Problem
Research questions and friction points this paper is trying to address.
Biological-Hybrid Intelligence
Computation Distribution
Adaptive Substrates
Biological-Hybrid Intelligence: A Conceptual Framework for Distributed Biological--Artificial Computation
🤖 AI Summary
本文提出生物-混合智能框架，通过生物电子接口和协调器分配适应性生物与人工基质间的计算任务，解决生物-人工智能系统中计算分配、重新分配及评估问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Biological-Hybrid Intelligence
computational allocation
adaptive substrates
operating modes
co-adaptation
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