title: Maximum likelihood decoding of neuronal inputs from an interspike interval distribution.
abstract: An expression for the probability distribution of the interspike interval of a leaky integrate-and-fire (LIF) model neuron is rigorously derived, based on recent theoretical developments in the theory of stochastic processes. This enables us to find for the first time a way of developing maximum likelihood estimates (MLE) of the input information (e.g., afferent rate and variance) for an LIF neuron from a set of recorded spike trains. Dynamic inputs to pools of LIF neurons both with and without interactions are efficiently and reliably decoded by applying the MLE, even within time windows as short as 25 msec.
authors.affiliation: Mathematical Department, Zhejiang Normal University, Jinhua, PR China. xuejuanzhang@gmail.com
authors.forename: Xuejuan
authors.initials: X
authors.lastname: Zhang
authors.forename: Gongqiang
authors.initials: G
authors.lastname: You
authors.forename: Tianping
authors.initials: T
authors.lastname: Chen
authors.forename: Jianfeng
authors.initials: J
authors.lastname: Feng
mesh_terms: D000465:Algorithms; D004594:Electrophysiology; D005071:Evoked Potentials / Q000502:physiology; D019706:Excitatory Postsynaptic Potentials / Q000502:physiology; D016013:Likelihood Functions; D008959:Models, Neurological; D009010:Monte Carlo Method; D009474:Neurons / Q000502:physiology*; D011336:Probability