Probabilistic inference of binary Markov random fields in spiking neural networks through mean-field approximation-Reference-Cited by-同舟云学术

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Probabilistic inference of binary Markov random fields in spiking neural networks through mean-field approximation

Published:2020-06 Issue: Volume:126 Page:42-51
ISSN:0893-6080
Container-title:Neural Networks
language:en
Short-container-title:Neural Networks

Author:

Zheng Yajing^ORCID,Jia Shanshan^ORCID,Yu Zhaofei^ORCID,Huang Tiejun,Liu Jian K.^ORCID,Tian Yonghong^ORCID

Funder

National Natural Science Foundation of China

National Postdoctoral Program for Innovative Talents, China

China Postdoctoral Science Foundation

Zhejiang Lab, China

Royal Society

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference37 articles.

1. Computational principles of sensorimotor control that minimize uncertainty and variability;Bays;The Journal of Physiology,2007

2. State-dependent computations: spatiotemporal processing in cortical networks;Buonomano;Nature Reviews Neuroscience,2009

3. Probabilistic models of cognition: where next?;Chater;Trends in Cognitive Sciences,2006

4. Spatio-temporal Markov Random Field for video denoising;Chen,2007

5. Theoretical neuroscience, Vol. 806;Dayan,2001

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1. A Spiking Neural Network Learning Markov Chain;2023 International Joint Conference on Neural Networks (IJCNN);2023-06-18

2. Secondary and Topological Structural Merge Prediction of Alpha-Helical Transmembrane Proteins Using a Hybrid Model Based on Hidden Markov and Long Short-Term Memory Neural Networks;International Journal of Molecular Sciences;2023-03-16

3. Neural Field Continuum Limits and the Structure–Function Partitioning of Cognitive–Emotional Brain Networks;Biology;2023-02-23

4. Active Inference Integrated With Imitation Learning for Autonomous Driving;IEEE Access;2022

5. A generative spiking neural-network model of goal-directed behaviour and one-step planning;PLOS Computational Biology;2020-12-08

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