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Journal articles on the topic 'Neuromorphic platform'

Author: Grafiati

Published: 14 March 2023

Last updated: 31 July 2025

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1

Urgese, Gianvito, Francesco Barchi, Emanuele Parisi, Evelina Forno, Andrea Acquaviva, and Enrico Macii. "Benchmarking a Many-Core Neuromorphic Platform With an MPI-Based DNA Sequence Matching Algorithm." Electronics 8, no. 11 (2019): 1342. http://dx.doi.org/10.3390/electronics8111342.

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SpiNNaker is a neuromorphic globally asynchronous locally synchronous (GALS) multi-core architecture designed for simulating a spiking neural network (SNN) in real-time. Several studies have shown that neuromorphic platforms allow flexible and efficient simulations of SNN by exploiting the efficient communication infrastructure optimised for transmitting small packets across the many cores of the platform. However, the effectiveness of neuromorphic platforms in executing massively parallel general-purpose algorithms, while promising, is still to be explored. In this paper, we present an implem

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Perez-Peña, Fernando, M. Angeles Cifredo-Chacon, and Angel Quiros-Olozabal. "Digital neuromorphic real-time platform." Neurocomputing 371 (January 2020): 91–99. http://dx.doi.org/10.1016/j.neucom.2019.09.004.

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Chen, Guang, Jian Cao, Chenglong Zou, et al. "PAIBoard: A Neuromorphic Computing Platform for Hybrid Neural Networks in Robot Dog Application." Electronics 13, no. 18 (2024): 3619. http://dx.doi.org/10.3390/electronics13183619.

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Hybrid neural networks (HNNs), integrating the strengths of artificial neural networks (ANNs) and spiking neural networks (SNNs), provide a promising solution towards generic artificial intelligence. There is a prevailing trend towards designing unified SNN-ANN paradigm neuromorphic computing chips to support HNNs, but developing platforms to advance neuromorphic computing systems is equally essential. This paper presents the PAIBoard platform, which is designed to facilitate the implementation of HNNs. The platform comprises three main components: the upper computer, the communication module,

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Russo, Nicola, Haochun Huang, Eugenio Donati, Thomas Madsen, and Konstantin Nikolic. "An Interface Platform for Robotic Neuromorphic Systems." Chips 2, no. 1 (2023): 20–30. http://dx.doi.org/10.3390/chips2010002.

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Neuromorphic computing is promising to become a future standard in low-power AI applications. The integration between new neuromorphic hardware and traditional microcontrollers is an open challenge. In this paper, we present an interface board and a communication protocol that allows communication between different devices, using a microcontroller unit (Arduino Due) in the middle. Our compact printed circuit board (PCB) links different devices as a whole system and provides a power supply for the entire system using batteries as the power supply. Concretely, we have connected a Dynamic Vision

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Neumann, Adam. "Advancements in Unsupervised Learning: Mode-Assisted Quantum Restricted Boltzmann Machines Leveraging Neuromorphic Computing on the Dynex Platform." International Journal of Bioinformatics and Intelligent Computing 3, no. 1 (2024): 91–103. http://dx.doi.org/10.61797/ijbic.v3i1.300.

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The integration of neuromorphic computing into the Dynex platform signifies a transformative step in computational technology, particularly in the realms of machine learning and optimization. This advanced platform leverages the unique attributes of neuromorphic dynamics, utilizing neuromorphic annealing - a technique divergent from conventional computing methods - to adeptly address intricate problems in discrete optimization, sampling, and machine learning. Our research concentrates on enhancing the training process of Restricted Boltzmann Machines (RBMs), a category of generative models tra

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Wang, Junyi. "A Review of Spiking Neural Networks." SHS Web of Conferences 144 (2022): 03004. http://dx.doi.org/10.1051/shsconf/202214403004.

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Spiking neuron network (SNN) attaches much attention to researchers in neuromorphic engineering and brain-like computing because of its advantages in Spatio-temporal dynamics, diverse coding mechanisms, and event-driven properties. This paper is a review of SNN in order to help researchers from other areas to know and became familiar with the field of SNN or even became interested in SNN. Neuron models, coding methods, training algorithms, and neuromorphic computing platforms will be introduced in this paper. This paper analyzes the disadvantages and advantages of several kinds of neural model

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Al Abdul Wahid, Seham, Arghavan Asad, and Farah Mohammadi. "A Survey on Neuromorphic Architectures for Running Artificial Intelligence Algorithms." Electronics 13, no. 15 (2024): 2963. http://dx.doi.org/10.3390/electronics13152963.

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Neuromorphic computing, a brain-inspired non-Von Neumann computing system, addresses the challenges posed by the Moore’s law memory wall phenomenon. It has the capability to enhance performance while maintaining power efficiency. Neuromorphic chip architecture requirements vary depending on the application and optimising it for large-scale applications remains a challenge. Neuromorphic chips are programmed using spiking neural networks which provide them with important properties such as parallelism, asynchronism, and on-device learning. Widely used spiking neuron models include the Hodgkin–Hu

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Zhai, Yongbiao, Peng Xie, Jiahui Hu, et al. "Reconfigurable 2D-ferroelectric platform for neuromorphic computing." Applied Physics Reviews 10, no. 1 (2023): 011408. http://dx.doi.org/10.1063/5.0131838.

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To meet the requirement of data-intensive computing in the data-explosive era, brain-inspired neuromorphic computing have been widely investigated for the last decade. However, incompatible preparation processes severely hinder the cointegration of synaptic and neuronal devices in a single chip, which limited the energy-efficiency and scalability. Therefore, developing a reconfigurable device including synaptic and neuronal functions in a single chip with same homotypic materials and structures is highly desired. Based on the room-temperature out-of-plane and in-plane intercorrelated polarizat

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Boldman, Walker L., Cheng Zhang, Thomas Z. Ward, et al. "Programmable Electrofluidics for Ionic Liquid Based Neuromorphic Platform." Micromachines 10, no. 7 (2019): 478. http://dx.doi.org/10.3390/mi10070478.

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Due to the limit in computing power arising from the Von Neumann bottleneck, computational devices are being developed that mimic neuro-biological processing in the brain by correlating the device characteristics with the synaptic weight of neurons. This platform combines ionic liquid gating and electrowetting for programmable placement/connectivity of the ionic liquid. In this platform, both short-term potentiation (STP) and long-term potentiation (LTP) are realized via electrostatic and electrochemical doping of the amorphous indium gallium zinc oxide (aIGZO), respectively, and pulsed bias m

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Tang, Jianbin, Benjamin Scott Mashford, and Antonio Jimeno Yepes. "Semantic Labeling Using a Low-Power Neuromorphic Platform." IEEE Geoscience and Remote Sensing Letters 15, no. 8 (2018): 1184–88. http://dx.doi.org/10.1109/lgrs.2018.2834522.

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Bose, Saurabh K., Joshua B. Mallinson, Edoardo Galli, et al. "Neuromorphic behaviour in discontinuous metal films." Nanoscale Horizons 7, no. 4 (2022): 437–45. http://dx.doi.org/10.1039/d1nh00620g.

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Discontinuous metal films, comprising nanoscale gold islands, exhibit correlated avalanches of electrical signals that mimic those observed in the cortex, providing an interesting platform for brain-inspired computing.

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Sugiarto, Indar, and Felix Pasila. "Understanding a Deep Learning Technique through a Neuromorphic System a Case Study with SpiNNaker Neuromorphic Platform." MATEC Web of Conferences 164 (2018): 01015. http://dx.doi.org/10.1051/matecconf/201816401015.

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Deep learning (DL) has been considered as a breakthrough technique in the field of artificial intelligence and machine learning. Conceptually, it relies on a many-layer network that exhibits a hierarchically non-linear processing capability. Some DL architectures such as deep neural networks, deep belief networks and recurrent neural networks have been developed and applied to many fields with incredible results, even comparable to human intelligence. However, many researchers are still sceptical about its true capability: can the intelligence demonstrated by deep learning technique be applied

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Petrov, A., L. Alekseeva, A. Ivanov, et al. "On the way to a neuromorphic memristor computer platform." Nanoindustry Russia, no. 1 (2016): 94–109. http://dx.doi.org/10.22184/1993-8578.2016.63.1.94.109.

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Ghani, Arfan, Thomas Dowrick, and Liam J. McDaid. "OSPEN: an open source platform for emulating neuromorphic hardware." International Journal of Reconfigurable and Embedded Systems (IJRES) 12, no. 1 (2023): 1. http://dx.doi.org/10.11591/ijres.v12.i1.pp1-8.

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This paper demonstrates a framework that entails a bottom-up approach to accelerate research, development, and verification of neuro-inspired sensing devices for real-life applications. Previous work in neuromorphic engineering mostly considered application-specific designs which is a strong limitation for researchers to develop novel applications and emulate the true behaviour of neuro-inspired systems. Hence to enable the fully parallel brain-like computations, this paper proposes a methodology where a spiking neuron model was emulated in software and electronic circuits were then implemente

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Arfan, Ghani, Dowrick Thomas, and J. McDaid Liam. "OSPEN: an open source platform for emulating neuromorphic hardware." International Journal of Reconfigurable and Embedded Systems (IJRES) 12, no. 1 (2023): 1–8. https://doi.org/10.11591/ijres.v12.i1.pp1-8.

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This paper demonstrates a framework that entails a bottom-up approach to accelerate research, development, and verification of neuro-inspired sensing devices for real-life applications. Previous work in neuromorphic engineering mostly considered application-specific designs which is a strong limitation for researchers to develop novel applications and emulate the true behaviour of neuro-inspired systems. Hence to enable the fully parallel brain-like computations, this paper proposes a methodology where a spiking neuron model was emulated in software and electronic circuits were then implemente

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Russo, Nicola, Thomas Madsen, and Konstantin Nikolic. "An Implementation of Communication, Computing and Control Tasks for Neuromorphic Robotics on Conventional Low-Power CPU Hardware." Electronics 13, no. 17 (2024): 3448. http://dx.doi.org/10.3390/electronics13173448.

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Bioinspired approaches tend to mimic some biological functions for the purpose of creating more efficient and robust systems. These can be implemented in both software and hardware designs. A neuromorphic software part can include, for example, Spiking Neural Networks (SNNs) or event-based representations. Regarding the hardware part, we can find different sensory systems, such as Dynamic Vision Sensors, touch sensors, and actuators, which are linked together through specific interface boards. To run real-time SNN models, specialised hardware such as SpiNNaker, Loihi, and TrueNorth have been i

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Alvarez-Canchila, Oscar I., Andres Espinal, Alberto Patiño-Saucedo, and Horacio Rostro-Gonzalez. "Optimizing Reservoir Separability in Liquid State Machines for Spatio-Temporal Classification in Neuromorphic Hardware." Journal of Low Power Electronics and Applications 15, no. 1 (2025): 4. https://doi.org/10.3390/jlpea15010004.

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In this paper, we propose an optimization approach using Particle Swarm Optimization (PSO) to enhance reservoir separability in Liquid State Machines (LSMs) for spatio-temporal classification in neuromorphic systems. By leveraging PSO, our method fine-tunes reservoir parameters, neuron dynamics, and connectivity patterns, maximizing separability while aligning with the resource constraints typical of neuromorphic hardware. This approach was validated in both software (NEST) and on neuromorphic hardware (SpiNNaker), demonstrating notable results in terms of accuracy and low energy consumption w

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Bradley, H., S. Louis, C. Trevillian, et al. "Artificial neurons based on antiferromagnetic auto-oscillators as a platform for neuromorphic computing." AIP Advances 13, no. 1 (2023): 015206. http://dx.doi.org/10.1063/5.0128530.

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Spiking artificial neurons emulate the voltage spikes of biological neurons and constitute the building blocks of a new class of energy efficient, neuromorphic computing systems. Antiferromagnetic materials can, in theory, be used to construct spiking artificial neurons. When configured as a neuron, the magnetization in antiferromagnetic materials has an effective inertia that gives them intrinsic characteristics that closely resemble biological neurons, in contrast with conventional artificial spiking neurons. It is shown here that antiferromagnetic neurons have a spike duration on the order

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Vanarse, Anup, Adam Osseiran, Alexander Rassau, and Peter van der Made. "Application of Neuromorphic Olfactory Approach for High-Accuracy Classification of Malts." Sensors 22, no. 2 (2022): 440. http://dx.doi.org/10.3390/s22020440.

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Current developments in artificial olfactory systems, also known as electronic nose (e-nose) systems, have benefited from advanced machine learning techniques that have significantly improved the conditioning and processing of multivariate feature-rich sensor data. These advancements are complemented by the application of bioinspired algorithms and architectures based on findings from neurophysiological studies focusing on the biological olfactory pathway. The application of spiking neural networks (SNNs), and concepts from neuromorphic engineering in general, are one of the key factors that h

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Devyatisil’nyi, A. S. "System for neuromorphic estimation of rotation of a mobile technological platform." Technical Physics 58, no. 7 (2013): 946–49. http://dx.doi.org/10.1134/s1063784213070050.

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Kamsma, T. M., E. A. Rossing, C. Spitoni, and R. van Roij. "Advanced iontronic spiking modes with multiscale diffusive dynamics in a fluidic circuit." Neuromorphic Computing and Engineering 4, no. 2 (2024): 024003. http://dx.doi.org/10.1088/2634-4386/ad40ca.

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Abstract Fluidic iontronics is emerging as a distinctive platform for implementing neuromorphic circuits, characterised by its reliance on the same aqueous medium and ionic signal carriers as the brain. Drawing upon recent theoretical advancements in both iontronic spiking circuits and in dynamic conductance of conical ion channels, which form fluidic memristors, we expand the repertoire of proposed neuronal spiking dynamics in iontronic circuits. Through a modelled circuit containing channels that carry a bipolar surface charge, we extract phasic bursting, mixed-mode spiking, tonic bursting,

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Stock, Raphael, Jakob Kaiser, Eric Müller, Johannes Schemmel, and Sebastian Schmitt. "Parametrizing analog multi-compartment neurons with genetic algorithms." Open Research Europe 3 (November 14, 2024): 144. http://dx.doi.org/10.12688/openreseurope.15775.2.

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Background Finding appropriate model parameters for multi-compartmental neuron models can be challenging. Parameters such as the leak and axial conductance are not always directly derivable from neuron observations but are crucial for replicating desired observations. The objective of this study is to replicate the attenuation behavior of an excitatory postsynaptic potential (EPSP) traveling along a linear chain of compartments on the analog BrainScaleS-2 neuromorphic hardware platform. Methods In the present publication we use genetic algorithms to find suitable model parameters. They promise

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Stock, Raphael, Jakob Kaiser, Eric Müller, Johannes Schemmel, and Sebastian Schmitt. "Parametrizing analog multi-compartment neurons with genetic algorithms." Open Research Europe 3 (September 8, 2023): 144. http://dx.doi.org/10.12688/openreseurope.15775.1.

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Background: Finding appropriate model parameters for multi-compartmental neuron models can be challenging. Parameters such as the leak and axial conductance are not always directly derivable from neuron observations but are crucial for replicating desired observations. The objective of this study is to replicate the attenuation behavior of an excitatory postsynaptic potential (EPSP) traveling along a linear chain of compartments on the analog BrainScaleS-2 neuromorphic hardware platform. Methods: In the present publication we use genetic algorithms to find suitable model parameters. They promi

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Park, Jaeseoung. "Bulk Switching Trilayer Metal Oxide RRAM for Neuromorphic Computing at the Edge." Ceramist 28, no. 1 (2025): 106–16. https://doi.org/10.31613/ceramist.2025.00024.

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The Complementary metal oxide semiconductor-Resistive random access memory (RRAM) integration presents significant potential for energy-efficient and high-speed neuromorphic computing. However, conventional filamentary RRAM technologies that rely on filamentary switching face challenges such as high variability, noise, reduced computational accuracy, and increased energy consumption. To address these limitations, we developed a filament-free, bulk-switching RRAM technology. By designing a trilayer metal-oxide stack, we optimized switching behavior across different oxide thicknesses and oxygen

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Kösters, Dominique J., Bryan A. Kortman, Irem Boybat, et al. "Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics." APL Machine Learning 1, no. 1 (2023): 016101. http://dx.doi.org/10.1063/5.0116699.

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The massive use of artificial neural networks (ANNs), increasingly popular in many areas of scientific computing, rapidly increases the energy consumption of modern high-performance computing systems. An appealing and possibly more sustainable alternative is provided by novel neuromorphic paradigms, which directly implement ANNs in hardware. However, little is known about the actual benefits of running ANNs on neuromorphic hardware for use cases in scientific computing. Here, we present a methodology for measuring the energy cost and compute time for inference tasks with ANNs on conventional h

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Ferreira de Lima, Thomas, Alexander N. Tait, Armin Mehrabian, et al. "Primer on silicon neuromorphic photonic processors: architecture and compiler." Nanophotonics 9, no. 13 (2020): 4055–73. http://dx.doi.org/10.1515/nanoph-2020-0172.

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AbstractMicroelectronic computers have encountered challenges in meeting all of today’s demands for information processing. Meeting these demands will require the development of unconventional computers employing alternative processing models and new device physics. Neural network models have come to dominate modern machine learning algorithms, and specialized electronic hardware has been developed to implement them more efficiently. A silicon photonic integration industry promises to bring manufacturing ecosystems normally reserved for microelectronics to photonics. Photonic devices have alre

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Rusev, Georgi, Svetlozar Yordanov, Simona Nedelcheva, et al. "Decoding Brain Signals in a Neuromorphic Framework for a Personalized Adaptive Control of Human Prosthetics." Biomimetics 10, no. 3 (2025): 183. https://doi.org/10.3390/biomimetics10030183.

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Current technological solutions for Brain-machine Interfaces (BMI) achieve reasonable accuracy, but most systems are large in size, power consuming and not auto-adaptive. This work addresses the question whether current neuromorphic technologies could resolve these problems? The paper proposes a novel neuromorphic framework of a BMI system for prosthetics control via decoding Electro Cortico-Graphic (ECoG) brain signals. It includes a three-dimensional spike timing neural network (3D-SNN) for brain signals features extraction and an on-line trainable recurrent reservoir structure (Echo state n

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Kim, Jaeseop, Seungyeon Lee, and Jiman Hong. "Reduction of Inference time in Neuromorphic Based Platform for IoT Computing Environments." Korean Institute of Smart Media 11, no. 2 (2022): 77–83. http://dx.doi.org/10.30693/smj.2022.11.2.77.

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Devyatisil’nyi, A. S. "Neuromorphic expansion of the GLONASS onboard functions for a mobile technological platform." Technical Physics 60, no. 10 (2015): 1419–22. http://dx.doi.org/10.1134/s1063784215100114.

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Dong, Pham-Khoi, Khanh N. Dang, Duy-Anh Nguyen, and Xuan-Tu Tran. "A light-weight neuromorphic controlling clock gating based multi-core cryptography platform." Microprocessors and Microsystems 106 (April 2024): 105040. http://dx.doi.org/10.1016/j.micpro.2024.105040.

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Forno, Evelina, Alessandro Salvato, Enrico Macii, and Gianvito Urgese. "PageRank Implemented with the MPI Paradigm Running on a Many-Core Neuromorphic Platform." Journal of Low Power Electronics and Applications 11, no. 2 (2021): 25. http://dx.doi.org/10.3390/jlpea11020025.

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SpiNNaker is a neuromorphic hardware platform, especially designed for the simulation of Spiking Neural Networks (SNNs). To this end, the platform features massively parallel computation and an efficient communication infrastructure based on the transmission of small packets. The effectiveness of SpiNNaker in the parallel execution of the PageRank (PR) algorithm has been tested by the realization of a custom SNN implementation. In this work, we propose a PageRank implementation fully realized with the MPI programming paradigm ported to the SpiNNaker platform. We compare the scalability of the

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Singh, Jagmeet, Hugh Morison, Zhimu Guo, et al. "Neuromorphic photonic circuit modeling in Verilog-A." APL Photonics 7, no. 4 (2022): 046103. http://dx.doi.org/10.1063/5.0079984.

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One of the significant challenges in neuromorphic photonic architectures is the lack of good tools to simulate large-scale photonic integrated circuits. It is crucial to perform simulations on a single platform to capture the circuit’s behavior in the presence of both optical and electrical components. Here, we adopted a Verilog-A based approach to model neuromorphic photonic circuits by considering both the electrical and optical properties. Verilog-A models for the primary optical devices, such as lasers, couplers, waveguides, phase shifters, and photodetectors, are discussed, along with stu

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Canales-Verdial, Jorge I., Jamison R. Wagner, Landon A. Schmucker, et al. "Energy-Efficient Neuromorphic Architectures for Nuclear Radiation Detection Applications." Sensors 24, no. 7 (2024): 2144. http://dx.doi.org/10.3390/s24072144.

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A comprehensive analysis and simulation of two memristor-based neuromorphic architectures for nuclear radiation detection is presented. Both scalable architectures retrofit a locally competitive algorithm to solve overcomplete sparse approximation problems by harnessing memristor crossbar execution of vector–matrix multiplications. The proposed systems demonstrate excellent accuracy and throughput while consuming minimal energy for radionuclide detection. To ensure that the simulation results of our proposed hardware are realistic, the memristor parameters are chosen from our own fabricated me

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Zhang, Zhen, Yifei Sun, and Hai-Tian Zhang. "Quantum nickelate platform for future multidisciplinary research." Journal of Applied Physics 131, no. 12 (2022): 120901. http://dx.doi.org/10.1063/5.0084784.

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Perovskite nickelates belong to a family of strongly correlated materials, which have drawn broad attention due to their thermally induced metal-to-insulator transition. Recent discoveries show that orbital filling mediated by ion intercalation can trigger a colossal non-volatile conductivity change in nickelates. The coupling and interaction between two types of charge carriers (i.e., ions and electrons) enable nickelate as an exotic mixed conductor for electronic, biological, and energy applications. In this Perspective, we first summarize the fundamentals and recent progresses in the manipu

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Ostrau, Christoph, Christian Klarhorst, Michael Thies, and Ulrich Rückert. "Benchmarking Neuromorphic Hardware and Its Energy Expenditure." Frontiers in Neuroscience 16 (June 2, 2022). http://dx.doi.org/10.3389/fnins.2022.873935.

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We propose and discuss a platform overarching benchmark suite for neuromorphic hardware. This suite covers benchmarks from low-level characterization to high-level application evaluation using benchmark specific metrics. With this rather broad approach we are able to compare various hardware systems including mixed-signal and fully digital neuromorphic architectures. Selected benchmarks are discussed and results for several target platforms are presented revealing characteristic differences between the various systems. Furthermore, a proposed energy model allows to combine benchmark performanc

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Shi, Zhi Wen, Zheng Yu Ren, Wei Sheng Wang, Hui Xiao, Yu Heng Zeng, and Li Qiang Zhu. "Bio-inspired Tactile Perception Platform with Information Encryption Function." Chinese Physics B, June 18, 2022. http://dx.doi.org/10.1088/1674-1056/ac7a15.

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Abstract Mimicking tactile perception is critical to the development of advanced interactive neuromorphic platforms. Inspired by cutaneous perceptual functions, a bionic tactile perceptual platform is proposed. PDMS based tactile sensors act as bionic skin touch receptors. Flexible indium tin oxide neuromorphic transistors fabricated with a single-step mask processing act as artificial synapses. Thus, the tactile perceptual platform possesses the ability of information processing. Interestingly, the flexible tactile perception platform can find applications in information encryption and decryp

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Dey, Srijanie, and Alexander Dimitrov. "Mapping and Validating a Point Neuron Model on Intel's Neuromorphic Hardware Loihi." Frontiers in Neuroinformatics 16 (May 30, 2022). http://dx.doi.org/10.3389/fnins.2022.883360.

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Neuromorphic hardware is based on emulating the natural biological structure of the brain. Since its computational model is similar to standard neural models, it could serve as a computational accelerator for research projects in the field of neuroscience and artificial intelligence, including biomedical applications. However, in order to exploit this new generation of computer chips, we ought to perform rigorous simulation and consequent validation of neuromorphic models against their conventional implementations. In this work, we lay out the numeric groundwork to enable a comparison between

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Dey, Srijanie, and Alexander Dimitrov. "Mapping and Validating a Point Neuron Model on Intel's Neuromorphic Hardware Loihi." Frontiers in Neuroinformatics 16 (May 30, 2022). http://dx.doi.org/10.3389/fninf.2022.883360.

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Neuromorphic hardware is based on emulating the natural biological structure of the brain. Since its computational model is similar to standard neural models, it could serve as a computational accelerator for research projects in the field of neuroscience and artificial intelligence, including biomedical applications. However, in order to exploit this new generation of computer chips, we ought to perform rigorous simulation and consequent validation of neuromorphic models against their conventional implementations. In this work, we lay out the numeric groundwork to enable a comparison between

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Fang, Wei, Yanqi Chen, Jianhao Ding, et al. "SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence." Science Advances 9, no. 40 (2023). http://dx.doi.org/10.1126/sciadv.adi1480.

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Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties. As the emerging spiking deep learning paradigm attracts increasing interest, traditional programming frameworks cannot meet the demands of the automatic differentiation, parallel computation acceleration, and high integration of processing neuromorphic datasets and deployment. In this work, we present the SpikingJelly framework to address the aforementioned dilemma. We contribute a full-stack toolkit for preprocessing n

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Primavera, Bryce A., and Jeffrey M. Shainline. "Considerations for Neuromorphic Supercomputing in Semiconducting and Superconducting Optoelectronic Hardware." Frontiers in Neuroscience 15 (September 6, 2021). http://dx.doi.org/10.3389/fnins.2021.732368.

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Any large-scale spiking neuromorphic system striving for complexity at the level of the human brain and beyond will need to be co-optimized for communication and computation. Such reasoning leads to the proposal for optoelectronic neuromorphic platforms that leverage the complementary properties of optics and electronics. Starting from the conjecture that future large-scale neuromorphic systems will utilize integrated photonics and fiber optics for communication in conjunction with analog electronics for computation, we consider two possible paths toward achieving this vision. The first is a s

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Gan, Yusong, Ying Shi, Sanjib Ghosh, Haiyun Liu, Huawen Xu, and Qihua Xiong. "Ultrafast neuromorphic computing driven by polariton nonlinearities." eLight 5, no. 1 (2025). https://doi.org/10.1186/s43593-025-00087-9.

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Abstract Neuromorphic computing offers a promising approach to artificial intelligence by mimicking biological neural networks to perform complex tasks efficiently. While software-based simulations have demonstrated the potential of neuromorphic architectures, a physical platform is crucial to fully realize its computational advantages. Herein, we present the first demonstration of perovskite microcavity exciton polaritons as a platform for reservoir computing-based artificial neural networks. By leveraging the nonlinear response properties of exciton polaritons, we developed a neuromorphic co

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Ghosh, Sanjib, Tomasz Paterek, and Timothy C. H. Liew. "Quantum Neuromorphic Platform for Quantum State Preparation." Physical Review Letters 123, no. 26 (2019). http://dx.doi.org/10.1103/physrevlett.123.260404.

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Yang, Zhen, Ying-Ming Lu, and Yu-Chao Yang. "Reconfigurable Mott electronics for homogeneous neuromorphic platform." Chinese Physics B, October 13, 2023. http://dx.doi.org/10.1088/1674-1056/ad02e8.

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Abstract To simplify the fabrication process and increase the versatility of neuromorphic systems, the reconfiguration concept has attracted much attention. Here, we developed a novel electrochemical VO2 (EC-VO2) device, which can be reconfigured as synapses or LIF neurons. The ionic dynamic doping contributes to the resistance changes of VO2, which enables the reversible modulation of device states. The analog resistance switching and tunable LIF functions were both measured based on the same device to demonstrate the capacity of reconfiguration. Based on the reconfigurable EC-VO2, the simula

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Huang, Zhuohui, Yanran Li, Yi Zhang, Jiewei Chen, Jun He, and Jie Jiang. "2D Multifunctional Devices: from Material Preparation to Device Fabrication and Neuromorphic Applications." International Journal of Extreme Manufacturing, February 28, 2024. http://dx.doi.org/10.1088/2631-7990/ad2e13.

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Abstract Neuromorphic computing systems, which mimic the operation of neurons and synapses in the human brain, are seen as an appealing next-generation computing method due to their strong and efficient computing abilities. Two-dimensional (2D) materials with dangling bond-free surfaces and atomic-level thicknesses have emerged as promising candidates for neuromorphic computing hardware. As a result, 2D neuromorphic devices may provide an ideal platform for developing multifunctional neuromorphic applications. Here, we review the recent neuromorphic devices based on 2D material and their multi

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Amaya, Camilo, and Axel von Arnim. "Neurorobotic reinforcement learning for domains with parametrical uncertainty." Frontiers in Neurorobotics 17 (October 25, 2023). http://dx.doi.org/10.3389/fnbot.2023.1239581.

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Neuromorphic hardware paired with brain-inspired learning strategies have enormous potential for robot control. Explicitly, these advantages include low energy consumption, low latency, and adaptability. Therefore, developing and improving learning strategies, algorithms, and neuromorphic hardware integration in simulation is a key to moving the state-of-the-art forward. In this study, we used the neurorobotics platform (NRP) simulation framework to implement spiking reinforcement learning control for a robotic arm. We implemented a force-torque feedback-based classic object insertion task (“p

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Zarrin, Pouya Soltani, Finn Zahari, Mamathamba K. Mahadevaiah, Eduardo Perez, Hermann Kohlstedt, and Christian Wenger. "Neuromorphic on-chip recognition of saliva samples of COPD and healthy controls using memristive devices." Scientific Reports 10, no. 1 (2020). http://dx.doi.org/10.1038/s41598-020-76823-7.

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AbstractChronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease, affecting millions of people worldwide. Implementation of Machine Learning (ML) techniques is crucial for the effective management of COPD in home-care environments. However, shortcomings of cloud-based ML tools in terms of data safety and energy efficiency limit their integration with low-power medical devices. To address this, energy efficient neuromorphic platforms can be used for the hardware-based implementation of ML methods. Therefore, a memristive neuromorphic platform is presented in this paper fo

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"INTEGRATION PLATFORM FOR MULTISCALE MODELING OF NEUROMORPHIC SYSTEMS." Informatics and Applications, June 30, 2020. http://dx.doi.org/10.14357/19922264200215.

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Pal, Arnab, Zichun Chai, Junkai Jiang, et al. "An ultra energy-efficient hardware platform for neuromorphic computing enabled by 2D-TMD tunnel-FETs." Nature Communications 15, no. 1 (2024). http://dx.doi.org/10.1038/s41467-024-46397-3.

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AbstractBrain-like energy-efficient computing has remained elusive for neuromorphic (NM) circuits and hardware platform implementations despite decades of research. In this work we reveal the opportunity to significantly improve the energy efficiency of digital neuromorphic hardware by introducing NM circuits employing two-dimensional (2D) transition metal dichalcogenide (TMD) layered channel material-based tunnel-field-effect transistors (TFETs). Our novel leaky-integrate-fire (LIF) based digital NM circuit along with its Hebbian learning circuitry operates at a wide range of supply voltages,

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Robertson, Joshua, Paul Kirkland, Juan Arturo Alanis, et al. "Ultrafast neuromorphic photonic image processing with a VCSEL neuron." Scientific Reports 12, no. 1 (2022). http://dx.doi.org/10.1038/s41598-022-08703-1.

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AbstractThe ever-increasing demand for artificial intelligence (AI) systems is underlining a significant requirement for new, AI-optimised hardware. Neuromorphic (brain-like) processors are one highly-promising solution, with photonic-enabled realizations receiving increasing attention. Among these, approaches based upon vertical cavity surface emitting lasers (VCSELs) are attracting interest given their favourable attributes and mature technology. Here, we demonstrate a hardware-friendly neuromorphic photonic spike processor, using a single VCSEL, for all-optical image edge-feature detection.

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Ausilio, Chiara, Claudia Lubrano, Daniela Rana, Giovanni Maria Matrone, Ugo Bruno, and Francesca Santoro. "Concealing Organic Neuromorphic Devices with Neuronal‐Inspired Supported Lipid Bilayers." Advanced Science, May 3, 2024. http://dx.doi.org/10.1002/advs.202305860.

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AbstractNeurohybrid systems have gained large attention for their potential as in vitro and in vivo platform to interrogate and modulate the activity of cells and tissue within nervous system. In this scenario organic neuromorphic devices have been engineered as bioelectronic platforms to resemble characteristic neuronal functions. However, aiming to a functional communication with neuronal cells, material synthesis, and surface engineering can yet be exploited for optimizing bio‐recognition processes at the neuromorphic‐neuronal hybrid interface. In this work, artificial neuronal‐inspired lip

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