Neuromorphic spiking information processing based on neuron-like excitable effect has achieved rapid development in recent years due to its advantages such as ultra-high operation speed, programming-free implementation and low power consumption. However, the current physical platforms lack building blocks like compilers, logic gates, and more importantly, data memory. These factors become the shackles to construct a full-physical layer neural network. In this paper, a neuromorphic regenerative memory scheme is proposed based on a time-delayed broadband nonlinear optoelectronic oscillator (OEO), which enables reshaping and regenerating on-off keying encoding sequences. Through biasing the dual-drive Mach-Zehnder electro-optic modulator in the OEO cavity near its minimum transmission point, the OEO can work in excitable regime, where localized states are maintained for robust nonlinear spiking response. Both simulation and experiment are carried out to demonstrate the proposed scheme, where the simulation results and the experimental results fit in with each other. The proposed OEO-based neuromorphic regenerative memory scheme exhibits long-term response ability for short-term excitation, which shows an enormous application potential for high-speed neuromorphic information buffering, optoelectronic interconnection and computing.
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http://dx.doi.org/10.1364/OE.495015 | DOI Listing |
Sci Rep
October 2024
Department of Neuroscience, Developmental and Regenerative Biology, The University of Texas at San Antonio, San Antonio, TX, 78349, USA.
Mater Today Bio
October 2024
Department of Geriatric Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710061, China.
Thanks to its structural characteristics and signal patterns similar to those of human brain synapses, memristors are widely believed to be applicable for neuromorphic computing. However, to our knowledge, memristors have not been effectively applied in the biomedical field, especially in disease diagnosis and health monitoring. In this work, a blood-based biomemristor was prepared for in vitro detection of hyperglycemia and hyperlipidemia.
View Article and Find Full Text PDFSci Rep
March 2024
Department of Neuroscience, Developmental and Regenerative Biology, The University of Texas at San Antonio, San Antonio, TX, 78349, USA.
There is an increasing need to implement neuromorphic systems that are both energetically and computationally efficient. There is also great interest in using electric elements with memory, memelements, that can implement complex neuronal functions intrinsically. A feature not widely incorporated in neuromorphic systems is history-dependent action potential time adaptation which is widely seen in real cells.
View Article and Find Full Text PDFBiomicrofluidics
January 2024
Department of Mechanical Engineering, Sogang University, 35 Baekbeom-ro (Sinsu-dong), Mapo-gu, Seoul 04107, Republic of Korea.
Green energy conversion in aqueous systems has attracted considerable interest owing to the sustainable clean energy demand resulting from population and economic growth and urbanization, as well as the significant potential energy from water resources and other regenerative sources coupled with fluids. In particular, molecular motion based on intrinsic micro/nanofluidic phenomena at the liquid-solid interface (LSI) is crucial for efficient and sustainable green energy conversion. The electrical double layer is the main factor affecting transport, interaction between molecules and surfaces, non-uniform ion distribution, synthesis, stimulated reactions, and motion by external renewable resources in both closed nanoconfinement and open surfaces.
View Article and Find Full Text PDFAdv Mater
April 2024
National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China.
In the era of information, characterized by an exponential growth in data volume and an escalating level of data abstraction, there has been a substantial focus on brain-like chips, which are known for their robust processing power and energy-efficient operation. Memristors are widely acknowledged as the optimal electronic devices for the realization of neuromorphic computing, due to their innate ability to emulate the interconnection and information transfer processes witnessed among neurons. This review paper focuses on memristor-based neuromorphic chips, which provide an extensive description of the working principle and characteristic features of memristors, along with their applications in the realm of neuromorphic chips.
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