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Recent advances in memory technologies, devices and materials have shown great potential for integration into neuromorphic electronic systems. However, a significant gap remains between the development of these materials and the realization…

Present day computers expend orders of magnitude more computational resources to perform various cognitive and perception related tasks that humans routinely perform everyday. This has recently resulted in a seismic shift in the field of…

Emerging Technologies · Computer Science 2017-12-22 Abhronil Sengupta , Kaushik Roy

Achieving brain-like density and performance in neuromorphic computers necessitates scaling down the size of nanodevices emulating neuro-synaptic functionalities. However, scaling nanodevices results in reduction of programming resolution…

Emerging Technologies · Computer Science 2023-03-14 A N M Nafiul Islam , Arnob Saha , Zhouhang Jiang , Kai Ni , Abhronil Sengupta

Analog electronic non-volatile memories mimicking synaptic operations are being explored for the implementation of neuromorphic computing systems. Compound synapses consisting of ensembles of stochastic binary elements are alternatives to…

Applied Physics · Physics 2019-10-02 Vaibhav Ostwal , Ramtin Zand , Ronald DeMara , Joerg Appenzeller

The term ``neuromorphic'' refers to systems that are closely resembling the architecture and/or the dynamics of biological neural networks. Typical examples are novel computer chips designed to mimic the architecture of a biological brain,…

Neural and Evolutionary Computing · Computer Science 2022-12-20 Dario Izzo , Alexander Hadjiivanov , Dominik Dold , Gabriele Meoni , Emmanuel Blazquez

The advent of memristors and resistive switching has transformed solid state physics, enabling advanced applications such as neuromorphic computing. Inspired by these developments, we introduce the concept of Mem-emitters, devices that…

Magnetic skyrmions have attracted considerable interest, especially after their recent experimental demonstration at room temperature in multilayers. The robustness, nanoscale size and non-volatility of skyrmions have triggered a…

Organic electrochemical transistors formed by in operando electropolymerization of the semiconducting channel are increasingly becoming recognized as a simple and effective implementation of synapses in neuromorphic hardware. However, very…

Memristors are promising next-generation memory candidates that are nonvolatile, possess low power requirements and are capable of nanoscale fabrication. In this article we physically realise and describe the use of organic memristors in…

Emerging Technologies · Computer Science 2012-12-17 Victor Erokhin , Gerard David Howard , Andrew Adamatzky

We show that memcapacitive (memory capacitive) systems can be used as synapses in artificial neural networks. As an example of our approach, we discuss the architecture of an integrate-and-fire neural network based on memcapacitive…

Disordered Systems and Neural Networks · Physics 2016-06-24 Y. V. Pershin , M. Di Ventra

Artificial neural networks and computational neuroscience models have made tremendous progress, allowing computers to achieve impressive results in artificial intelligence (AI) applications, such as image recognition, natural language…

Neural and Evolutionary Computing · Computer Science 2019-11-05 Giacomo Indiveri , Yulia Sandamirskaya

Living systems implement and execute an extraordinary plethora of computational tasks. The inherent degree of large scale coordination emerges as a global property, from the intricate sea of microscopic interactions. The brain, with its…

Disordered Systems and Neural Networks · Physics 2018-01-03 Duccio Fanelli , Francesco Ginelli , Roberto Livi , Niccolò Zagli , Clement Zankoc

Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are inspired by neurobiological systems, and this feature distinguishes neuromorphic systems from conventional computing systems. The…

Learning with physical systems is an emerging paradigm that seeks to harness the intrinsic nonlinear dynamics of physical substrates for learning. The impetus for a paradigm shift in how hardware is used for computational intelligence stems…

Disordered Systems and Neural Networks · Physics 2026-04-28 Francesco Caravelli , Gianluca Milano , Adam Z. Stieg , Carlo Ricciardi , Simon Anthony Brown , Zdenka Kuncic

Nanofluidic memristive devices work with nanoscale pores and ions dissolved in water, which harness the ionic memory effect aiming to store and process information. These devices share the same charge carriers as biological systems and…

Materials Science · Physics 2026-04-22 Wenzhe Zhou , Dongjiao Ge , Ao Zhang , Jincheng Xu , Yu Ji , Yiran Gong , Wenchang Zhang , Jidong Li , Li Lin , Zhiping Xu , Pengzhan Sun

Inspired by the dendritic integration and spiking operation of a biological neuron, flexible oxide-based neuron transistors gated by solid-state electrolyte films are fabricated on flexible plastic substrates for biochemical sensing…

Materials Science · Physics 2015-05-20 Ning Liu , Li Qiang Zhu , Ping Feng , Chang Jin Wan , Yang Hui Liu , Yi Shi , Qing Wan

Magnetic nanoparticles (MNPs) are the foundation of several new strategies for neural repair and neurological therapies. The fact that a remote force can act on MNPs at the cytoplasmic space constitutes the essence of many new…

Neurons and Cognition · Quantitative Biology 2024-02-22 Gerardo F. Goya , Vittoria Raffa

Neuromorphic computing and engineering has been the focus of intense research efforts that have been intensified recently by the mutation of Information and Communication Technologies (ICT). In fact, new computing solutions and new hardware…

Applied Physics · Physics 2018-10-09 Sebastien Pecqueur , Dominique Vuillaume , Fabien Alibart

Y-Flash memristors utilize the mature technology of single polysilicon floating gate non-volatile memories (NVM). It can be operated in a two-terminal configuration similar to the other emerging memristive devices, i.e., resistive…

Neuromorphic computing devices attempt to emulate features of biological nervous systems through mimicking the properties of synapses, towards implementing the emergent properties of their counterparts, such as learning. Inspired by recent…