k-组件建模的最小k核问题。

IF 2.3 4区 医学 Q1 Neuroscience
Journal of Mathematical Neuroscience Pub Date : 2015-12-01 Epub Date: 2015-07-14 DOI:10.1186/s13408-015-0027-4
Cynthia I Wood, Illya V Hicks
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引用次数: 10

摘要

细胞集合的概念由Hebb提出,并由Palm在图论的框架内进行数学形式化。在联想记忆的研究中,细胞集合是一组紧密相连的神经元,代表了我们知识的一个“概念”。这个组以一种特殊的方式连接,只有一小部分神经元会激发整个集合。我们将细胞组装的概念与最小k核的闭合联系起来,并研究了一种称为k组装的特定类型的细胞组装。本文的目标是找出网络中所有必须被激发才能激活k组装的子结构。通过数值实验,我们证实了这些重要亚群的部分重叠。为了探索这一问题,我们提出了一种回溯算法来查找给定无向图的所有最小k核,这类问题属于np困难问题。该方法是对brown和Kerbosch算法的改进,用于寻找无向图的所有团。测试图中的结果提供了分析图结构的洞察力,并有助于更好地理解概念是如何存储的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

The Minimal k-Core Problem for Modeling k-Assemblies.

The Minimal k-Core Problem for Modeling k-Assemblies.

The Minimal k-Core Problem for Modeling k-Assemblies.

The Minimal k-Core Problem for Modeling k-Assemblies.

The concept of cell assembly was introduced by Hebb and formalized mathematically by Palm in the framework of graph theory. In the study of associative memory, a cell assembly is a group of neurons that are strongly connected and represent a "concept" of our knowledge. This group is wired in a specific manner such that only a fraction of its neurons will excite the entire assembly. We link the concept of cell assembly to the closure of a minimal k-core and study a particular type of cell assembly called k-assembly. The goal of this paper is to find all substructures within a network that must be excited in order to activate a k-assembly. Through numerical experiments, we confirm that fractions of these important subgroups overlap. To explore the problem, we present a backtracking algorithm to find all minimal k-cores of a given undirected graph, which belongs to the class of NP-hard problems. The proposed method is a modification of the Bron and Kerbosch algorithm for finding all cliques of an undirected graph. The results in the tested graphs offer insight in analyzing graph structure and help better understand how concepts are stored.

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来源期刊
Journal of Mathematical Neuroscience
Journal of Mathematical Neuroscience Neuroscience-Neuroscience (miscellaneous)
自引率
0.00%
发文量
0
审稿时长
13 weeks
期刊介绍: The Journal of Mathematical Neuroscience (JMN) publishes research articles on the mathematical modeling and analysis of all areas of neuroscience, i.e., the study of the nervous system and its dysfunctions. The focus is on using mathematics as the primary tool for elucidating the fundamental mechanisms responsible for experimentally observed behaviours in neuroscience at all relevant scales, from the molecular world to that of cognition. The aim is to publish work that uses advanced mathematical techniques to illuminate these questions. It publishes full length original papers, rapid communications and review articles. Papers that combine theoretical results supported by convincing numerical experiments are especially encouraged. Papers that introduce and help develop those new pieces of mathematical theory which are likely to be relevant to future studies of the nervous system in general and the human brain in particular are also welcome.
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