Jiashuo Wang,Mengwen Yuan,Jiangrong Shen et al.
Jiashuo Wang et al.
Memory is a complex process in the brain that involves the encoding, consolidation, and retrieval of previously experienced stimuli. The brain is capable of rapidly forming memories of sensory input. However, applying the memory system to r...
Excitation-Inhibition Balance Controls Synchronization in a Simple Model of Coupled Phase Oscillators [0.03%]
一种耦合相振子模型中的兴奋-抑制平衡控制同步现象的方法
Satoshi Kuroki,Kenji Mizuseki
Satoshi Kuroki
Collective neuronal activity in the brain synchronizes during rest and desynchronizes during active behaviors, influencing cognitive processes such as memory consolidation, knowledge abstraction, and creative thinking. These states involve ...
Decision Threshold Learning in the Basal Ganglia for Multiple Alternatives [0.03%]
纹状体在多个选项抉择中的决策界阈学习机制研究
Thom Griffith,Sophie-Anne Baker,Nathan F Lepora
Thom Griffith
In recent years, researchers have integrated the historically separate, reinforcement learning (RL), and evidence-accumulation-to-bound approaches to decision modeling. A particular outcome of these efforts has been the RL-DDM, a model that...
Ville Karlsson,Joni Kämäräinen
Ville Karlsson
Spiking neural networks (SNNs) provide an energy-efficient alternative to traditional artificial neural networks, leveraging diverse neural encoding schemes such as rate, time-to-first-spike (TTFS), and population-based binary codes. Each e...
Dynamics and Bifurcation Structure of a Mean-Field Model of Adaptive Exponential Integrate-and-Fire Networks [0.03%]
具适应性的平均场指数集成与发射神经网络模型的动力学及分岔结构研究
Lionel Kusch,Damien Depannemaecker,Alain Destexhe et al.
Lionel Kusch et al.
The study of brain activity spans diverse scales and levels of description and requires the development of computational models alongside experimental investigations to explore integrations across scales. The high dimensionality of spiking ...
Dynamics of Continuous Attractor Neural Networks With Spike Frequency Adaptation [0.03%]
具有脉冲频率适应的连续吸引子神经网络的动力学特性研究
Yujun Li,Tianhao Chu,Si Wu
Yujun Li
Attractor neural networks consider that neural information is stored as stationary states of a dynamical system formed by a large number of interconnected neurons. The attractor property empowers a neural system to encode information robust...
Memory States From Almost Nothing: Representing and Computing in a Nonassociative Algebra [0.03%]
一无所有得记忆状态：非结合代数中的表示与计算
Stefan Reimann
Stefan Reimann
This letter presents a nonassociative algebraic framework for the representation and computation of information items in high-dimensional space. This framework is consistent with the principles of spatial computing and with the empirical fi...
Low-Rank, High-Order Tensor Completion via t- Product-Induced Tucker (tTucker) Decomposition [0.03%]
基于t-产品诱导的tucker分解的低秩高阶张量补全算法
Yaodong Li,Jun Tan,Peilin Yang et al.
Yaodong Li et al.
Recently, tensor singular value decomposition (t-SVD)-based methods were proposed to solve the low-rank tensor completion (LRTC) problem, which has achieved unprecedented success on image and video inpainting tasks. The t-SVD is limited to ...
Adding Space to Random Networks of Spiking Neurons: A Method Based on Scaling the Network Size [0.03%]
将尖峰神经元的随机网络中的空间添加进去：一种基于缩放网络大小的方法
Cecilia Romaro,Jose Roberto Castilho Piqueira,A C Roque
Cecilia Romaro
Many spiking neural network models are based on random graphs that do not include topological and structural properties featured in real brain networks. To turn these models into spatial networks that describe the topographic arrangement of...
Reformulation of RBM to Unify Linear and Nonlinear Dimensionality Reduction [0.03%]
RBM的重新构建统一了线性与非线性的降维方法
Jiangsheng You,Chun-Yen Liu
Jiangsheng You
A restricted Boltzmann machine (RBM) is a two-layer neural network with shared weights and has been extensively studied for dimensionality reduction, data representation, and recommendation systems in the literature. The traditional RBM req...