ABSTRACT

The work presents a concept of an implementation of an explainable artificial intelligence (XAI) using effective models of third-generation neurons. The article discusses a concept of building a neural network based on spiking neurons modelled on ladder nervous systems. A distinction is made between voltage signals encoding information in a network and current signals which contain the correlation between information in the network and pattern features. Analyzes feature a neuron model based on the cusp catastrophe theory eliminating network sensitivity to problems of synapse plasticity, weight mismatch and coupling of neurons based on electric models. The paper presents applications of a spiking neural network for reporting the state of water quality while generating justifications. The article contains results of an analysis of confusion of justifications with ACC = 1 for a set of 10,000 patterns. It also discusses the speed of pattern analysis in the simulated network.

摘要

这项工作提出了使用第三代神经元的有效模型实施可解释人工智能 (XAI) 的概念。本文讨论了基于阶梯神经系统建模的尖峰神经元构建神经网络的概念。区分网络中编码信息的电压信号和包含网络中信息与模式特征之间相关性的电流信号。分析的特点是基于尖点突变理论的神经元模型消除了网络对突触可塑性、权重不匹配和基于电模型的神经元耦合问题的敏感性。本文介绍了尖峰神经网络在报告水质状态同时生成理由的应用。这篇文章包含对一组 10,000 个模式的 ACC = 1 的理由混淆的分析结果。它还讨论了模拟网络中模式分析的速度。