有效的信息传输需要具有内部模块化结构的稀疏电路，这些结构具有不同的布线模式。重要的权衡强调了布线图案开发中平衡的必要性。有效电路的动力学在响应刺激时表现出适度的灵活性，与先前视觉系统研究的观察结果一致。最大化信息传递可以允许类似于实际生物电路的信息处理功能的自组织，不受模态的限制。这项研究提供了对神经科学的见解以及提高储层计算性能的潜力。
