Results 121 to 130 of about 3,555,912 (299)

Research on SNN Learning Algorithms and Networks Based on Biological Plausibility

IEEE Access
Spiking Neural Networks, inspired by the brain’s neuronal information processing mech- anisms, utilize sparse, event-based spike signals to emulate biological computation.
Bingqiang Huo, Fang Li, Siyu Peng, Hongwei Chen, Sudan Xin, Hongjun Wang   +5 more
doaj   +1 more source

Hyperdimensional decoding of spiking neural networks

Neuromorphic Computing and Engineering
Abstract This work presents a novel spiking neural network (SNN) decoding method, combining SNNs with hyperdimensional computing (HDC). This decoding method is designed to achieve high accuracy, high noise robustness, low inference latency and low energy consumption. Compared to analogous architectures decoded with existing approaches,
Cedrick Kinavuidi, Luca Peres, Oliver Rhodes   +2 more
openaire   +4 more sources

Metal Oxide Nano‐Interface Boosting the Deep Ultraviolet Adjustable Noise‐Filtering In‐Sensor Computing

Advanced Materials, EarlyView.
We show that sol‐gel‐fractured indium–magnesium oxide combines deep‐ultraviolet responsivity, high carrier mobility, and an excellent memory dynamic range. This unique materials platform enables deep‐ultraviolet long‐afterglow light‐emitting devices with multifunctional integration.
Zhongshi Ju, Peng Li, Jingsong Yuan, Boyuan Yu, Bin Han, Yusheng Chen, Jiangang Ma, Haiyang Xu, Yichun Liu, Paolo Samorì   +9 more
wiley   +1 more source

A Model for Programmability and Virtuality in Dynamical Neural Networks [PDF]

, 2009
In this dissertation a fixed-weight architecture for Continuous Time Recurrent Neural Networks (CTRNNs) is proposed in order to give an account for biological phenomena, controlled by neuronal activity, in which changes of behavior occur so fast that ...
Donnarumma, Francesco
core   +1 more source

Fast learning without synaptic plasticity in spiking neural networks

Scientific Reports
Spiking neural networks are of high current interest, both from the perspective of modelling neural networks of the brain and for porting their fast learning capability and energy efficiency into neuromorphic hardware. But so far we have not been able to
Anand Subramoney, Guillaume Bellec, Franz Scherr, Robert Legenstein, Wolfgang Maass   +4 more
doaj   +1 more source

Assessing ECG Interpretation Expertise in Medical Practitioners Through Eye Movement Data and Neuromorphic Models

IEEE Access
This study introduces an innovative method for assessing ECG interpretation abilities in medical professionals via eye-tracking data. We examine eye movement patterns from five separate groups of cardiology practitioners utilizing a combination of ...
Syed Mohsin Bokhari, Muhammad Shafi, Fazal Noor, Sarmad Sohaib, Saad Alqahtany, Mark Donnelly   +5 more
doaj   +1 more source

Data‐Driven Materials Science for Energy‐Sustainable Applications

Advanced Materials, EarlyView.
Data‐driven approaches powered by artificial intelligence are transforming materials discovery for energy sustainability. This review examines how auto‐generated high‐quality materials databases and domain‐specific language models accelerate research in photovoltaics, thermoelectrics, batteries and magnetic materials. Applications involve extraction of
Jacqueline M. Cole
wiley   +1 more source

DPSNN: spiking neural network for low-latency streaming speech enhancement

Neuromorphic Computing and Engineering
Speech enhancement improves communication in noisy environments, affecting areas such as automatic speech recognition (ASR), hearing aids, and telecommunications.
Tao Sun, Sander Bohté
doaj   +1 more source

Noise‐Tunable Memristor Enabling Programmable Probabilistic Neurons for Frequency‐Selective Time‐Series Signal Encoding

Advanced Materials, EarlyView.
Memristors offer tunable resistance and intrinsic instability, making them promising tunable noise sources. We propose a spiking‐rate‐programmable probabilistic neuron using a Ru/TaOx/Pt memristor, where resistance‐dependent noise enables frequency‐selective encoding.
Do Hoon Kim, Seoeun Jang, Hakseung Rhee, Min Gu Lee, Daehee Kim, Taewook Go, Woon Hyung Cheong, Hanchan Song, Kyung Min Kim   +8 more
wiley   +1 more source

Engineering Pluripotent Stem Cells‐Derived Inner Ear Organoids With Enhanced Maturation and Reproducibility by Micro‐Topographical Cues

Advanced Materials, EarlyView.
Micro‐topographical cues applied through temporally controlled microscale confinement improve the reproducibility, spatial organization, and neurosensory‐associated features of pluripotent stem cell‐derived inner ear organoids. Integration with a vascularized organoid platform further enables controlled investigation of vascular‐epithelial interactions
Harshita Sharma, Jungeun Lim, Woochan Kim, Yeon Ju Kim, Dream Kim, Shinyull Lee, Chaeyeon Park, Stephen Rhee, Shruthy Kuttapan, Jungho Ha, Sang Wook Park, Kyunghoon Kim, Noo Li Jeon, Sunho Park, Yun‐Hoon Choung, Jangho Kim   +15 more
wiley   +1 more source