Purin：一种受生物学启发的人工神经网络机制

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Abstract:Artificial neural networks (ANNs) usually represent neural transmission with fixed trainable weights during a training batch, which omits short-term changes in synaptic efficacy. In addition, the discrete time-step simulation requires additional temporal processing that many conventional ANN architectures do not use. To overcome these challenges, we propose Purin, a biology-inspired and ANN-compatible mechanism, that introduces synaptic efficacy modulation into conventional convolutional neural networks. Purin uses a time-interval-based abstraction for neural activities, which allows Purin to introduce short- and long-term synaptic efficacy changes without using discrete time-steps. Purin introduces a bounded factor to represent temporary synaptic efficacy changes, together with two weight matrices that represent input-side and output-side efficacy. The weight matrices are updated by backpropagation and interpreted as the long-term synaptic efficacy changes. Experimental results show that after removing the confounding factors in the AlexNet, VGG11, and GoogLeNet architectures, Purin improves the classification accuracies in all three models across the evaluated datasets.

Comments:	8 pages, 2 figures, 8 tables
Subjects:	Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
MSC classes:	68T05
ACM classes:	I.2.6
Cite as:	arXiv:2609.31235 [cs.AI]
	(or arXiv:2609.31235v2 [cs.AI] for this version)
	https://doi.org/10.48550/arXiv.2609.31235 arXiv-issued DOI via DataCite

Submission history

From: Zishu Liu [view email]
[v1] Fri, 25 Sep 2026 13:15:22 UTC (919 KB)
[v2] Mon, 9 Nov 2026 21:21:07 UTC (1,307 KB)