title: The log-dynamic brain: how skewed distributions affect network operations.
abstract: We often assume that the variables of functional and structural brain parameters - such as synaptic weights, the firing rates of individual neurons, the synchronous discharge of neural populations, the number of synaptic contacts between neurons and the size of dendritic boutons - have a bell-shaped distribution. However, at many physiological and anatomical levels in the brain, the distribution of numerous parameters is in fact strongly skewed with a heavy tail, suggesting that skewed (typically lognormal) distributions are fundamental to structural and functional brain organization. This insight not only has implications for how we should collect and analyse data, it may also help us to understand how the different levels of skewed distributions - from synapses to cognition - are related to each other.
authors.affiliation: 1] New York University Neuroscience Institute, New York University Langone Medical Center, New York, New York 10016, USA. [2] Center for Neural Science, New York University, New York, New York 10003, USA.
authors.forename: György
authors.initials: G
authors.lastname: Buzsáki
authors.affiliation: 1] New York University Neuroscience Institute, New York University Langone Medical Center, New York, New York 10016, USA. [2] Allen Institute for Brain Science, 551 North 34th Street, Seattle, Washington 98103, USA.
authors.forename: Kenji
authors.initials: K
authors.lastname: Mizuseki
mesh_terms: D000818:Animals; D001921:Brain / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*