Results 231 to 240 of about 59,835 (291)

SpiNeRF: direct-trained spiking neural networks for efficient neural radiance field rendering. [PDF]

Front Neurosci
Yao X, Hu Q, Zhou F, Liu T, Mo Z, Zhu Z, Zhuge Z, Cheng J.   +7 more
europepmc   +1 more source

Parametric Analysis of Spiking Neurons in 16 nm Fin Field‐Effect Transistor Technology

Advanced Intelligent Discovery, EarlyView.
Energy efficient computing has driven a shift toward brain‐inspired neuromorphic hardware. This study explores the design of three distinct silicon neuron topologies implemented in 16 nm fin field‐Effect transistor technology. While the Axon‐Hillock design achieves gigahertz throughput, its functional fragility persists. The Morris–Lecar model captures
Logan Larsh, Raiyan Siddique, Sarah S. Sharif, Yaser Mike Banad   +3 more
wiley   +1 more source

Learning-efficient spiking neural networks with multi-compartment spatio-temporal backpropagation. [PDF]

iScience
Liu Y, Wang Y, Zhang C, Yu L, Fang Y, Chen F.   +5 more
europepmc   +1 more source

Dual‐Mode Oscillations in SiOx‐Based Threshold Switching Devices for Unconventional Probabilistic Computing

Advanced Intelligent Discovery, EarlyView.
To enable versatile unconventional computing, a single SiOx threshold switching device is engineered to exhibit controllable dual‐mode oscillation. By modulating the input voltage, the device selectively provides stable full oscillation for oscillatory neural networks and stochastic probabilistic oscillation for probabilistic bits and true random ...
Hyeonsik Choi, Jihyun Kim, Yonghun Kim, Jiyong Woo   +3 more
wiley   +1 more source

Unveiling the role of local metabolic constraints on the structure and activity of spiking neural networks. [PDF]

PLoS Comput Biol
Jaras I, Orchard ME, Maldonado PE, Vergara RC.   +3 more
europepmc   +1 more source

Clinically Informed Intelligent Classification of Ovarian Cancer Cells by Label‐Free Holographic Imaging Flow Cytometry

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
Quantitative phase maps of single cells recorded in flow cytometry modality feed a hierarchical architecture of machine learning models for the label‐free identification of subtypes of ovarian cancer. The employment of a priori clinical information improves the classification performance, thus emulating the clinical application of liquid biopsy during ...
Daniele Pirone, Beatrice Cavina, Daniele Gaetano Sirico, Martina Mugnano, Vittorio Bianco, Lisa Miccio, Anna Myriam Perrone, Anna Maria Porcelli, Giuseppe Gasparre, Ivana Kurelac, Pasquale Memmolo, Pietro Ferraro   +11 more
wiley   +1 more source

Reinforced liquid state machines-new training strategies for spiking neural networks based on reinforcements. [PDF]

Front Comput Neurosci
Krenzer D, Bogdan M.
europepmc   +1 more source

Training spiking neural networks

, 2023
openaire   +1 more source

Haptic In‐Sensor Computing Device Based on CNT/PDMS Nanocomposite Physical Reservoir

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
Using a porous carbon nanotube‐polydimethylsiloxane nanocomposite, a sensor array integrated with a physical reservoir computing paradigm capable of in‐sensor computing is demonstrated. The device is able to classify between nine objects with an accuracy above 80%, opening the possibility for low‐power sensing/computing for future robotics.
Kouki Kimizuka, Saman Azhari, Shoshi Tokuno, Ahmet Karacali, Yuki Usami, Shuhei Ikemoto, Hakaru Tamukoh, Hirofumi Tanaka   +7 more
wiley   +1 more source

Dynamic spatio-temporal pruning for efficient spiking neural networks. [PDF]

Front Neurosci
Gou S, Fu J, Sha Y, Cao Z, Guo Z, Eshraghian JK, Li R, Jiao L.   +7 more
europepmc   +1 more source