arXiv：cs.LG· Shuang Gao, Peter E. Caines·· 3 小时前
Transmission Neural Networks：抑制性与兴奋性连接
Transmission Neural Networks: Inhibitory and Excitatory Connections
AI 导读
Gao 和 Caines 提出的 Transmission Neural Network 模型被扩展至包含抑制性连接与神经递质群体，神经元状态为二值，建模同时涵盖传输动力学及兴奋、抑制两类连接。
正文
Abstract:This paper extends the Transmission Neural Network model proposed by Gao and Caines in [1]-[3] to incorporate inhibitory connections and neurotransmitter populations. The extended network model contains binary neuronal states, transmission dynamics, and inhibitory and excitatory connections. Under technical assumptions, we establish the characterization of the firing probabilities of neurons, and show that such a characterization considering inhibitions can be equivalently represented by a neural network where each neuron has a continuous state of dimension 2. Moreover, we incorporated neurotransmitter populations into the modeling and establish the limit network model when the number of neurotransmitters at all synaptic connections go to infinity. Finally, sufficient conditions for stability and contraction properties of the limit network model are established.
| Comments: | 8 pages |
| Subjects: | Social and Information Networks (cs.SI); Machine Learning (cs.LG); Systems and Control (eess.SY); Dynamical Systems (math.DS) |
| Cite as: | arXiv:2604.04246 [cs.SI] |
| (or arXiv:2604.04246v2 [cs.SI] for this version) | |
| https://doi.org/10.48550/arXiv.2604.04246 arXiv-issued DOI via DataCite |
Submission history
From: Shuang Gao [view email]
[v1]
Sun, 5 Apr 2026 20:07:46 UTC (241 KB)
[v2]
Thu, 8 Oct 2026 02:50:37 UTC (175 KB)
来源：arXiv：cs.LG · arxiv.org