title: Adaptation reduces variability of the neuronal population code.
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.
authors: Neuroinformatics and Theoretical Neuroscience, Freie Universität Berlin and BCCN-Berlin, Berlin, Germany. farzad@zedat.fu-berlin.de
authors: Farzad
authors: F
authors: Farkhooi
authors: Eilif
authors: E
authors: Muller
authors: Martin P
authors: MP
authors: Nawrot
mesh_terms: D000222:Adaptation, Physiological; D008954:Models, Biological; D009474:Neurons / Q000166:cytology*; D013569:Synapses / Q000378:metabolism