title: Phase analysis method for burst onset prediction.
abstract: The response of bursting neurons to fluctuating inputs is usually hard to predict, due to their strong nonlinearity. For the same reason, decoding the injected stimulus from the activity of a bursting neuron is generally difficult. In this paper we propose a method describing (for neuron models) a mechanism of phase coding relating the burst onsets with the phase profile of the input current. This relation suggests that burst onset may provide a way for postsynaptic neurons to track the input phase. Moreover, we define a method of phase decoding to solve the inverse problem and estimate the likelihood of burst onset given the input state. Both methods are presented here in a unified framework, describing a complete coding-decoding procedure. This procedure is tested by using different neuron models, stimulated with different inputs (stochastic, sinusoidal, up, and down states). The results obtained show the efficacy and broad range of application of the proposed methods. Possible applications range from the study of sensory information processing, in which phase-of-firing codes are known to play a crucial role, to clinical applications such as deep brain stimulation, helping to design stimuli in order to trigger or prevent neural bursting.
authors.affiliation: DITEN, University of Genoa, Via Opera Pia 11a, 16145 Genova, Italy.
authors.forename: Flavio
authors.initials: F
authors.lastname: Stellino
authors.affiliation: The BioRobotics Institute, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, 56025 Pontedera, Italy.
authors.forename: Alberto
authors.initials: A
authors.lastname: Mazzoni
authors.affiliation: DITEN, University of Genoa, Via Opera Pia 11a, 16145 Genova, Italy.
authors.forename: Marco
authors.initials: M
authors.lastname: Storace
mesh_terms: D000200:Action Potentials; D000818:Animals; D003198:Computer Simulation; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes