生物混合智能：分布式生物-人工计算的概念框架

中文摘要 AI 辅助

生物系统与人工系统提供互补的适应、学习和计算形式，体外神经技术的进展正日益实现二者间的双向耦合。随着这些系统愈发紧密地整合，一个关键的架构问题是如何在两种基底间分配与任务相关的计算。然而现有的生物混合解决方案要么优化生物基底，要么优化AI模型，要么优化二者的接口，均未明确处理此类计算如何分配、重新分配及评估。我们提出生物混合智能（Biological-Hybrid Intelligence, BHI），这是一个用于在自适应生物基底与人工基底间分配计算的概念框架，二者通过生物电子接口耦合，由协调器进行协同。BHI将两种基底均视为计算实体，其计算职责可在运行过程中改变。BHI要求双向共适应，区别于仅解码生物活动、刺激生物基底或仅适配单一组件的系统。BHI进一步定义了三种运行模式：对抗、协作与共生，分别以两种基底是否竞争、划分计算任务或在任务执行中相互必需为区分。BHI为比较计算框架提供了共同基础，定义了延迟、生存力、接口带宽、学习效率及可复现性的基准，还强调了由双向刺激、适应与数据交换引发的治理考量。更广泛而言，BHI邀请计算机科学家将生物基底视为主动计算资源，不仅要思考任务应如何计算，还要思考其计算应位于何处。因此，BHI将生物-人工整合重新定义为计算分配、协同与控制的系统级问题。

英文摘要

Biological and artificial systems offer complementary forms of adaptation, learning, and computation, with advances in in-vitro neurotechnology increasingly enabling bidirectional coupling between them. As these systems become more tightly integrated, a key architectural question is how task-relevant computation should be distributed across both substrates. Yet existing biohybrid solutions optimise the biological substrate, the AI model, or their interface without explicitly addressing how such computation is allocated, reassigned, and evaluated. We introduce Biological-Hybrid Intelligence (BHI), a conceptual framework for distributing computation across adaptive biological and artificial substrates coupled through a bioelectronic interface and coordinated by an orchestrator. BHI treats both substrates as computational entities whose computational responsibilities may change during operation. BHI requires reciprocal co-adaptation and differs from systems that merely decode biological activity, stimulate a living substrate, or adapt a single component. BHI further defines three operating modes: adversarial, collaborative, and codependent, distinguished by whether the substrates compete, divide computational labour, or become mutually necessary for task performance. BHI provides a common basis for comparing computational frameworks, defining benchmarks for latency, viability, interface bandwidth, learning efficiency, and reproducibility. It also highlights governance considerations arising from reciprocal stimulation, adaptation, and data exchange. More broadly, BHI invites computer scientists to consider biological substrates as active computational resources and to ask not only how a task should be computed, but where its computation should reside. BHI therefore reframes biological-artificial integration as a system-level problem of computational allocation, coordination, and control.

补充信息

备注

Submitted for magazine publication