Literature DB >> 20094906

Local field potentials indicate network state and account for neuronal response variability.

Ryan C Kelly1, Matthew A Smith, Robert E Kass, Tai Sing Lee.   

Abstract

Multineuronal recordings have revealed that neurons in primary visual cortex (V1) exhibit coordinated fluctuations of spiking activity in the absence and in the presence of visual stimulation. From the perspective of understanding a single cell's spiking activity relative to a behavior or stimulus, these network fluctuations are typically considered to be noise. We show that these events are highly correlated with another commonly recorded signal, the local field potential (LFP), and are also likely related to global network state phenomena which have been observed in a number of neural systems. Moreover, we show that attributing a component of cell firing to these network fluctuations via explicit modeling of the LFP improves the recovery of cell properties. This suggests that the impact of network fluctuations may be estimated using the LFP, and that a portion of this network activity is unrelated to the stimulus and instead reflects ongoing cortical activity. Thus, the LFP acts as an easily accessible bridge between the network state and the spiking activity.

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Mesh:

Year:  2010        PMID: 20094906      PMCID: PMC3604740          DOI: 10.1007/s10827-009-0208-9

Source DB:  PubMed          Journal:  J Comput Neurosci        ISSN: 0929-5313            Impact factor:   1.621


  52 in total

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9.  Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex.

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  46 in total

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Review 4.  The mechanics of state-dependent neural correlations.

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5.  How advances in neural recording affect data analysis.

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Journal:  Nat Neurosci       Date:  2011-02       Impact factor: 24.884

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7.  Stimulus-dependent spiking relationships with the EEG.

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8.  Single-Cell Membrane Potential Fluctuations Evince Network Scale-Freeness and Quasicriticality.

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9.  Adjusted regularization of cortical covariance.

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Journal:  J Comput Neurosci       Date:  2018-09-06       Impact factor: 1.621

10.  Inferring synaptic inputs from spikes with a conductance-based neural encoding model.

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