一个基于局部信息理论目标函数的可解释神经学习的一般框架
Abdullah Makkeh1,2, Marcel Graetz1,3, Andreas C Schneider2,4,5
1Department of Data-driven Analysis of Biological Networks, Göttingen Campus Institute for Dynamics of Biological Networks, University of Göttingen, Göttingen 37077, Germany.
概括
研究人员通过从部分信息分解 (PID) 导出局部学习规则来开发"信息形态"神经网络. 这些网络提供了一种可解释的方法来理解局部神经元动力学如何推动网络层面的学习和任务执行.
科学领域:
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
- 信息理论 信息理论
背景情况:
- 了解生物和人工网络中的本地学习动态是一项挑战.
- 现有的方法缺乏一个一般的,可解释的和适应的框架,以满足当地的学习目标.
研究的目的:
- 根据部分信息分解 (PID) 来推导一个参数局部学习规则.
- 介绍和展示新型"信息形态"神经网络的功能.
主要方法:
- 制定了对分区模型神经元的局部信息处理目标.
- 使用部分信息分解 (PID) 的进步推导出了本地学习规则.
- 在各种学习任务中引入并测试了"信息形态"神经网络.
主要成果:
- 成功推导出一个参数局部学习规则.
- 在监督,无监督和记忆学习任务中展示了信息形态网络的多功能性.
- 展示了PID框架用于分析本地学习的可解释性.
结论:
- 信息形神经网络为研究本地学习提供了有价值的,可解释的工具.
- 这种方法有助于更好地理解地方动态如何为网络层面的解决方案做出贡献.
- 该框架可以适应各种学习任务和网络结构.
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