Shape Matters: Few-Shot Object Classification From High-Information Contour Features [0.03%]
形必有因：基于高信息轮廓特征的Few-shot物体分类
Maria Osório,Alexandre Bernardino,Andreas Wichert
Maria Osório
A key challenge in visual object recognition is developing models that generalize from limited data while maintaining transparency in their decision making. We propose a biologically inspired model that addresses both issues by classifying ...
Joshua Hess,Quaid Morris
Joshua Hess
This letter shows that generative diffusion processes converge to associative memory systems at vanishing noise levels and characterizes the stability, robustness, memorization, and generation dynamics of both model classes. Morse-Smale dyn...
Neuronal Spike Trains as Functional-Analytic Distributions: Representation, Analysis, and Significance [0.03%]
神经脉冲序列的功能分析分布表示、分析及其意义
Gabriel A Silva
Gabriel A Silva
The action potential constitutes the digital component of the signaling dynamics of neurons. But the biophysical nature of the full-time course of the action potential associated with changes in membrane potential is mathematically distinct...
Quantifying Information Stored in Synaptic Connections Rather Than in Firing Activities of Neural Networks [0.03%]
量化存储在突触连接中的信息而非神经网络放电活动中的信息
Xinhao Fan,Shreesh P Mysore
Xinhao Fan
A cornerstone of our understanding of both biological and artificial neural networks is that they store information in the strengths of synaptic connections among the neurons. However, in contrast to the well-established theory for quantify...
Unsupervised Feature Selection Using Bayesian Tucker Decomposition [0.03%]
基于贝叶斯Tucker分解的无监督特征选择方法研究
Y-H Taguchi,Yoh-Ichi Mototake
Y-H Taguchi
In this letter, we propose Bayesian Tucker decomposition (BTuD) in which the residuals are supposed to follow. Although we have proposed an algorithm to perform the proposed BTuD, the conventional higher-order orthogonal iteration can gener...
Cross-Frequency Coupling as a Neural Substrate for Prediction Error Evaluation: A Laminar Neural Mass Modeling Approach [0.03%]
预测误差评估的神经基质：层状神经质量模型方法
Giulio Ruffini,Edmundo Lopez-Sola,Raul Palma et al.
Giulio Ruffini et al.
Predictive coding frameworks suggest neural computations rely on hierarchical error minimization, yet the neural implementation of this inference remains unclear. We propose that cross-frequency coupling (CFC) furnishes fundamental mechanis...
A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling [0.03%]
一种不确定条件下的决策模型：顺序抽样自强化学习模型
Jamal Esmaily,Rani Moran,Yasser Roudi et al.
Jamal Esmaily et al.
Although evidence integration to the boundary model has successfully explained a wide range of behavioral and neural data in decision making under uncertainty, how animals learn and optimize the boundary remains unresolved. Here, we propose...
DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning [0.03%]
分布与正/负乐观主义在强化学习中的应用
Taisuke Kobayashi
Taisuke Kobayashi
In reinforcement learning (RL), temporal difference (TD) error is known to be related to the firing rate of dopamine neurons. It has been observed that each dopamine neuron does not behave uniformly, but each responds to the TD error in an ...
Prashant Rangarajan,Rajesh P N Rao
Prashant Rangarajan
Active inference, a neutrally inspired model for inferring actions based on the free energy principle (FEP), has been proposed as a unifying framework for understanding perception, action, and learning in the brain. Active inference has pre...
W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators [0.03%]
W-核及其主空间：贝叶斯估计的频率评价方法研究
Yukito Iba
Yukito Iba
Evaluating the variability of posterior estimates is a key aspect of Bayesian model assessment. In this study, we focus on the posterior covariance matrix W, defined through the log likelihoods of individual observations. Previous studies, ...