arXiv cs.AI· Ali Mahdavi, Azadeh Zamanifar, Amirfarhad Farhadi, Omid Kashefi·· 12 小时前AI 评分19
ApproxBrain：连接组约束神经系统中可认证的充分性前沿
ApproxBrain: Certified Sufficiency Frontiers in Connectome-Constrained Neural Systems
AI 导读
ApproxBrain 将计算充分性形式化为以任务和容差对（允许退化 ε、允许失败比例 ρ）为条件的成本索引神经元子集族，并在留出参数抽样上用单侧界认证候选。在公开行走模拟器上，已发表的 148 神经元电路被认证为带余量充分（32 次留出抽样平均 Dtask = 0.017，无失败），其任意 146 或 147 神经元子集均不充分，移除三个中间神经元中的任一个都会破坏充分性。
正文 · 原文
Abstract:Background. A connectome specifies the substrate available to a nervous system, not how much of it a given computation requires. Reported compact sufficient circuits are point estimates: one subset, one task, one fidelity threshold, one parameterisation. Methods. We formalise computational sufficiency as a cost-indexed family of neuron subsets conditioned on a task and a tolerance pair (permitted degradation epsilon, permitted failure fraction rho), separate task adequacy from phenotype fidelity, and certify candidates on held-out parameter draws with one-sided bounds rather than reporting a single minimum. We measure three quantities a single circuit cannot express: non-decomposability, degeneracy, and an intervention-derived role taxonomy. Results. On a released walking simulator, the published 148-neuron circuit is certified sufficient with margin (mean Dtask = 0.017 over 32 held-out draws, no failure), no 146- or 147-neuron subset of it is sufficient, and removing any one of its three interneurons destroys adequacy. Complement splitting recovers the published set exactly in three of seven seeds and reaches its size in five, within 66-97 evaluations, against 221 neurons for the bundle hierarchy at the same budget. Varying only the task changes which neurons are needed: our best-found solutions retain 155 and 149 neurons and share only 3 of 11 searchable neurons. Relaxing the tolerance reveals alternative sufficient circuits, and the whole-CNS interface alone fails every replicate while 93 search-chosen neurons give a certified solution. Conclusions. Sufficiency is set-valued and conditioned jointly on task and tolerance, and no single search strategy dominates across regimes. A frontier is interpretable only alongside its interface-only baseline, its tolerance and its budget. Every result here is model-relative.
| Subjects: | Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE) |
| Cite as: | arXiv:2611.06929 [q-bio.NC] |
| (or arXiv:2611.06929v1 [q-bio.NC] for this version) | |
| https://doi.org/10.48550/arXiv.2611.06929 arXiv-issued DOI via DataCite |
Submission history
From: Azadeh Zamanifar [view email]
[v1]
Tue, 22 Sep 2026 16:42:02 UTC (38 KB)
来源：arXiv cs.AI · arxiv.org