Ken Takano,Hideitsu Hino,Shotaro Akaho et al.
Ken Takano et al.
This study considers the common situation in data analysis when there are few observations of the distribution of interest or the target distribution, while abundant observations are available from auxiliary distributions. In this situation...
Baosheng Yu,Meng Fang,Dacheng Tao
Baosheng Yu
Linear submodular bandits has been proven to be effective in solving the diversification and feature-based exploration problem in information retrieval systems. Considering there is inevitably a budget constraint in many web-based applicati...
An Online Structural Plasticity Rule for Generating Better Reservoirs [0.03%]
一种在线结构可塑性规则以生成更好的水库动力学系统模型
Subhrajit Roy,Arindam Basu
Subhrajit Roy
In this letter, we propose a novel neuro-inspired low-resolution online unsupervised learning rule to train the reservoir or liquid of liquid state machines. The liquid is a sparsely interconnected huge recurrent network of spiking neurons....
Vitaly L Galinsky,Lawrence R Frank
Vitaly L Galinsky
We present a quantitative statistical analysis of pairwise crossings for all fibers obtained from whole brain tractography that confirms with high confidence that the brain grid theory (Wedeen et al., 2012a ) is not supported by the evidenc...
Exponential Stability of Almost Periodic Solutions for Memristor-Based Neural Networks with Distributed Leakage Delays [0.03%]
基于忆阻器的分布式漏延神经网络几乎周期解的指数稳定性
Changjin Xu,Peiluan Li,Yicheng Pang
Changjin Xu
In this letter, we deal with a class of memristor-based neural networks with distributed leakage delays. By applying a new Lyapunov function method, we obtain some sufficient conditions that ensure the existence, uniqueness, and global expo...
Chaotic Resonance in Coupled Inferior Olive Neurons with the Llinás Approach Neuron Model [0.03%]
采用Llinás神经元模型的 inferior olive 神经元混沌共振
Sou Nobukawa,Haruhiko Nishimura
Sou Nobukawa
It is well known that cerebellar motor control is fine-tuned by the learning process adjusted according to rich error signals from inferior olive (IO) neurons. Schweighofer and colleagues proposed that these signals can be produced by chaot...
Continuous Online Sequence Learning with an Unsupervised Neural Network Model [0.03%]
基于无监督神经网络模型的连续在线序列学习方法研究
Yuwei Cui,Subutai Ahmad,Jeff Hawkins
Yuwei Cui
The ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, hierarchical temporal memory (HTM) sequence memory recently has bee...
Hien D Nguyen,Luke R Lloyd-Jones,Geoffrey J McLachlan
Hien D Nguyen
The mixture-of-experts (MoE) model is a popular neural network architecture for nonlinear regression and classification. The class of MoE mean functions is known to be uniformly convergent to any unknown target function, assuming that the t...
Karthik H Shankar,Inder Singh,Marc W Howard
Karthik H Shankar
Predicting the timing and order of future events is an essential feature of cognition in higher life forms. We propose a neural mechanism to nondestructively translate the current state of spatiotemporal memory into the future, so as to con...
Neural Quadratic Discriminant Analysis: Nonlinear Decoding with V1-Like Computation [0.03%]
神经二次判别分析：具有V1计算类似的非线性解码
Marino Pagan,Eero P Simoncelli,Nicole C Rust
Marino Pagan
Linear-nonlinear (LN) models and their extensions have proven successful in describing transformations from stimuli to spiking responses of neurons in early stages of sensory hierarchies. Neural responses at later stages are highly nonlinea...