Using Inspiration from Synaptic Plasticity Rules to Optimize Traffic Flow in Distributed Engineered Networks | Citedby Results | Neural Computation | MIT Press

Skip to Main Content

Skip Nav Destination

Using Inspiration from Synaptic Plasticity Rules to Optimize Traffic Flow in Distributed Engineered Networks

Neural Computation (2017) 29 (5): 1204–1228.

This article has been cited by the following articles in journals that are participating in Crossref Cited-by Linking.

A feedback control principle common to several biological and engineered systems

Jonathan Y. Suen
Saket Navlakha

Journal of The Royal Society Interface (2022) 19 (188)

SO‐VMEC: Service offloading in virtual mobile edge computing using deep reinforcement learning

Mohammed Laroui
Hatem Ibn‐Khedher
Moussa Ali Cherif
Hassine Moungla
Hossam Afifi
Ahmed E. Kamel

Transactions on Emerging Telecommunications Technologies (2022) 33 (10)

Prediction of traffic flow with small time granularity at intersection based on probabilistic network

Wenbin Xiao
Shunying Zhu
Qiucheng Chen
Srikanta Patnaik

Journal of Intelligent & Fuzzy Systems (2020) 39 (2): 1659.

Short-term traffic flow prediction in smart multimedia system for Internet of Vehicles based on deep belief network

Fanhui Kong
Jian Li
Bin Jiang
Houbing Song

Future Generation Computer Systems (2019) 93: 460.

A neural data structure for novelty detection

Sanjoy Dasgupta
Timothy C. Sheehan
Charles F. Stevens
Saket Navlakha

Proceedings of the National Academy of Sciences (2018) 115 (51): 13093.

Network Design and the Brain

Saket Navlakha
Ziv Bar-Joseph
Alison L. Barth

Trends in Cognitive Sciences (2018) 22 (1): 64.