General Neurodynamic Associative Memory Model

尽管记忆是我们大多数人每天都不费吹灰之力就能完成的事情，但它实际上是一项极其复杂的任务。例如，存储和检索对象的简单行为是我们还没有设法通过编程让机器达到接近人类效率和健壮性的事情。*我的研究旨在更好地理解人类认知系统如何完成从模式中创建（和增强）表示的复杂任务，以及识别、识别、分类和分类它们。特别是，本研究侧重于一个有趣的非线性动力学系统的观点，其中时间和变化是关键变量。在该透视图中，内存被表示为系统的不变状态。*为了理解人类认知系统是如何工作的，我们需要建立正式的模型。如今，没有正式的模型可以在不损害简单性和自一致性的情况下考虑到这种行为的多样性。在本研究中，我们将使用并行的人工神经网络，其中信息分布在单元之间。由于大脑使用相似的计算单元和机制来完成不同的任务，因此该方法使用通用的双向联想记忆模型作为通用认知架构的构建块。本研究旨在使用一个独特的模型来考虑无监督、无监督和强化学习。该模型必须具有灵活性，以适应神经科学和认知约束。希望通过发展一种通用的双向联想记忆，我们将更好地理解大脑是如何工作的，以及如何将这种理解转化为计算智能

Although it is something most of us do every day without effort, memorizing is in fact an incredibly complex task. For instance, the simple act of storing and retrieving an object is something we have not yet managed to program a machine to do with anything approaching human efficiency and robustness. * My research aims to better understand how human cognitive system accomplishes the complex task of create (and enhance) representation from patterns as well as recognize, identify, categorize and classify them. In particular, this research focuses on an interesting nonlinear dynamics system perspective where time and change are the key variables. Within that perspective memory are represented as invariant states of the system.* To understand how the human cognitive system works, we need to develop formal models. Nowadays, no formal models can take into account this variety of behavior without scarifying simplicity and self-consistency. In this research, we will use artificial neural networks that are parallel and where information is distributed among the units. Since the brain uses similar computing units and mechanisms for different tasks, this approach uses a general bidirectional associative memory model as building blocks for a general cognitive architecture. This research aims to take into account unsupervised, unsupervised and reinforcement learning using a unique model. The model must have the flexibility to adapt itself in regards to neuroscience and cognition constraints. It is hoped that by developing a general bidirectional associative memory we will have a better understanding on how the mind works and how this understanding can be translated into computational intelligence