Associative memory or content-addressable memory is an important component function in computer science and information processing, and at the same time a key concept in cognitive and computational brain science. Many different neural network architectures and learning rules have been proposed to model the brain's associative memory while investigating key component functions like figure-ground segmentation, perceptual reconstruction and rivalry. A less investigated but equally important capability of associative memory is prototype extraction where the training set comprises distorted prototype instances and the task is to recall the correct generating prototype given a new distorted instance. In this paper we benchmark associative memory function of seven different Hebbian learning rules employed in non-modular and modular recurrent networks with winner-take-all dynamics operating on moderately sparse binary patterns. We measure pattern storage and weight information capacity, prototype extraction capabilities, and sensitivity to correlations in data. The original additive Hebb rule comes out with worst capacity, covariance learning proves to be robust but with moderate capacity, and the Bayesian-Hebbian learning rules show highest capacity in almost all different conditions tested.

翻译：联想记忆或内容可寻址记忆是计算机科学与信息处理中的重要组件功能，同时也是认知与计算脑科学中的核心概念。为模拟大脑的联想记忆能力，研究者提出了多种神经网络架构与学习规则，并探究了图形-背景分割、知觉重构及知觉竞争等关键组件功能。原型提取作为联想记忆的一个虽较少被研究但同等重要的能力，其任务要求训练集由带噪声的原型实例构成，最终需根据新出现的带噪声实例回忆出正确的生成原型。本文对七种不同Hebbian学习规则在非模块化与模块化循环网络中的联想记忆功能进行了基准测试，这些网络采用胜者全得动力学机制，并作用于中等稀疏度的二元模式。我们分别测量了模式存储容量、权重信息容量、原型提取能力以及对数据相关性的敏感度。结果表明：原始加性Hebb规则的表现容量最低；协方差学习虽稳健但容量中等；而贝叶斯-Hebbian学习规则在几乎所有测试条件下均展现出最高容量。