使用矢量符号架构建模型的神经概率计算模型
P Michael Furlong1, Chris Eliasmith1
1Centre for Theoretical Neuroscience, University of Waterloo, 200 University Ave., Waterloo, ON N2L 3G1 Canada.
Cognitive neurodynamics
|December 23, 2024
概括
本研究探讨了使用尖端神经网络在矢量符号架构 (VSAs) 中的建模不确定性. 我们展示了VSA如何可以执行用于认知建模的概率运算,从而增强计算神经科学.
科学领域:
- 认知科学 认知科学
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
背景情况:
- 分布式向量表示方式是连接主义者和象征性AI之间的桥梁.
- 在这些系统中模拟不确定性仍然是一个挑战.
- 矢量符号架构 (VSA) 为符号表示提供了一个框架.
研究的目的:
- 为了证明VSA的神经实现如何可以执行概率运算.
- 展示这些操作对于构建认知模型是如何有用的.
- 探索基于VSA的概率与量子概率之间的关系.
主要方法:
- 解释与概率分布相关的VSA符号捆绑.
- 在空间语义指针 (SSP) 上使用相似运算符进行密度估计.
- 设计尖端神经网络以计算和相互信息.
主要成果:
- 在VSA中的分数结合会诱导密度估计的准核函数.
- 新型尖端神经网络成功计算和相互信息.
- 提出的方法对不确定性的认知建模有希望.
结论:
- VSA的神经实现的尖端可以有效地模拟不确定性.
- 这种方法为将概率推理整合到认知架构中提供了一种新的方式.
- 这些方法在各种VSA框架中可能具有普遍性.
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