Toward Next-Generation Biologically Plausible Single Neuron Modeling: An Evolutionary Dendritic Neuron Model
Conventional deep learning models rely heavily on the McCulloch–Pitts (MCP) neuron, limiting their interpretability and biological plausibility. The Dendritic Neuron Model (DNM) offers a more realistic alternative by simulating nonlinear and compartmentalized processing within dendritic branches, en...
Saved in:
| Main Authors: | Chongyuan Wang, Huiyi Liu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/9/1465 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Modeling Electrical Potential in Multi-Dendritic Neurons Using Bessel Functions
by: Kaouther Selmi, et al.
Published: (2025-03-01) -
What Does It Mean to be "Plausible"?
by: Christian Dahlman
Published: (2024-06-01) -
Research on SNN Learning Algorithms and Networks Based on Biological Plausibility
by: Bingqiang Huo, et al.
Published: (2025-01-01) -
Quantitative analysis of dendritic branching pattern of large neurons in human cerebellum
by: Milošević Nebojša T., et al.
Published: (2010-01-01) -
Learning Dendritic-Neuron-Based Motion Detection for RGB Images: A Biomimetic Approach
by: Tianqi Chen, et al.
Published: (2024-12-01)