2006
DOI: 10.1126/science.1127240
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Modeling Single-Neuron Dynamics and Computations: A Balance of Detail and Abstraction

Abstract: The fundamental building block of every nervous system is the single neuron. Understanding how these exquisitely structured elements operate is an integral part of the quest to solve the mysteries of the brain. Quantitative mathematical models have proved to be an indispensable tool in pursuing this goal. We review recent advances and examine how single-cell models on five levels of complexity, from black-box approaches to detailed compartmental simulations, address key questions about neural dynamics and sign… Show more

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Cited by 416 publications
(288 citation statements)
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“…In the past decades, many experimental and theoretical studies have focussed on the relation between spike input and output of neurons and of populations of neurons with the aim to understand information processing by (groups of) neurons (see, e.g. Tiesinga et al 2001;Dayan and Abbott 2001;Escalona et al 2002;Kuhn et al 2004;Herz et al 2006). In general, most of these studies have investigated the response of a neuron to a single stimulus, which may be encoded in single-unit or multi-unit activity.…”
Section: Introductionmentioning
confidence: 99%
“…In the past decades, many experimental and theoretical studies have focussed on the relation between spike input and output of neurons and of populations of neurons with the aim to understand information processing by (groups of) neurons (see, e.g. Tiesinga et al 2001;Dayan and Abbott 2001;Escalona et al 2002;Kuhn et al 2004;Herz et al 2006). In general, most of these studies have investigated the response of a neuron to a single stimulus, which may be encoded in single-unit or multi-unit activity.…”
Section: Introductionmentioning
confidence: 99%
“…Neural activation is denoted in formal terms by utilizing single compartment cell models with gradual activation dynamics [10]. The cell activity is described by the membrane potential subject to changes given external excitatory and inhibitory synaptic input.…”
Section: Neural Computational Mechanismsmentioning
confidence: 99%
“…As the density of I A channels increases from zero, the membrane is able to support progressively lower firing frequencies at spiking threshold. The resulting linearization of the FI curve from Type II to Type I has clear consequences for encoding information in firing rate as well as other computational properties such as thresholding and gain scaling, all of which are subjects of intense research (10)(11)(12)(13)(14)(15)(16)(17).…”
mentioning
confidence: 99%