Carina Curto,Katherine Morrison
Carina Curto
Threshold-linear networks are a common class of firing rate models that describe recurrent interactions among neurons. Unlike their linear counterparts, these networks generically possess multiple stable fixed points (steady states), making...
Guillaume Lajoie,Lai-Sang Young
Guillaume Lajoie
This work is part of an effort to understand the neural basis for our visual system's ability, or failure, to accurately track moving visual signals. We consider here a ring model of spiking neurons, intended as a simplified computational m...
The Enhanced Rise and Delayed Fall of Memory in a Model of Synaptic Integration: Extension to Discrete State Synapses [0.03%]
一种突触整合模型中记忆的增强增长与延缓衰减：从连续状态突触到离散状态突触的扩展
Terry Elliott
Terry Elliott
Integrate-and-express models of synaptic plasticity propose that synapses may act as low-pass filters, integrating synaptic plasticity induction signals in order to discern trends before expressing synaptic plasticity. We have previously sh...
Xue-Zhong Wang,Yimin Wei,Predrag S Stanimirović
Xue-Zhong Wang
Two complex Zhang neural network (ZNN) models for computing the Drazin inverse of arbitrary time-varying complex square matrix are presented. The design of these neural networks is based on corresponding matrix-valued error functions arisin...
The Geometry of Plasticity-Induced Sensitization in Isoinhibitory Rate Motifs [0.03%]
塑性诱导的感觉化在等抑制率图式中的几何学分布
Gautam Kumar,ShiNung Ching
Gautam Kumar
A well-known phenomenon in sensory perception is desensitization, wherein behavioral responses to persistent stimuli become attenuated over time. In this letter, our focus is on studying mechanisms through which desensitization may be media...
Balázs Hangya,Joshua I Sanders,Adam Kepecs
Balázs Hangya
Decision confidence is a forecast about the probability that a decision will be correct. From a statistical perspective, decision confidence can be defined as the Bayesian posterior probability that the chosen option is correct based on the...
Learning Minimal Latent Directed Information Polytrees [0.03%]
最小限 latent directed information polytree的学习
Jalal Etesami,Negar Kiyavash,Todd Coleman
Jalal Etesami
We propose an approach for learning latent directed polytrees as long as there exists an appropriately defined discrepancy measure between the observed nodes. Specifically, we use our approach for learning directed information polytrees whe...
Karl Friston,Ivan Herreros
Karl Friston
This letter offers a computational account of Pavlovian conditioning in the cerebellum based on active inference and predictive coding. Using eyeblink conditioning as a canonical paradigm, we formulate a minimal generative model that can ac...
Linking Neuromodulated Spike-Timing Dependent Plasticity with the Free-Energy Principle [0.03%]
基于自由能原理的神经调制时序依赖性可塑性的研究
Takuya Isomura,Koji Sakai,Kiyoshi Kotani et al.
Takuya Isomura et al.
The free-energy principle is a candidate unified theory for learning and memory in the brain that predicts that neurons, synapses, and neuromodulators work in a manner that minimizes free energy. However, electrophysiological data elucidati...
Dimensionality-Dependent Generalization Bounds for k-Dimensional Coding Schemes [0.03%]
维度相关的广义界限化k维编码方案
Tongliang Liu,Dacheng Tao,Dong Xu
Tongliang Liu
The k-dimensional coding schemes refer to a collection of methods that attempt to represent data using a set of representative k-dimensional vectors and include nonnegative matrix factorization, dictionary learning, sparse coding, k-means c...