Plasticity in NEUral Memristive Architectures

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都在努力在神经生物学的框架内为意识提供权威的定义。工程师们最近加入了这一探索，开发了模拟生物功能的神经形态VLSI电路。然而，迄今为止，人工系统还不能忠实地再现自然属性，如真正的处理局部性（内存和计算）和复杂性（每平方厘米10^10个突触），阻止实现长期目标：自主认知系统的创建。该项目旨在开发能够感知，学习和适应刺激，利用五个领先的欧洲机构在神经科学，纳米技术，建模和电路设计的最新发展。新发现的忆阻器的非线性动力学以及可塑性被证明支持基于尖峰和尖峰定时依赖可塑性（STDP），使得这种极其紧凑的设备成为实现大规模自适应电路的绝佳候选者;迈向“自主认知系统”的一步。真实的神经元和突触的固有特性以及它们在形成神经电路中的组织将被利用来优化基于CMOS的神经元、忆阻网格以及将两者整合到实时生物物理学上真实的神经形态系统中。最后，将用传统和抽象的方法对平台进行测试，以评估技术及其自主能力。

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.