title: Neuron classification based on temporal firing patterns by the dynamical analysis with changing time resolution (DCT) method.
abstract: Spike train data of many neurons can be obtained by multirecording techniques; however, the data make it difficult to estimate the connective structure in a large network. Neuron classification should be helpful in that regard, assuming that multiple neurons having similar connections with other neurons show a similar temporal firing pattern. We propose a novel method for classifying neurons based on temporal firing patterns of spike train data called the dynamical analysis with changing time resolution (DCT) method. The DCT method can evaluate temporal firing patterns by a simple algorithm with few arbitrary factors and automatically classify neurons by similarity of temporal firing patterns. In the DCT method, temporal firing patterns were objectively evaluated by analyzing their dependence on temporal resolution. We confirmed the effectiveness of the DCT method using actual spike train data.
authors.affiliation: Department of Physiology, Kinki University School of Medicine, 377-2 Ohno-Higashi, Osaka-Sayama, Osaka 589-8511, Japan. oshio@med.kindai.ac.jp
authors.forename: Ken-ichi
authors.initials: K
authors.lastname: Oshio
authors.forename: Satoshi
authors.initials: S
authors.lastname: Yamada
authors.forename: Michio
authors.initials: M
authors.lastname: Nakashima
mesh_terms: D000200:Action Potentials / Q000502:physiology; D016000:Cluster Analysis; D008959:Models, Neurological; D009474:Neurons / Q000145:classification* / Q000502:physiology; D013997:Time Factors