Bursting as a source for predictability in biological neural network activity

DOI10.1016/S0167-2789(97)00131-0zbMath0925.92008WikidataQ61782925 ScholiaQ61782925MaRDI QIDQ1373939

L. Menendez de la Prida, J. V. Sanchez-Andres, Nico Stollenwerk

Publication date: 26 November 1997

Published in: Physica D (Search for Journal in Brave)

zbMATH Keywords

time seriesPoisson processesKolmogorov-Smirnov testGaussian-scaled random phase surrogatesPierson-Moss methodunstable periodicities

Mathematics Subject Classification ID

General biology and biomathematics (92B05)

Related Items (3)

A subharmonic dynamical bifurcation during in vitro epileptiform activity ⋮ Spatiotemporal Distributions of Unstable Periodic Orbits in Noisy Coupled Chaotic Systems ⋮ Two-compartment stochastic model of a neuron

Cites Work

• Testing for nonlinearity in time series: the method of surrogate data
• Dissection of a model for neuronal parabolic bursting
• Analysis of bursting in a thalamic neuron model
• Detecting nonlinearities in neuro-electrical signals: A study of synchronous local field potentials
• USING COARSE-GRAINED MEASURES TO CHARACTERIZE ELECTROMYOGRAPHIC SIGNALS
• NONLINEAR FORECASTING OF SPIKE TRAINS FROM SENSORY NEURONS

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