.png){kind=icon}
返回
Neural computing in four spatial dimensions
.png){kind=icon}
OA
AI
DOI：10.1007/s11571-020-09598-2.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Relationships among near set theory, shape maps and recent accounts of the Quantum Hall effect pave the way to neural networks computations performed in higher dimensions. We illustrate the operational procedure to build a real or artificial neural network able to detect, assess and quantify a fourth spatial dimension. We show how, starting from two-dimensional shapes embedded in a 2D topological charge pump, it is feasible to achieve the corresponding four-dimensional shapes, which encompass a larger amount of information. Synthesis of surface shape components, viewed topologically as shape descriptions in the form of feature vectors that vary over time, leads to a 4D view of cerebral activity. This novel, relatively straightforward architecture permits to increase the amount of available qbits in a fixed volume.
Keyword:
Hall effect
Oscillations
Fourth dimension
Brain
Neuronal network
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.9
论文数:
1.5K
被引数:
2.8K
机构
引用论文
The Borsuk-Ulam theorem solves the curse of dimensionality: Comment on The unreasonable effectiveness of small neural ensembles in high-dimensional brain by Alexander N. Gorban et al..png)
PHYSICS OF LIFE REVIEWS
PHYSICS OF LIFE REVIEWSIF14.3
PREAI
分享收藏分享收藏分享收藏分享收藏
查看更多内容