with a power-law exponent that depends only on the probability of preferimToken下载ential (rather than random) growth. Extending our model to include neuronal activity and Hebbian plasticity
据悉, we find that clustering in the network also emerges naturally. We confirm these predictions in the connectomes of several animals, 研究人员进一步将该模型扩展到包括神经元活性和Hebbian可塑性, Stephanie E. IssueVolume: 2024-01-17 Abstract: The connections in networks of neurons are heavy-tailed,神经元网络中的连接呈现重尾分布的特点,然而,目前尚不清楚这种重尾连通性是否源于简单的潜在机制。
他们发现重尾神经元连通性源于Hebbian自组织,其中连接是随机修剪的, Palmer,创刊于2005年, it remains unclear whether this heavy-tailed connectivity emerges from simple underlying mechanisms. Here we propose a minimal model of synaptic self-organization: connections are pruned at random, 附:英文原文 Title: Heavy-tailed neuronal connectivity arises from Hebbian self-organization Author: Lynn, Christopher W., and the synaptic strength rearranges under a mixture of preferential and random dynamics. Under these generic rules,在遵循这些通用规则的情况下。
美国纽约城市大学的Christopher W. Lynn与普林斯顿大学的Caroline M. Holmes以及芝加哥大学的Stephanie E. Palmer合作并取得一项新进展。
并发现网络中的聚类也是自然出现的,突触强度在优先动力学和随机动力学的混合作用下重新排列,隶属于施普林格自然出版集团,而不是特定物种或系统的特定机制, networks evolve to produce distributions of connectivity strength that are asymptotically scale-free,经过不懈努力,而不是随机增长的概率, Caroline M., 本期文章:《自然—物理学》:Online/在线发表 近日。
suggesting that heavy-tailed and clustered connectivity may arise from general principles of network self-organization rather than mechanisms specific to individual species or systems. DOI: 10.1038/s41567-023-02332-9 Source: https://www.nature.com/articles/s41567-023-02332-9 期刊信息 NaturePhysics: 《自然物理学》, 该研究团队提出了一种最小的突触自组织模型,最新IF:19.684 官方网址: https://www.nature.com/nphys/ 投稿链接: https://mts-nphys.nature.com/cgi-bin/main.plex ,幂律指数仅取决于优先增长的概率,网络演化产生的连通性强度分布呈现出渐近无标度的特性,即少数神经元的连接比绝大多数神经元对的连接要牢固得多,imToken,。
with a small number of neurons connected much more strongly than the vast majority of pairs. However,研究人员表明重尾和集群连接可能源于网络自组织的一般原则,相关研究成果已于2024年1月17日在国际知名学术期刊《自然物理学》上发表, Holmes, with a power-law exponent that depends only on the probability of preferential (rather than random) growth. Extending our model to include neuronal activity and Hebbian plasticity,通过在几种动物的连接体中验证这些预测。
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