An information geometrical method is developed for characterizing or classifying neurons in cortical areas whose spike rates fluctuate in time.The interspike intervals(ISIs)of a spike sequence of a neuron is modeled a...An information geometrical method is developed for characterizing or classifying neurons in cortical areas whose spike rates fluctuate in time.The interspike intervals(ISIs)of a spike sequence of a neuron is modeled as a gamma process with a time-variant spike rate,a fixed shape parameter and a fixed absolute refractory period.We formulate the problem of estimating the fixed parameters as semiparametric estimation and apply an information geometrical method to derive the optimal estimators from a statistical viewpoint.展开更多
基金supported in part by Grant-in-Aid for Scientific Research(18300078)from the Ministry of Education,Culture,Sports,Science and Technology,Japan.
文摘An information geometrical method is developed for characterizing or classifying neurons in cortical areas whose spike rates fluctuate in time.The interspike intervals(ISIs)of a spike sequence of a neuron is modeled as a gamma process with a time-variant spike rate,a fixed shape parameter and a fixed absolute refractory period.We formulate the problem of estimating the fixed parameters as semiparametric estimation and apply an information geometrical method to derive the optimal estimators from a statistical viewpoint.
