Journal ArticleDOI
Modelling and analysis of local field potentials for studying the function of cortical circuits
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TLDR
Careful mathematical modelling and analysis are needed to take full advantage of the opportunities that this signal offers in understanding signal processing in cortical circuits and, ultimately, the neural basis of perception and cognition.Abstract:
Local field potentials (LFPs) provide a wealth of information about synaptic processing in cortical populations but are difficult to interpret. Einevoll and colleagues consider the neural origin of cortical LFPs and discuss LFP modelling and analysis methods that can improve the interpretation of LFP data.read more
Citations
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Journal ArticleDOI
The neurogenesis of P1 and N1: A concurrent EEG/LFP study
Michael Bruyns-Haylett,Jingjing Luo,Aneurin J. Kennerley,Samuel Harris,Luke Boorman,Elizabeth Milne,Nicolas Vautrelle,Yurie Hayashi,Benjamin J. Whalley,Myles Jones,Jason Berwick,Jorge J. Riera,Ying Zheng +12 more
TL;DR: It is suggested that the temporal delay of inhibition with respect to excitation observed in intracellular recordings is also reflected in extracellular field potentials (FPs), resulting in a temporal window during which only excitatory post‐synaptic activity and leak channel activity are recorded in the ERP and evoked LFP time series.
Posted ContentDOI
Degeneracy in hippocampal physiology and plasticity
TL;DR: This review assesses the potential of degeneracy as a framework to achieve encoding and homeostasis without cross-interferences, and postulate that multiscale parametric and interactional complexity could establish disparate routes towards accomplishing these conjoint goals.
Journal ArticleDOI
Predominance of Movement Speed Over Direction in Neuronal Population Signals of Motor Cortex: Intracranial EEG Data and A Simple Explanatory Model
Jiří Hammer,Tobias Pistohl,Jörg Fischer,Pavel Krsek,Martin Tomášek,Petr Marusic,Andreas Schulze-Bonhage,Andreas Schulze-Bonhage,Ad Aertsen,Tonio Ball,Tonio Ball +10 more
TL;DR: It is shown that in iEEG, contrasting to what has been previously found on the single neuron level, speed predominates over velocity, a principle that may be helpful in the interpretation of neuronal population signals in general, including EEG and functional magnetic resonance imaging.
Journal ArticleDOI
Local recording of biological magnetic fields using Giant Magneto Resistance-based micro-probes
Francesca Barbieri,Vincent Trauchessec,Laure Caruso,Josué Trejo-Rosillo,Bartosz Telenczuk,E. Paul,Thierry Bal,Alain Destexhe,Claude Fermon,Myriam Pannetier-Lecoeur,Gilles Ouanounou +10 more
TL;DR: Bio-compatible sensors based on Giant Magneto-Resistance (GMR) spin electronics are presented and it is shown on a mouse muscle in vitro, using electrophysiology and computational modeling, that this technology permits simultaneous local recordings of the magnetic fields from action potentials.
Journal ArticleDOI
Synchronised spiking activity underlies phase amplitude coupling in the subthalamic nucleus of Parkinson's disease patients
Anders Christian Meidahl,Christian K.E. Moll,Bernadette C.M. van Wijk,Alessandro Gulberti,Gerd Tinkhauser,Manfred Westphal,Andreas K. Engel,Wolfgang Hamel,Peter Brown,Andrew Sharott +9 more
TL;DR: This work provides multiple lines of evidence that PAC in the human STN reflects the locking of spiking activity to network beta oscillations and that this coupling progressively increases with the duration of beta-bursts.
References
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A mathematical theory of communication
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Causality: models, reasoning, and inference
TL;DR: The art and science of cause and effect have been studied in the social sciences for a long time as mentioned in this paper, see, e.g., the theory of inferred causation, causal diagrams and the identification of causal effects.
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Investigating causal relations by econometric models and cross-spectral methods
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Learning the parts of objects by non-negative matrix factorization
TL;DR: An algorithm for non-negative matrix factorization is demonstrated that is able to learn parts of faces and semantic features of text and is in contrast to other methods that learn holistic, not parts-based, representations.
Learning parts of objects by non-negative matrix factorization
TL;DR: In this article, non-negative matrix factorization is used to learn parts of faces and semantic features of text, which is in contrast to principal components analysis and vector quantization that learn holistic, not parts-based, representations.
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