Cross-Frequency Coupling as a Neural Substrate for Prediction Error Evaluation: A Laminar Neural Mass Modeling Approach [0.03%]
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, ...
A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing [0.03%]
一种基于隐马尔可夫模型的高维计算序列分类方法
Krzysztof Ślot,Jakub Bednarski,Kacper Kubicki et al.
Krzysztof Ślot et al.
This letter introduces a novel method for discrete-sequence classification within the hyperdimensional computing (HDC) paradigm. The method, inspired by the concept of hidden Markov models (HMM), implements mechanisms that effectively addre...
Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics [0.03%]
基于圆形统计的电生理相位脑网络稀疏图模型方法研究
Issey Sukeda,Takeru Matsuda
Issey Sukeda
Identifying phase coupling from electrophysiological signals recorded by multiple electrodes, such as electroencephalogram (EEG) and electrocorticography (ECoG), helps neuroscientists and clinicians understand underlying brain structures or...
Toward a Computational Phenomenology of Meditative Deconstruction: "Letting Go" and the Deconstruction of Experience With Active Inference [0.03%]
迈向冥想解构的计算现象学：“放下”与经验解构的主动推理模型
Shawn Prest
Shawn Prest
Meditative experience has long been associated with conceptual attenuation, reduced reactivity to phenomena, increased present moment perception, and more pleasant experience. However, the computational mechanisms underlying such meditative...
Competition Between Memory Updating and Differentiation Emerges From Intrinsic Network Dynamics [0.03%]
竞争的记忆更新和分化源于内在的网络动力学
Julia Pronoza,Nina Liedtke,Marius Boeltzig et al.
Julia Pronoza et al.
When an event occurs that is similar to a previous experience, the original episodic memory can be modified with new information (updating) or a new memory can be encoded separately (differentiation). Prediction errors, the deviation betwee...
Domain Adaptation With Additional Features via Label-Aware and Graph-Based Fused Gromov-Wasserstein Optimal Transport [0.03%]
基于标签感知和图融合的Gromov-瓦瑟斯坦领域适应方法
Toshimitsu Aritake,Hideitsu Hino
Toshimitsu Aritake
In many domain adaptation tasks, the source and target domains share an identical feature space, so the domain gap arises only from the distributional shift. In practice; however, new-target-only features (e.g., sensors added after training...