Results 91 to 100 of about 76,325 (312)
Frontiers in Systems Neuroscience, 2020 Cortical neural circuits display highly irregular spiking in individual neurons but variably sized collective firing, oscillations and critical avalanches at the population level, all of which have functional importance for information processing ...
Junhao Liang +4 more
doaj +1 more source
Testing of information condensation in a model reverberating spiking neural network
, 2011 Information about external world is delivered to the brain in the form of structured in time spike trains. During further processing in higher areas, information is subjected to a certain condensation process, which results in formation of abstract ...
Vidybida, Alexander K.
core +1 more source
Spectrally Tunable 2D Material‐Based Infrared Photodetectors for Intelligent Optoelectronics
Advanced Functional Materials, EarlyView.Intelligent optoelectronics through spectral engineering of 2D material‐based infrared photodetectors. Abstract The evolution of intelligent optoelectronic systems is driven by artificial intelligence (AI). However, their practical realization hinges on the ability to dynamically capture and process optical signals across a broad infrared (IR) spectrum.
Junheon Ha +18 more
wiley +1 more source
High-performance deep spiking neural networks with 0.3 spikes per neuron
Nature CommunicationsCommunication by rare, binary spikes is a key factor for the energy efficiency of biological brains. However, it is harder to train biologically-inspired spiking neural networks than artificial neural networks.
Ana Stanojevic +5 more
doaj +1 more source
Implementing Signature Neural Networks with Spiking Neurons
Frontiers in Computational Neuroscience, 2016 Spiking Neural Networks constitute the most promising approach to develop realistic ArtificialNeural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding inspiking models is based on the precise timing of individual spikes.
José Luis Carrillo-Medina +1 more
doaj +1 more source
Advanced Functional Materials, EarlyView.This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee +17 more
wiley +1 more source
A spiking neural network for real-time Spanish vowel phonemes recognition [PDF]
, 2017 This paper explores neuromorphic approach capabilities applied to real-time speech processing. A spiking recognition neural network composed of three types of neurons is proposed. These neurons are based on an integrative and fire model and are capable
Gómez Rodríguez, Francisco de Asís +3 more
core
Event-Driven Contrastive Divergence for Spiking Neuromorphic Systems
, 2013 Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification.
Cauwenberghs, Gert +4 more
core +1 more source
Advanced Functional Materials, EarlyView.In this study, the preparation techniques for silver‐based gas diffusion electrodes used for the electrochemical reduction of carbon dioxide (eCO2R) are systematically reviewed and compared with respect to their scalability. In addition, physics‐based and data‐driven modeling approaches are discussed, and a perspective is given on how modeling can aid ...
Simon Emken +6 more
wiley +1 more source
Neural Plasticity, 2020 Electrophysiological studies have shown that mammalian primary visual cortex are selective for the orientations of visual stimuli. Inspired by this mechanism, we propose a hierarchical spiking neural network (SNN) for image classification.
Xiumin Li, Hao Yi, Shengyuan Luo
doaj +1 more source