Diffusion and innovation rates for multidimensional neuronal data with large spatial covariances
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Updated: Oct 11, 2026

14:27
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
O Françoist1, C Larota, T Hervé
1LMC/IMAG, Grenoble, France.
Abstract:
The diffusion model has been introduced as a statistical model for processing multidimensional neuronal data. This paper extends estimation procedures for the parameters of this model when spatial covariances are large. The new method is based upon linear regression techniques. It is applied to an optical recording of the auditory cortex of a guinea pig stimulated with pure tone bursts (frequency 14 kHz).

