Mojtaba Madadi Asl
Mojtaba Madadi Asl
Triboelectric nanogenerators for neural data interpretation: bridging multi-sensing interfaces with neuromorphic and deep learning paradigms [0.03%]
用于神经数据解释的摩擦纳米发电机：多传感接口与神经形态和深度学习范式的桥梁技术
Lingli Gan,Shuqin Yuan,Min Guo et al.
Lingli Gan et al.
The rapid growth of computational neuroscience and brain-computer interface (BCI) technologies require efficient, scalable, and biologically compatible approaches for neural data acquisition and interpretation. Traditional sensors and signa...
Neural heterogeneity as a unifying mechanism for efficient learning in spiking neural networks [0.03%]
神经异质性作为尖峰神经网络高效学习的统一机制
Fudong Zhang,Jingjing Cui
Fudong Zhang
The brain is a highly diverse and heterogeneous network, yet the functional role of this neural heterogeneity remains largely unclear. Despite growing interest in neural heterogeneity, a comprehensive understanding of how it influences comp...
Interleaving cortex-analog mixing improves deep non-negative matrix factorization networks [0.03%]
类脑交织混合改善深度非负矩阵分解网络
Mahbod Nouri,David Rotermund,Alberto Garcia-Ortiz et al.
Mahbod Nouri et al.
Considering biological constraints in artificial neural networks has led to dramatic improvements in performance. Nevertheless, to date, the positivity of long-range signals in the cortex has not been shown to yield improvements. While Non-...
Universal differential equations as a unifying modeling language for neuroscience [0.03%]
万能微分方程作为神经科学统一建模语言的优势
Ahmed El-Gazzar,Marcel van Gerven
Ahmed El-Gazzar
The rapid growth of large-scale neuroscience datasets has spurred diverse modeling strategies, ranging from mechanistic models grounded in biophysics, to phenomenological descriptions of neural dynamics, to data-driven deep neural networks ...
Multiscale intracranial EEG dynamics across sleep-wake states: toward memory-related processing [0.03%]
跨越睡眠-觉醒状态的多尺度脑内电图动态变化：面向记忆相关处理
Juan M Tenti,Monserrat Pallares Di Nunzio,Marisa A Bab et al.
Juan M Tenti et al.
Sleep is known to support memory consolidation through a complex interplay of neural dynamics across multiple timescales. Using intracranial EEG (iEEG) recordings from patients undergoing clinical monitoring, we characterize spectral activi...
Sudden restructuring of memory representations in recurrent neural networks with repeated stimulus presentations [0.03%]
重复刺激呈现下循环神经网络中记忆表征的突变式重组现象
Jonathon R Howlett
Jonathon R Howlett
While acquisition curves in human learning averaged at the group level display smooth, gradual changes in performance, individual learning curves across cognitive domains reveal sudden, discontinuous jumps in performance. Similar thresholdi...
An AI methodology to reduce training intensity, error rates, and size of neural networks [0.03%]
一种减少神经网络训练强度、错误率和规模的AI方法学
Thaddeus J A Kobylarz
Thaddeus J A Kobylarz
Massive computing systems are required to train neural networks. The prodigious amount of consumed energy makes the creation of AI applications significant polluters. Despite the enormous training effort, neural network error rates limit it...
Haoming Yang,Pramod Kc,Panyu Chen et al.
Haoming Yang et al.
Neuronal synchronization refers to the temporal coordination of activity across populations of neurons, a process that underlies coherent information processing, supports the encoding of diverse sensory stimuli, and facilitates adaptive beh...
Using noise to distinguish between system and observer effects in multimodal neuroimaging [0.03%]
利用噪声区分多模态神经影像中的系统效应和观察者效应
Erik D Fagerholm,Hirokazu Tanaka,Gregory Scott et al.
Erik D Fagerholm et al.
Introduction: It has become increasingly common to record brain activity simultaneously at more than one spatiotemporal scale. Here, we address a central question raised by such cross-scale datasets: do they reflect the s...