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Biomedical subjects

L A Baccalá

Publications and source records attributed to L A Baccalá.

4 recordsLinked to original sources
PubMed2001

Partial directed coherence: a new concept in neural structure determination.

This paper introduces a new frequency-domain approach to describe the relationships (direction of information flow) between multivariate time series based on the decomposition of multivariate partial coherences computed from multivariate autoregressive models. We discuss its application and compare its performance to other approaches to the problem of determining neural structure relations from the simultaneous measurement of neural electrophysiological signals. The new concept is shown to reflect a frequency-domain representation of the concept of Granger causality.

Cerebral Cortex↗
PubMed1999

Using partial directed coherence to describe neuronal ensemble interactions.

This paper illustrates the use of the recently introduced method of partial directed coherence in approaching how interactions among neural structures change over short time spans that characterize well defined behavioral states. Central to the method is its use of multivariate time series modelling in conjunction with the concept of Granger causality. Simulated neural network models were used to illustrate the technique's power and limitations when dealing with neural spiking data. This was followed by the analysis of multi-unit activity data illustrating dynamical change in the interaction of thalamo-cortical structures in a behaving rat.

Action Potentials↗
PubMed1999

Non-linear analysis of the rhythmic activity in rodent brains.

This paper discusses the employment of non-parametric non-linear prediction algorithms to investigate non-linear dynamics in the rhythmic brain activity of rats. Three algorithms (Sugihara-May Simplex, K-neighbour and Casdagli's) were tested yielding similar prediction results which--when subject to a suitable bootstrap based t-tests--revealed that the theta waves recorded in rat brains cannot have their intrinsic non-linearity dismissed at a significance of 0.05.

Algorithms↗