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Plasticity in NEUral Memristive Architectures

神经忆阻架构中的可塑性

基本信息

批准号：
EP/J00801X/2
负责人：
Themis Prodromakis
金额：
$ 27.81万
依托单位：
University of Southampton
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2013
资助国家：
英国
起止时间：
2013 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FJ00801X%2F2
关键词：
Plasticity NEUral Memristive Architectures

项目摘要

During the past two decades, philosophers, psychologists, cognitive scientists, clinicians and neuroscientists strived to provide authoritative definitions of consciousness within a neurobiological framework. Engineers have more recently joined this quest by developing neuromorphic VLSI circuits for emulating biological functions. Yet, to date artificial systems have not been able to faithfully recreate natural attributes such as true processing locality (memory and computation) and complexity (10^10 synapses per cm2), preventing the achievement of a long-term goal: the creation of autonomous cognitive systems.This project aspires to develop experimental platforms capable of perceiving, learning and adapting to stimuli by leveraging on the latest developments of five leading European institutions in neuroscience, nanotechnology, modeling and circuit design. The non-linear dynamics as well as the plasticity of the newly discovered memristor are shown to support Spike-based- and Spike-Timing-Dependent-Plasticity (STDP), making this extremely compact device an excellent candidate for realizing large-scale self-adaptive circuits; a step towards "autonomous cognitive systems". The intrinsic properties of real neurons and synapses as well as their organization in forming neural circuits will be exploited for optimising CMOS-based neurons, memristive grids and the integration of the two into realtime biophysically realistic neuromorphic systems. Finally, the platforms would be tested with conventional as well as abstract methods to evaluate the technology and its autonomous capacity.

在过去的二十年里，哲学家、心理学家、认知科学家、临床医生和神经科学家努力在神经生物学框架内为意识提供权威的定义。工程师们最近也加入了这一探索，开发了用于模拟生物功能的神经形态VLSI电路。然而，到目前为止，人工系统还不能忠实地再现真实的处理局部性(记忆和计算)和复杂性(每平方厘米10个突触)等自然属性，阻碍了一个长期目标的实现：创建自主认知系统。该项目渴望通过利用五家领先的欧洲机构在神经科学、纳米技术、建模和电路设计方面的最新发展，开发能够感知、学习和适应刺激的实验平台。新发现的忆阻器的非线性动力学和可塑性被证明支持基于尖峰和尖峰时序的可塑性(STDP)，使这种极其紧凑的器件成为实现大规模自适应电路的极佳候选者；朝着“自主认知系统”迈出了一步。真实神经元和突触的内在属性以及它们在形成神经电路中的组织将被用于优化基于CMOS的神经元、记忆网格以及将两者集成到实时生物物理现实神经形态系统中。最后，这些平台将用常规和抽象的方法进行测试，以评估技术及其自主能力。