Memory Consolidation in Memristive Systems

关键目标这项研究将把计算神经科学理论与新的记忆技术结合起来，开发受大脑启发的神经系统，表现出长寿命的记忆巩固和回忆。人们最初的兴趣是发现当前机器学习理论在冯-诺伊曼体系结构的低效和它们所采用的静态记忆方法方面的局限性。这个项目的主要目标是实现一种新技术，这种技术具有永远在线、永远学习的能力，并且可以与其他物理和可编程系统集成，与物联网社会一起流动。工程方法论应研究自下而上的方法来开发硅胶中的神经电路。我的目标是建立在“记忆融合”概念的基础上，实现人工突触模型，这种模型表现出超塑性，因此能够动态存储具有内在时间基准的信息。这种技术的理论模型将在Python语言中完成，制造的器件将使用Arc Instruments分析工具进行测试。成功的模型将被大量集成，以开发具有整合和召回功能的有源存储器件。量化个体记忆的理论工作是在存储中实现最大模块层次结构的基本任务。由部分相同信息组成的多个记忆应该与相应的预先存在的突触相关联，其有效性应该得到重新加强。在这类设备中，记忆整合的拓扑结构最受关注，因为它可以在信息输入和推理之间建立新的桥梁。贝叶斯统计等数学模型将用于研究上述硬件记忆召回方法。通过随机组合突触信息来创造和存储新的记忆，系统性能可以被设计成展示新的“创造性”方法。这样的改进可能是人工自动化系统多概念问题解决能力的显著飞跃。应用这项研究将完全与智能社会和更普遍的人工智能的趋势同步。潜在的影响是巨大的，因为成功的结果可以为计算开辟新的途径，最终克服当前内存操作的静态限制。

Key objectivesThis research will combine computational neuroscience theory with novel memristive technologies for the development of brain-inspired neural systems that exhibit long-life memory consolidation and recall. Initial interest has risen from identifying the limitations of current machine-learning theory with respect to both inefficiencies of von-Neumann architecture and the static memory methods they employ. The main goal for this project is the realisation of a new technology that exhibits always-on, always-learning capabilities and can be integrated to other physical and programmable systems, in flow with the Internet of Things society.Engineering MethodologyA bottom-up approach shall be examined to develop neural circuits in-Silico. I aim to build on the "memristive fuse" concept to realise artificial synaptic models that exhibit meta-plasticity and are therefore able to dynamically store information with an intrinsic time reference. Theoretical modelling of such technology will be done in Python and fabricated devices will be tested using Arc Instruments analysis tools.Successful models shall be integrated in numbers, to develop active memory devices exhibiting consolidation and recall. Theoretical work on quantifying individual memories is an essential task to achieve maximum modular hierarchy in storage. Multiple memories consisting partially of the same information should be associated to corresponding pre-existing synapses, whose efficacies should be re-enforced. The topology of memory consolidation in such devices is of most interest since it can create new bridges between information input and reasoning.Mathematical models such as Bayesian statistics will be used to study the mentioned hardware memory recall methods. System performance can be designed to exhibit new "creative" methods by stochastically combining synaptic information to create and store new memories. Such improvement can be a significantly leap in the multi-concept problem-solving abilities of artificial automated systems.ApplicationsThis research will be in full sync with the trend towards a smart society and more general artificial intelligence. The potential impact is vast since successful results can open new paths for computation, finally overcoming the static limitations of current memory manipulation.