Results 71 to 80 of about 215,411 (303)

Spike‐Based Spatiotemporal Processing Enabled by Oscillation Neuron for Energy‐Efficient Artificial Sensory Systems

Advanced Intelligent Systems, 2022
Developing an energy‐efficient artificial sensory system is of great significance for neuroprosthesis, neurorobotics, and intelligent human–machine interfaces.
Shuai Zhong, Yishu Zhang, Hao Zheng, Fangwen Yu, Rong Zhao   +4 more
doaj   +1 more source

Ofatumumab Therapy and Long‐Term Seizure Outcomes of Patients With Relapsing Anti‐NMDA Receptor Encephalitis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective There is a lack of studies on seizure outcomes associated with ofatumumab therapy in patients for relapsing anti‐N‐methyl‐D‐aspartate (NMDA) receptor encephalitis. We aimed to evaluate long‐term seizure outcomes of ofatumumab therapy for relapsing anti‐NMDA receptor encephalitis.
Jian Wang, Mengjiao Li, Ping Kong
wiley   +1 more source

Belief Propagation in Networks of Spiking Neurons [PDF]

Neural Computation, 2009
From a theoretical point of view, statistical inference is an attractive model of brain operation. However, it is unclear how to implement these inferential processes in neuronal networks. We offer a solution to this problem by showing in detailed simulations how the belief propagation algorithm on a factor graph can be embedded in a network of ...
Andreas Steimer, Wolfgang Maass 0001, Rodney J. Douglas   +2 more
openaire   +4 more sources

WiN-GUI: A graphical tool for neuron-based encoding

SoftwareX
Neuromorphic computing relies on event-based, energy-efficient communication, inherently implying the need for conversion between real-valued (sensory) data and binary, sparse spiking representation.
Simon F. Müller-Cleve, Fernando M. Quintana, Vittorio Fra, Pedro L. Galindo, Fernando Perez-Peña, Gianvito Urgese, Chiara Bartolozzi   +6 more
doaj   +1 more source

Engineering Spiking Neurons Using Threshold Switching Devices for High-Efficient Neuromorphic Computing

Frontiers in Neuroscience, 2022
Inspired by the human brain, the spike-based neuromorphic system has attracted strong research enthusiasm because of the high energy efficiency and powerful computational capability, in which the spiking neurons and plastic synapses are two fundamental ...
Yanting Ding, Yanting Ding, Yajun Zhang, Xumeng Zhang, Xumeng Zhang, Xumeng Zhang, Pei Chen, Pei Chen, Zefeng Zhang, Yue Yang, Yue Yang, Lingli Cheng, Lingli Cheng, Chen Mu, Chen Mu, Ming Wang, Ming Wang, Du Xiang, Du Xiang, Guangjian Wu, Guangjian Wu, Guangjian Wu, Keji Zhou, Keji Zhou, Keji Zhou, Zhe Yuan, Qi Liu, Qi Liu, Qi Liu   +28 more
doaj   +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga, Marco Zaffalon   +3 more
wiley   +1 more source

Stationary Bumps in Networks of Spiking Neurons [PDF]

Neural Computation, 2001
We examine the existence and stability of spatially localized “bumps” of neuronal activity in a network of spiking neurons. Bumps have been proposed in mechanisms of visual orientation tuning, the rat head direction system, and working memory. We show that a bump solution can exist in a spiking network provided the neurons fire asynchronously within ...
Carlo R. Laing, Carson C. Chow
openaire   +2 more sources

Automatic fitting of spiking neuron models to electrophysiological recordings

Frontiers in Neuroinformatics, 2010
Spiking models can accurately predict the spike trains produced by cortical neurons in response to somatically injected currents. Since the specific characteristics of the model depend on the neuron, a computational method is required to fit models to ...
Cyrille Rossant, Dan F M Goodman, Jonathan Platkiewicz, Romain Brette   +3 more
doaj   +1 more source

A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy

Advanced Engineering Materials, EarlyView.
A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle, Cesar Alvarez‐Llamas, Vincent Motto‐Ros, Christophe Dujardin, Jérémie Margueritat, David Rodney   +5 more
wiley   +1 more source

SIMULASI AKTIVITAS SPIKING MODEL-MODEL NEURON MENGGUNAKAN METODE EULER [PDF]

, 2020
Simulation spiking activity models of neurons using Euler method has been done. This study aims to simulate activity spiking in neuron models. The neuron models used are the Hodgkin-Huxley neuron model, the Integrate and Fireneuron model, the Wilson ...
Ahmad Syahid, NIM.: 15620003
core   +1 more source