title: [Neural representation of time].
abstract: Temporal information is essential for perception and behavior. Although the neural substrates for temporal processing have been elucidated in many different conditions, how individual neurons in each network represent time remains largely unknown. Here we review previous models of time representation in the brain, and propose that these models can be classified into four different groups based on two viewpoints. The first viewpoint is that temporal information is either prospective or retrospective. For example, the online control of movement timing requires prospective or predictive information, whereas the duration discrimination of previously presented stimuli depends on retrospective temporal information. The other viewpoint is whether neuronal coding is based on modulation of the firing rate in each neuron (rate coding) or the occurrence of synchronous activity across multiple neurons (temporal coding). The accumulator model and state-dependence model both represent time by modulating the rate of neuronal firing depending on the elapsed time, thereby providing the prospective and retrospective information, respectively. In contrast, temporal coding is used by the coincidence detection and entrainment/synchronization models acquired through learning. This classification might be helpful for comprehensive understanding of the neuronal mechanisms of temporal processing, each of which is implemented by the intrinsic property of each sensory system and/or by a dedicated network specialized for timing. We also propose a model incorporating serial stages of temporal processing to reproduce a fixed time interval, and suggest that future physiological and pharmacological experiments might prove our hypothesis.
authors.affiliation: Department of Physiology, Hokkaido University School of Medicine.
authors.forename: Masaki
authors.initials: M
authors.lastname: Tanaka
authors.forename: Jun
authors.initials: J
authors.lastname: Kunimatsu
authors.forename: Shogo
authors.initials: S
authors.lastname: Ohmae
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000818:Animals; D001921:Brain / Q000502:physiology*; D006801:Humans; D007858:Learning / Q000502:physiology; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D013998:Time Perception / Q000502:physiology*