Results 71 to 80 of about 60,411 (286)

Optoelectronic Synaptic Devices Using Molecular Telluride Phase‐Change Inks for Three‐Factor Learning

Advanced Functional Materials, EarlyView.
Optoelectronic synaptic devices based on solution‐processed molecular telluride GST‐225 phase‐change inks are demonstrated for three‐factor learning. A global optical signal broadcast through a silicon waveguide induces non‐volatile conductance updates exclusively in locally electrically flagged memristors.
Kevin Portner, Dhananjeya Kumaar, Giacomo Torti, Killian Keller, Till Zellweger, Florian M. Schenk, Christoph Weilenmann, Daniel Chelladurai, Vara Prasad Jonnalagadda, Ghazi Sarwat Syed, Vanessa Wood, Jürg Leuthold, Alexandros Emboras, Maksym Yarema, Mathieu Luisier   +14 more
wiley   +1 more source

Effects of Spike Anticipation on the Spiking Dynamics of Neural Networks [PDF]

Frontiers in Computational Neuroscience, 2015
Synchronization is one of the central phenomena involved in information processing in living systems. It is known that the nervous system requires the coordinated activity of both local and distant neural populations. Such an interplay allows to merge different information modalities in a whole processing supporting high-level mental skills as ...
Daniel de Santos Sierra, Abel Sanchez-Jimenez, Mariano A. Garcia-Vellisca, Adrián Navas, José Antonio Villacorta-Atienza   +4 more
openaire   +4 more sources

Photon Avalanching Nanoparticles: The Next Generation of Upconverting Nanomaterials?

Advanced Functional Materials, EarlyView.
This Perspective outlines the mechanistic foundations that enable photon‐avalanche (PA) behavior in lanthanide nanomaterials and contrasts them with emerging application spaces and forward‐looking design strategies. By bridging threshold engineering, energy‐transfer dynamics, and materials engineering, we provide a coherent roadmap for advancing the ...
Kimoon Lee, Yunseo Kim, Hyeong‐ku Jo, Dongjoon Yoon, Byeonghyeok Im, WonJeong Yu, Eui‐Rim On, Changhwan Lee   +7 more
wiley   +1 more source

Spiking Neural Networks: History, Current Status and the Future

Dynamics
Simulated spiking neural networks have been explored for over a hundred years. Many of these networks are driven by biological considerations and an attempt to simulate brains, but others are used with little biological consideration.
Christian R. Huyck
doaj   +1 more source

Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System

, 2017
Emulating spiking neural networks on analog neuromorphic hardware offers several advantages over simulating them on conventional computers, particularly in terms of speed and energy consumption.
Bellec, Guillaume, Grubl, Andreas, Guttler, Maurice, Hartel, Andreas, Hartmann, Stephan, Husmann, Dan, Husmann, Kai, Jeltsch, Sebastian, Karasenko, Vitali, Klahn, Johann, Kleider, Mitja, Koke, Christoph, Kononov, Alexander, Legenstein, Robert, Maass, Wolfgang, Mauch, Christian, Mayr, Christian, Meier, Karlheinz, Muller, Eric, Muller, Paul, Partzsch, Johannes, Petrovici, Mihai Alexandru, Schemmel, Johannes, Schiefer, Stefan, Schmitt, Sebastian, Scholze, Stefan, Schuffny, Rene, Thanasoulis, Vasilis, Vogginger, Bernhard   +28 more
core   +1 more source

Multi‐Scale Interface Engineering of MXenes for Multifunctional Sensory Systems

Advanced Functional Materials, EarlyView.
MXenes, as two‐dimensional transition metal carbides and nitrides, demonstrate remarkable capabilities for multifunctional sensing applications. This review systematically examines multi‐scale interface engineering approaches that enhance sensing performance, enable diverse detection functionalities, and improve system‐level compatibility in MXene ...
Jiaying Liao, Sin‐Yi Pang, Jianhua Hao
wiley   +1 more source

On Training Spiking Neural Networks by Means of a Novel Quantum Inspired Machine Learning Method

Applied AI Letters
In spite of the high potential shown by spiking neural networks (e.g., temporal patterns), training them remains an open and complex problem. In practice, while in theory these networks are computationally as powerful as mainstream artificial neural ...
Jean Michel Sellier, Alexandre Martini
doaj   +1 more source

Spike-Timing Dependent Learning Dynamics in Silicon-Doped Hafnium-Oxide-Based Ferroelectric Field Effect Transistors

IEEE Journal of the Electron Devices Society
Brain-inspired computing, with its potential for energy-efficient spatio-temporal data processing, has spurred significant interest in spiking neural networks and their hardware implementations. Leveraging their non-volatile memory and analog tunability,
Masud Rana Sk, Apu Das, Gautham Kumar, Deepanshi Bhatnagar, Sourodeep Roy, Yannick Raffel, Maximilian Lederer, Konrad Seidel, Sourav De, Bhaswar Chakrabarti   +9 more
doaj   +1 more source

A Markovian event-based framework for stochastic spiking neural networks

, 2011
In spiking neural networks, the information is conveyed by the spike times, that depend on the intrinsic dynamics of each neuron, the input they receive and on the connections between neurons. In this article we study the Markovian nature of the sequence
A Delorme, A Delorme, A Holden, A Lachal, A Lachal, A Roxin, A Tonnelier, Arbib, B Cessac, C Fricker, E Claverol, E Gobet, E Kandel, E Rolls, EM Izhikevich, H Gromoll, HC Tuckwell, HP McKean, I Karatzas, J Touboul, J Touboul, Jonathan D. Touboul, L Ricciardi, L Watts, M Cottrell, M Cottrell, M Davis, M Fabre-Thorpe, M Goldman, M Rudolph, MN Shadlen, N Brunel, Olivier D. Faugeras, P Kloeden, R Brette, R Brette, R Brette, S Asmussen, S Thorpe, T Makino, T Turova, TS Turova, W Gerstner, W Gerstner, WR Softky   +44 more
core   +3 more sources

Spiking neural networks trained with backpropagation for low power neuromorphic implementation of voice activity detection

, 2020
Recent advances in Voice Activity Detection (VAD) are driven by artificial and Recurrent Neural Networks (RNNs), however, using a VAD system in battery-operated devices requires further power efficiency.
Cernak, Milos, Dellaferrera, Giorgia, Mainar, Pablo, Martinelli, Flavio   +3 more
core   +1 more source