Results 41 to 50 of about 505 (135)

Opportunities for integrated photonic neural networks

Nanophotonics, Volume 9, Issue 13, Page 4221-4232, June 2018., 2018
Abstract Photonics offers exciting opportunities for neuromorphic computing. This paper specifically reviews the prospects of integrated optical solutions for accelerating inference and training of artificial neural networks. Calculating the synaptic function, thereof, is computationally very expensive and does not scale well on state‐of‐the‐art ...
Pascal Stark, Folkert Horst, Roger Dangel, Jonas Weiss, Bert Jan Offrein   +4 more
wiley   +1 more source

An On-chip Trainable and Clock-less Spiking Neural Network with 1R Memristive Synapses

, 2017
Spiking neural networks (SNNs) are being explored in an attempt to mimic brain's capability to learn and recognize at low power. Crossbar architecture with highly scalable Resistive RAM or RRAM array serving as synaptic weights and neuronal drivers in ...
Ganguly, Udayan, Shukla, Aditya
core   +1 more source

Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity [PDF]

, 2017
Brain-inspired computation can revolutionize information technology by introducing machines capable of recognizing patterns (images, speech, video) and interacting with the external world in a cognitive, humanlike way.
Ambrogio, Stefano, Bianchi, Stefano, Calderoni, A., Carboni, Roberto, Ielmini, Daniele, Milo, Valerio, Pedretti, Giacomo, Ramaswamy, N., SOTTOCORNOLA SPINELLI, Alessandro   +8 more
core   +1 more source

Perspective: Organic electronic materials and devices for neuromorphic engineering

, 2018
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 platforms are
Alibart, Fabien, Pecqueur, Sebastien, Vuillaume, Dominique   +2 more
core   +2 more sources

Review of Memristors for In‐Memory Computing and Spiking Neural Networks

Advanced Intelligent Systems, Volume 8, Issue 3, March 2026.
Memristors uniquely enable energy‐efficient, brain‐inspired computing by acting as both memory and synaptic elements. This review highlights their physical mechanisms, integration in crossbar arrays, and role in spiking neural networks. Key challenges, including variability, relaxation, and stochastic switching, are discussed, alongside emerging ...
Mostafa Shooshtari, Teresa Serrano‐Gotarredona, Bernabé Linares‐Barranco   +2 more
wiley   +1 more source

Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning [PDF]

, 2016
Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems.
Alexantrou Serb, Erika Covi, Marco Fanciulli, Marco Fanciulli, Sabina Spiga, Stefano Brivio, Themis Prodromakis   +6 more
core   +2 more sources

The Evolution of Gas Sensors Into Neuromorphic Systems

Advanced Electronic Materials, Volume 12, Issue 2, 21 January 2026.
Gas sensors are vital for various applications, but conventional designs rely on separate sensing, memory, and processing units, limiting speed, power efficiency, and adaptability. Neuromorphic gas sensing overcomes these constraints by integrating all functions in a single device.
Kevin Dominguez, Dhananjay D. Kumbhar, Abdul Momin Syed, Regina C. Martin, Nazek El‐Atab   +4 more
wiley   +1 more source

Structural and Electrical Behavior of Swift Heavy Ion Irradiated Hafnium Oxide Polymorphs in Ferroelectric and Resistive Memories

Advanced Electronic Materials, Volume 11, Issue 21, December 17, 2025.
This study investigates the irradiation resistance of different HfO2‐x stacks to swift heavy ions with different energies. The results demonstrate that Ca‐ion irradiation has a negligible effect on the crystallinity and switching properties of the functional layers.
Philipp Schreyer, David Lehninger, Tobias Vogel, Tianren Zhang, Taewook Kim, Eszter Piros, Yingxin Li, Yu Duan, Maximilian Lederer, Konrad Seidel, Christina Trautmann, Eugenia Toimil‐Molares, Lambert Alff   +12 more
wiley   +1 more source

Training Deep Spiking Neural Networks Using Backpropagation [PDF]

, 2016
Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation.
Afshar, Bengio, Camunas-Mesa, Cohen, Diehl, Diehl, Esser, Esser, Furber, Garbin, Gerstner, Glorot, Goodfellow, He, He, Hunsberger, Indiveri, Ioffe, Jun Haeng Lee, Kingma, LeCun, LeCun, Lichtsteiner, Loosli, Maass, Masquelier, Merolla, Michael Pfeiffer, Nageswaran, Neftci, Neil, O'Connor, O'Connor, Orchard, Oster, Rozell, Rumelhart, Schmidhuber, Srivastava, Tobi Delbruck, Vincent, Wan   +41 more
core   +2 more sources

Investigation of Cycle-to-Cycle Variability in HfO₂-Based OxRAM

IEEE Electron Device Letters, 2016
This letter studies the intrinsic variability in oxide-based resistive RAM technology, highlighting the presence of a short range ( $\approx 40$ ) correlation of resistances among cycles (for both low resistance state and high resistance state). Experimental results demonstrate the existence of a resistance correlation, and an analytical model is ...
Piccolboni, Giuseppe, Molas, Gabriel, Garbin, Daniele, Vianello, Elisa, Cueto, Olga, Cagli, Carlo, Traore, Boubacar, de Salvo, Barbara, Ghibaudo, Gérard, Perniola, Luca   +9 more
openaire   +2 more sources