General Neurodynamic Associative Memory Model

尽管我们大多数人每天都在不费吹灰之力地记忆，但事实上，记忆是一项令人难以置信的复杂任务。例如，存储和检索对象的简单行为是我们还没有成功地对机器进行编程来实现任何接近人类效率和健壮性的操作。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)表征以及识别、识别、分类和分类的复杂任务。特别是，这项研究集中在一个有趣的非线性动力学系统的角度，其中时间和变化是关键变量。在该透视图内，存储器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，我们需要开发正式的模型。如今，任何正式的模型都无法在不考虑简单性和自我一致性的情况下考虑到这种行为的多样性。在这项研究中，我们将使用人工神经网络，这些网络是并行的，信息在单元之间分配。由于大脑对不同的任务使用类似的计算单元和机制，因此该方法使用一般的双向联想记忆模型作为一般认知架构的构建块。本研究旨在使用一种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限制。希望通过开发一种通用的双向联想记忆，我们将对大脑如何工作以及如何将这种理解转化为计算智能有更好的理解

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