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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Posted ContentDOI
Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity
TL;DR: In this article , a multiscale subspace identification (multiscale SID) algorithm was proposed to enable computationally efficient modeling and dimensionality reduction for multimodal discrete-continuous spike field data.
Reliability of energy landscape analysis of resting-state functional MRI data
TL;DR: In this article , a permutation test was proposed to assess whether or not indices characterizing the energy landscape are more consistent across different sets of scanning sessions from the same participant (i.e., within-participant reliability) than across different sessions from different participants.
Posted ContentDOI
Local contribution to the somatosensory evoked potentials in rat’s thalamus
Władysław Średniawa,Zuzanna Borzymowska,Kacper Kondrakiewicz,P. Jurgielewicz,Bartosz Mindur,Pawel Hottowy,Daniel K. Wójcik,Ewa Kublik +7 more
TL;DR: In this article , the contribution of local and distant currents to LFP recorded from rat thalamic nuclei and barrel cortex activated by whisker stimulation was investigated. But, the results were limited to the case of cortical recordings in the somatosensory system in response to cortical responses to whisker deflection.
Journal Article
Identifying neural contributions to the local field potential through modeling: a review of Barbieri et al. (2014)
TL;DR: This is a journal-club style review of the paper "Stimulus dependence of local field potential spectra: experiment versus theory" by Barbieri et al. (2014).
Journal ArticleDOI
A Novel Biophysical Model for the Generation of Sharp Wave Ripples in CA1 Hippocampus
TL;DR: In this paper , a large-scale biophysically realistic neural network model of CA1 hippocampus with functionally organized circuit modules containing distinct types of neurons was developed, which provided insights into the role of neuronal types and their microcircuit motifs in generating Sharp-wave ripples in the CA1 region.
References
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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.
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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
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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