arXiv cs.AI· Rajkumar Vasudeva Raju, Zhe Li, Scott Linderman, Xaq Pitkow·· 13 小时前AI 评分20
从大规模神经活动中推断分布式规范计算：一个数学框架
Inferring Inference
AI 导读
研究者提出一个数学框架，可从大规模神经活动模式中推断大脑的规范分布式计算，在规范层面假设大脑学习环境及其潜在原因的结构化内部模型，在算法层面将推断过程建模为图结构世界模型上的非线性消息传递算法。
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