Agreement in Spiking Neural Networks

被引：0

作者：

Kunev, Martin ^{[1
]}

Kuznetsov, Petr ^{[1
,3
]}

Sheynikhovich, Denis ^{[2
]}

机构：

[1] Inst Polytech Paris, LTCI, Telecom Paris, Palaiseau, France

[2] Sorbonne Univ, Inst Vis, INSERM, CNRS, Paris, France

[3] Inst Polytech Paris, LTCI, Telecom Paris, 19 Pl Marguerite Perey, Palaiseau F-91123, France

来源：

JOURNAL OF COMPUTATIONAL BIOLOGY | 2022年 / 29卷 / 04期

关键词：

binary agreement; complexity; consensus; spiking neural network; winner-take-all;

D O I：

10.1089/cmb.2021.0365

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

We study the problem of binary agreement in a spiking neural network (SNN). We show that binary agreement on n inputs can be achieved with O(n) of auxiliary neurons. Our simulation results suggest that agreement can be achieved in our network in O(logn) time. We then describe a subclass of SNNs with a biologically plausible property, which we call size-independence. We prove that solving a class of problems, including agreement and Winner-Take-All, in this model requires omega(n) auxiliary neurons, which makes our agreement network size-optimal.

引用

页码：358 / 369

页数：12

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