Predicting the synaptic information efficacy in cortical layer 5 pyramidal neurons using a minimal integrate-and-fire model
type: Item
instance of: scholarly article
title: Predicting the synaptic information efficacy in cortical layer 5 pyramidal neurons using a minimal integrate-and-fire model
zbMATH Open document ID: 1161.92011
author: Michael London
author: Matthew E. Larkum
author: Michael Häusser
published in: Biological Cybernetics
publication date: 2009-02-03T00:00:00Z
Mathematics Subject Classification ID: 92C20
Mathematics Subject Classification ID: 92-08
zbMATH DE Number: 5502384
zbMATH Keywords: synaptic information efficacy
zbMATH Keywords: linear integrate-and-fire model
zbMATH Keywords: predicting every spike
zbMATH Keywords: layer 5 cortical pyramidal neuron
MaRDI profile type: Publication
full work available at URL: s00422-008-0268-3
OpenAlex ID: W2106474466
Wikidata QID: Q33557166
cites work: Computation in a Single Neuron: Hodgkin and Huxley Revisited
cites work: Firing Rate of the Noisy Quadratic Integrate-and-Fire Neuron
cites work: Estimating the entropy of binary time series: methodology, some theory and a simulation study
cites work: Predicting spike timing of neocortical pyramidal neurons by simple threshold models
cites work: Estimating Entropy Rates with Bayesian Confidence Intervals
cites work: The context-tree weighting method: basic properties
DOI: 10.1007/S00422-008-0268-3
description: scientific article; zbMATH DE number 5502384