Nanoelektronik, Technische Fakultät, Christian-Albrechts-Universität zu Kiel, D-24143, Kiel, Germany.
Department of Neurology, Memory Disorders and Plasticity Group, University Hospital Schleswig-Holstein, Kiel, Germany.
Sci Rep. 2018 Jun 19;8(1):9367. doi: 10.1038/s41598-018-27616-6.
Memristive systems have gained considerable attention in the field of neuromorphic engineering, because they allow the emulation of synaptic functionality in solid state nano-physical systems. In this study, we show that memristive behavior provides a broad working framework for the phenomenological modelling of cellular synaptic mechanisms. In particular, we seek to understand how close a memristive system can account for the biological realism. The basic characteristics of memristive systems, i.e. voltage and memory behavior, are used to derive a voltage-based plasticity rule. We show that this model is suitable to account for a variety of electrophysiology plasticity data. Furthermore, we incorporate the plasticity model into an all-to-all connecting network scheme. Motivated by the auto-associative CA3 network of the hippocampus, we show that the implemented network allows the discrimination and processing of mnemonic pattern information, i.e. the formation of functional bidirectional connections resulting in the formation of local receptive fields. Since the presented plasticity model can be applied to real memristive devices as well, the presented theoretical framework can support both, the design of appropriate memristive devices for neuromorphic computing and the development of complex neuromorphic networks, which account for the specific advantage of memristive devices.
忆阻系统在神经形态工程领域引起了相当大的关注，因为它们允许在固态纳米物理系统中模拟突触功能。在这项研究中，我们表明忆阻行为为细胞突触机制的现象建模提供了广泛的工作框架。具体来说，我们试图了解忆阻系统在多大程度上可以模拟生物现实。忆阻系统的基本特征，即电压和记忆行为，用于推导出基于电压的可塑性规则。我们表明，该模型适合解释各种电生理学可塑性数据。此外，我们将该可塑性模型整合到全连接网络方案中。受海马体 CA3 网络的自联想启发，我们表明，所实现的网络允许对记忆模式信息进行区分和处理，即形成功能性双向连接，从而形成局部感受野。由于所提出的可塑性模型也可以应用于实际的忆阻器件，因此所提出的理论框架可以支持为神经形态计算设计合适的忆阻器件以及开发复杂的神经形态网络，从而利用忆阻器件的特定优势。
