TY - GEN
T1 - Recurrent Dendritic Neural Network
AU - Li, Jiayi
AU - Liu, Zhipeng
AU - Song, Yaotong
AU - Gao, Shangce
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In the realm of deep learning, the prevalent use of the simplistic McCulloch-Pitts Neuron as a foundational building block lacks the interpretability required for complex models. In contrast, the dendritic neuron model (DNM) closely emulates biological neural networks and has demonstrated its effectiveness in diverse classification and prediction tasks. This paper introduces the Recurrent Dendritic Neural Network (RDNN), a novel artificial neural network based on the DNM framework. RDNN offers substantial advantages over traditional neural networks, particularly in financial time series forecasting. We propose that the incorporation of DNM as the fundamental unit in artificial neural networks enhances computational capabilities, positioning it at the forefront of the next generation of deep learning technologies.
AB - In the realm of deep learning, the prevalent use of the simplistic McCulloch-Pitts Neuron as a foundational building block lacks the interpretability required for complex models. In contrast, the dendritic neuron model (DNM) closely emulates biological neural networks and has demonstrated its effectiveness in diverse classification and prediction tasks. This paper introduces the Recurrent Dendritic Neural Network (RDNN), a novel artificial neural network based on the DNM framework. RDNN offers substantial advantages over traditional neural networks, particularly in financial time series forecasting. We propose that the incorporation of DNM as the fundamental unit in artificial neural networks enhances computational capabilities, positioning it at the forefront of the next generation of deep learning technologies.
KW - Dendritic Neural Network
KW - Recurrent Neural Network
KW - Time Series Forecasting
UR - http://www.scopus.com/inward/record.url?scp=85186065741&partnerID=8YFLogxK
U2 - 10.1109/ITAIC58329.2023.10408923
DO - 10.1109/ITAIC58329.2023.10408923
M3 - 会議への寄与
AN - SCOPUS:85186065741
T3 - IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)
SP - 1285
EP - 1289
BT - IEEE ITAIC 2023 - IEEE 11th Joint International Information Technology and Artificial Intelligence Conference
A2 - Xu, Bing
A2 - Mou, Kefen
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023
Y2 - 8 December 2023 through 10 December 2023
ER -