推断推理：从大规模神经活动推断规范分布式计算的数学框架
Inferring Inference
AI 导读
一项 arXiv 研究提出从大规模神经活动模式推断大脑规范分布式计算的数学框架，假设大脑学习环境的结构化内部模型，推理过程是世界图模型上的非线性消息传递算法。该框架能从感知推理任务的神经活动中找出潜在变量的神经表征、变量间交互及消息函数，并在模拟大脑的人工神经记录上成功恢复了潜在变量、其表征与动力学以及规范消息函数。
正文 · 原文
Abstract:Patterns of microcircuitry in the cerebral cortex suggest that the brain has an array of repeated canonical computational units. However, neural representations are distributed, so the relevant computations may only be related indirectly to single-neuron transformations. It thus remains an open challenge how to define canonical distributed computations. Here we integrate normative and algorithmic theories of neural computation to present a mathematical framework for inferring canonical distributed computations from large-scale neural activity patterns. At the normative level, we hypothesize that the brain learns a structured internal model of its environment and its latent causes. At the algorithmic level, we propose that this inference process is a nonlinear message-passing algorithm on a graph-structured model of the world. Given a time series of neural activity during a perceptual inference task, our analysis framework finds (i) the neural representation of the relevant latent variables, (ii) interactions between these latent variables that define the brain's internal model of the world, and (iii) message functions that specify the inference algorithm. With enough data, key computational properties are identifiable due to constraints from the symmetries inherent in any canonical computation, up to a joint global transformation of all interactions and the message-passing functions. As a demonstration of this framework, we analyze artificial neural recordings generated by a simulated brain that implicitly implements an approximate inference algorithm. Given simulated inputs and neural activity we successfully recover the latent variables, their neural representation and dynamics, and canonical message functions that govern the dynamics. Analysis of these models reveals features of experimental design required to extract canonical computations from neural data.
| Comments: | 26 pages, 4 figures and 1 supplementary figure |
| Subjects: | Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2310.03186 [q-bio.NC] |
| (or arXiv:2310.03186v4 [q-bio.NC] for this version) | |
| https://doi.org/10.48550/arXiv.2310.03186 arXiv-issued DOI via DataCite |
Submission history
From: Xaq Pitkow [view email]
[v1]
Wed, 4 Oct 2023 22:12:11 UTC (30,802 KB)
[v2]
Fri, 6 Oct 2023 19:14:54 UTC (30,802 KB)
[v3]
Fri, 13 Oct 2023 22:04:12 UTC (30,816 KB)
[v4]
Tue, 27 Oct 2026 01:27:21 UTC (22,650 KB)
来源：arXiv cs.AI · arxiv.org