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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.

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Citations
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Journal ArticleDOI

Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks.

TL;DR: The generic nature of the hybrid scheme and its public implementation in hybridLFPy form the basis for LFP predictions from other and larger point-neuron network models, as well as extensions of the current application with additional biological detail.
Journal ArticleDOI

Modelling and Analysis of Electrical Potentials Recorded in Microelectrode Arrays (MEAs)

TL;DR: A biophysical forward-modelling formalism based on the finite element method (FEM) is used to establish quantitatively accurate links between neural activity in the slice and potentials recorded in the MEA set-up, and methods for estimation of current-source density (CSD) from MEA potentials are explored.
Journal ArticleDOI

Toward More Versatile and Intuitive Cortical Brain–Machine Interfaces

TL;DR: This review will focus on several new topics in the arena of cortical prosthetics using: recordings from cortical areas outside motor cortex; local field potentials as a source of recorded signals; somatosensory feedback for more dexterous control of robotics; and new decoding methods that work in concert to form an ecology of decode algorithms.
Journal ArticleDOI

Hippocampal Place Cells Couple to Three Different Gamma Oscillations during Place Field Traversal

TL;DR: It is shown that spike timing of place cells can tune to all three gamma oscillations, but phase coupling to the mid-frequency gamma oscillation conveyed from the entorhinal cortex was restricted to leaving a place field.
Journal ArticleDOI

Optimal Electrode Size for Multi-Scale Extracellular-Potential Recording From Neuronal Assemblies

TL;DR: It is demonstrated that the noise and signal attenuation depend more on the electrode impedance than on electrode size, per se, especially for electrodes <10 μm in width or diameter to achieve high-spatial-resolution readout.
References
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Journal ArticleDOI

A mathematical theory of communication

TL;DR: This final installment of the paper considers the case where the signals or the messages or both are continuously variable, in contrast with the discrete nature assumed until now.
MonographDOI

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.
Book ChapterDOI

Investigating causal relations by econometric models and cross-spectral methods

TL;DR: In this article, it is shown that the cross spectrum between two variables can be decomposed into two parts, each relating to a single causal arm of a feedback situation, and measures of causal lag and causal strength can then be constructed.
Journal ArticleDOI

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

D. D. Lee
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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