Publication

A new Mathematical Framework to Understand Single Neuron Computations

Publications associées (141)

Graph Chatbot

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Connectez-vous pour utiliser Chat avec Graph Search

Effect of lateral connections on the accuracy of the population code for a network of spiking neurons

Wulfram Gerstner

We study how neuronal connections in a population of spiking neurons affect the accuracy of stimulus estimation. Neurons in our model code for a one-dimensional orientation variable

\phi

. Connectivity between two neurons depends on the absolute differenc ...

2001

A new Mathematical Framework to Understand Single Neuron Computations

Graph Chatbot

Chattez avec Graph Search

Generalized integrate-and-fire models of neuronal activity approximate spike trains of a detailed model to a high degree of accuracy

Regulation of brain proteolytic activity is necessary for the in vivo function of NMDA receptors

Correlation maps allow neuronal electrical properties to be predicted from single-cell gene expression profiles in rat neocortex

Deriving physical connectivity from neuronal morphology

Stable Propagation of Activity Pulses in Populations of Spiking Neurons

Spike frequency adaptation and neocortical rhythms

Effects of grouping in contextual modulation

A framework for spiking neuron models - the spike response model

Effect of lateral connections on the accuracy of the population code for a network of spiking neurons

Coding properties of spiking neurons: reverse- and cross-correlations

Deriving physical connectivity from neuronal morphology

Correlation maps allow neuronal electrical properties to be predicted from single-cell gene expression profiles in rat neocortex

Regulation of brain proteolytic activity is necessary for the in vivo function of NMDA receptors

Generalized integrate-and-fire models of neuronal activity approximate spike trains of a detailed model to a high degree of accuracy

Stable Propagation of Activity Pulses in Populations of Spiking Neurons

Spike frequency adaptation and neocortical rhythms

Effects of grouping in contextual modulation

Coding properties of spiking neurons: reverse- and cross-correlations

Effect of lateral connections on the accuracy of the population code for a network of spiking neurons

A framework for spiking neuron models - the spike response model