Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, 560012, India. Electronic address: https://twitter.com/PaveeSeeni.
Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, 560012, India.
Curr Opin Neurobiol. 2022 Oct;76:102620. doi: 10.1016/j.conb.2022.102620. Epub 2022 Aug 17.
Efficient information coding (EIC) is a universal biological framework rooted in the fundamental principle that system responses should match their natural stimulus statistics for maximizing environmental information. Quantitatively assessed through information theory, such adaptation to the environment occurs at all biological levels and timescales. The context dependence of environmental stimuli and the need for stable adaptations make EIC a daunting task. We argue that biological complexity is the principal architect that subserves deft execution of stable EIC. Complexity in a system is characterized by several functionally segregated subsystems that show a high degree of functional integration when they interact with each other. Complex biological systems manifest heterogeneities and degeneracy, wherein structurally different subsystems could interact to yield the same functional outcome. We argue that complex systems offer several choices that effectively implement EIC and homeostasis for each of the different contexts encountered by the system.
有效信息编码（EIC）是一个普遍的生物学框架，其基础原理是系统响应应与其自然刺激统计数据相匹配，以最大化环境信息。通过信息论进行量化评估，这种对环境的适应发生在所有的生物层次和时间尺度上。由于环境刺激的上下文依赖性和对稳定适应的需求，EIC 是一项艰巨的任务。我们认为，生物复杂性是实现稳定 EIC 的主要架构。一个系统的复杂性表现为几个功能上分离的子系统，当它们相互作用时，表现出高度的功能整合。复杂的生物系统表现出异质性和简并性，其中结构不同的子系统可以相互作用产生相同的功能结果。我们认为，复杂系统为系统遇到的每个不同环境提供了几种有效实施 EIC 和体内平衡的选择。
