Publication

Adaptation reduces variability of the neuronal population code

Eilif Benjamin Muller
2011
Journal Articles

Abstract

Sequences of events in noise-driven excitable systems with slow variables often show serial correlations among their intervals of events. Here, we employ a master equation for generalized non-renewal processes to calculate the interval and count statistics of superimposed processes governed by a slow adaptation variable. For an ensemble of neurons with spike-frequency adaptation, this results in the regularization of the population activity and an enhanced postsynaptic signal decoding. We confirm our theoretical results in a population of cortical neurons recorded in vivo.

Official source

https://infoscience.epfl.ch/entities/publication/0563a9c1-aa2b-4b95-8043-a9eb8bcc1db6

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Eilif Benjamin Muller
2011
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/0563a9c1-aa2b-4b95-8043-a9eb8bcc1db6

About this result

Related concepts (31)

Biological neuron model

Biological neuron models, also known as a spiking neuron models, are mathematical descriptions of the properties of certain cells in the nervous system that generate sharp electrical potentials across their cell membrane, roughly one millisecond in duration, called action potentials or spikes (Fig. 2). Since spikes are transmitted along the axon and synapses from the sending neuron to many other neurons, spiking neurons are considered to be a major information processing unit of the nervous system.

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Adaptation reduces variability of the neuronal population code

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