@inproceedings{783db443cf9e43c09dd5f85b81a89fd8,
title = "Interpretive Reservoir: A Preliminary Study on the Association between Artificial Neural Network and Biological Neural Network",
abstract = "Inspired by the biological nervous system and leveraging the recent advances in neuroscience, artificial neural networks (ANNs) have been extensively investigated and achieved great success in various domains. Nevertheless, the link between the intricate cognitive activity of the biological brain and the learning scheme of ANNs is still unclear and under-explored. Therefore, in this study we aim to preliminarily examine the association between these two parts and provide some explanations and interpretations on the memory-related characteristics associated with neural network topologies and internal connections by modeling the EEG/ERP brain activities with echo state network (ESN)-like architecture. Vector autoregressive (VAR) is adopted for parameter training. The experimental results partially verify the role of network connection pattern and synaptic strength in the memory representation of ANNs.",
keywords = "EEG, ERP, ESN, artificial neural network, brain activity modeling, vector autoregressive",
author = "Wei Wang and Yang Gao and Jin, \{And Zhanpeng\}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 International Joint Conference on Neural Networks, IJCNN 2018 ; Conference date: 08-07-2018 Through 13-07-2018",
year = "2018",
month = oct,
day = "10",
doi = "10.1109/IJCNN.2018.8489191",
language = "英语",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings",
address = "美国",
}