Dynamic Multiscale Modes of Resting State Brain Activity Detected by Entropy Field Decomposition [0.03%]
基于熵场分解的静息态脑活动动态多尺度模式发现
Lawrence R Frank,Vitaly L Galinsky
Lawrence R Frank
The ability of functional magnetic resonance imaging (FMRI) to noninvasively measure fluctuations in brain activity in the absence of an applied stimulus offers the possibility of discerning functional networks in the resting state of the b...
Learning Theory Estimates with Observations from General Stationary Stochastic Processes [0.03%]
一般平稳随机过程观察下的学习理论估计方法
Hanyuan Hang,Yunlong Feng,Ingo Steinwart et al.
Hanyuan Hang et al.
This letter investigates the supervised learning problem with observations drawn from certain general stationary stochastic processes. Here by general, we mean that many stationary stochastic processes can be included. We show that when the...
Henning U Voss
Henning U Voss
The leaky integrator, the basis for many neuronal models, possesses a negative group delay when a time-delayed recurrent inhibition is added to it. By means of this delay, the leaky integrator becomes a predictor for some frequency componen...
Hermite Functional Link Neural Network for Solving the Van der Pol-Duffing Oscillator Equation [0.03%]
求解van der Pol-Duffing振子方程的Hermite函数链接神经网络方法
Susmita Mall,S Chakraverty
Susmita Mall
Hermite polynomial-based functional link artificial neural network (FLANN) is proposed here to solve the Van der Pol-Duffing oscillator equation. A single-layer hermite neural network (HeNN) model is used, where a hidden layer is replaced b...
Credible Intervals for Precision and Recall Based on a K-Fold Cross-Validated Beta Distribution [0.03%]
基于K折交叉验证Beta分布的精确度和召回率可信区间
Yu Wang,Jihong Li
Yu Wang
In typical machine learning applications such as information retrieval, precision and recall are two commonly used measures for assessing an algorithm's performance. Symmetrical confidence intervals based on K-fold cross-validated t distrib...
Clique-Based Neural Associative Memories with Local Coding and Precoding [0.03%]
基于局部编/解码的神经联想记忆网络及其克隆体研究
Asieh Abolpour Mofrad,Matthew G Parker,Zahra Ferdosi et al.
Asieh Abolpour Mofrad et al.
Techniques from coding theory are able to improve the efficiency of neuroinspired and neural associative memories by forcing some construction and constraints on the network. In this letter, the approach is to embed coding techniques into n...
Varun Raj Kompella,Matthew Luciw,Marijn Frederik Stollenga et al.
Varun Raj Kompella et al.
Consider a self-motivated artificial agent who is exploring a complex environment. Part of the complexity is due to the raw high-dimensional sensory input streams, which the agent needs to make sense of. Such inputs can be compactly encoded...
Asymmetry of Neuronal Combinatorial Codes Arises from Minimizing Synaptic Weight Change [0.03%]
神经元组合码的不对称性源于最小化突触权重变化
Christian Leibold,Mauro M Monsalve-Mercado
Christian Leibold
Synaptic change is a costly resource, particularly for brain structures that have a high demand of synaptic plasticity. For example, building memories of object positions requires efficient use of plasticity resources since objects can easi...
Karthik Devarajan,Vincent C K Cheung
Karthik Devarajan
A unified approach to nonnegative matrix factorization based on the theory of generalized linear models is proposed. This approach embeds a variety of statistical models, including the exponential family, within a single theoretical framewo...
Scalable Semisupervised Functional Neurocartography Reveals Canonical Neurons in Behavioral Networks [0.03%]
可扩展的半监督功能神经 картография 揭示行为网络中的典型神经元
E Paxon Frady,Ashish Kapoor,Eric Horvitz et al.
E Paxon Frady et al.
Large-scale data collection efforts to map the brain are underway at multiple spatial and temporal scales, but all face fundamental problems posed by high-dimensional data and intersubject variability. Even seemingly simple problems, such a...