Learning Polychronous Neuronal Groups Using Joint Weight-Delay Spike-Timing-Dependent Plasticity [0.03%]
基于联合权重-延迟的 spike-timing-dependent可塑性学习多时钟神经元群体
Haoqi Sun,Olga Sourina,Guang-Bin Huang
Haoqi Sun
Polychronous neuronal group (PNG), a type of cell assembly, is one of the putative mechanisms for neural information representation. According to the reader-centric definition, some readout neurons can become selective to the information re...
A Note on Divergences [0.03%]
论散度
Xiao Liang
Xiao Liang
In many areas of neural computation, like learning, optimization, estimation, and inference, suitable divergences play a key role. In this note, we study the conjecture presented by Amari ( 2009 ) and find a counterexample to show that the ...
The Functional Segregation and Integration Model: Mixture Model Representations of Consistent and Variable Group-Level Connectivity in fMRI [0.03%]
功能隔离与整合模型：fMRI组水平连接一致性和变异性的混合模型表示方法
Nathan W Churchill,Kristoffer Madsen,Morten Mørup
Nathan W Churchill
The brain consists of specialized cortical regions that exchange information between each other, reflecting a combination of segregated (local) and integrated (distributed) processes that define brain function. Functional magnetic resonance...
Simulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON [0.03%]
用于推进脑研究的仿真神经技术：在NEURON中并行化大型网络
William W Lytton,Alexandra H Seidenstein,Salvador Dura-Bernal et al.
William W Lytton et al.
Large multiscale neuronal network simulations are of increasing value as more big data are gathered about brain wiring and organization under the auspices of a current major research initiative, such as Brain Research through Advancing Inno...
Integrator or Coincidence Detector: A Novel Measure Based on the Discrete Reverse Correlation to Determine a Neuron's Operational Mode [0.03%]
整合器还是巧合检测器：一种基于离散逆相关的新度量方法，用于确定神经元的工作模式
Jacob Kanev,Achilleas Koutsou,Chris Christodoulou et al.
Jacob Kanev et al.
In this letter, we propose a definition of the operational mode of a neuron, that is, whether a neuron integrates over its input or detects coincidences. We complete the range of possible operational modes by a new mode we call gap detectio...
Simon de Montigny,Benoît R Mâsse
Simon de Montigny
Error backpropagation in networks of spiking neurons (SpikeProp) shows promise for the supervised learning of temporal patterns. However, its widespread use is hindered by its computational load and occasional convergence failures. In this ...
Identification of Stable Spike-Timing-Dependent Plasticity from Spiking Activity with Generalized Multilinear Modeling [0.03%]
基于广义多线性模型的稳定 spike-timing 依赖可塑性的识别方法研究
Brian S Robinson,Theodore W Berger,Dong Song
Brian S Robinson
Characterization of long-term activity-dependent plasticity from behaviorally driven spiking activity is important for understanding the underlying mechanisms of learning and memory. In this letter, we present a computational framework for ...
Daniel Martí,Mattia Rigotti,Mingoo Seok et al.
Daniel Martí et al.
Neuromorphic engineering combines the architectural and computational principles of systems neuroscience with semiconductor electronics, with the aim of building efficient and compact devices that mimic the synaptic and neural machinery of ...
The Space-Clamped Hodgkin-Huxley System with Random Synaptic Input: Inhibition of Spiking by Weak Noise and Analysis with Moment Equations [0.03%]
带有随机突触输入的空间钳制霍奇金-赫胥黎系统：弱噪声下放电的抑制以及矩方程分析
Henry C Tuckwell,Susanne Ditlevsen
Henry C Tuckwell
We consider a classical space-clamped Hodgkin-Huxley model neuron stimulated by synaptic excitation and inhibition with conductances represented by Ornstein-Uhlenbeck processes. Using numerical solutions of the stochastic model system obtai...
Marie Levakova,Massimiliano Tamborrino,Lubomir Kostal et al.
Marie Levakova et al.
The time to the first spike after stimulus onset typically varies with the stimulation intensity. Experimental evidence suggests that neural systems use such response latency to encode information about the stimulus. We investigate the deco...