Low-Frequency Local Field Potentials and Spikes in Primary Visual Cortex Convey Independent Visual Information

By A. Belitski, Arthur Gretton, C. Magri, Yusuke Murayama, Marcelo A. Montemurro, Nikos K. Logothetis, Stefano Panzeri. Journal of Neuroscience, 2008

433 citations recorded by the source corpus.

Local field potentials (LFPs) reflect subthreshold integrative processes that complement spike train measures. However, little is yet known about the differences between how LFPs and spikes encode rich naturalistic sensory stimuli. We addressed this question by recording LFPs and spikes from the primary visual cortex of anesthetized macaques while presenting a color movie. We then determined how the power of LFPs and spikes at different frequencies represents the visual features in the movie. We found that the most informative LFP frequency ranges were 1–8 and 60–100 Hz. LFPs in the range of 12–40 Hz carried little information about the stimulus, and may primarily reflect neuromodulatory inputs. Spike power was informative only at frequencies <12 Hz. We further quantified “signal correlations” (correlations in the trial-averaged power response to different stimuli) and “noise correlations” (trial-by-trial correlations in the fluctuations around the average) of LFPs and spikes recorded from the same electrode. We found positive signal correlation between high-gamma LFPs (60–100 Hz) and spikes, as well as strong positive signal correlation within high-gamma LFPs, suggesting that high-gamma LFPs and spikes are generated within the same network. LFPs <24 Hz shared strong positive noise correlations, indicating that they are influenced by a common source, such as a diffuse neuromodulatory input. LFPs <40 Hz showed very little signal and noise correlations with LFPs >40 Hz and with spikes, suggesting that low-frequency LFPs reflect neural processes that in natural conditions are fully decoupled from those giving rise to spikes and to high-gamma LFPs.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/3d5cbceb3258ea33641f99390833c2b1d54a2013

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Citations in the corpus

The benefits of noise in neural systems: bridging theory and experiment (2011, methodology): “Experimental verification would require placing extracellular recording electrodes (polytrodes) in LGN and V1 areas of monkeys while they are immobilized but awak”
The temporal structures and functional significance of scale-free brain activity (2010, neutral): “Linkage to Previous Work This study follows from a recent line of research emphasizing the physiological significance of broadband LFP, EEG, and ECoG activity (Belitski et al., 2008; Bressler et al., 1993; Henrie and Shapley, 2005; Manning et al., 2009; Miller et al., 2009a, 2009b; Montemurro et al.”
Power-Law Scaling in the Brain Surface Electric Potential (2009, background): “Activity-related narrow-band PSD increases, correlating with fMRI [55], have been demonstrated in the ‘‘high-c’’ (40–100Hz) frequency range of the LFP [56–60] and the MEG [61,62].”
Neural correlates of high-gamma oscillations (60–200 Hz) in macaque local field potentials and their potential implications in electrocorticography (2008, result): “Our results are consistent with a growing number of LFP studies suggesting a link between high-gamma oscillations and spiking activity (Liu and Newsome, 2006; Belitski et al., 2008).”
Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models (2015, background): “The recording of LFPs has a prominent role in systems neuroscience, and such recordings have been used extensively to investigate cortical network mechanisms involved in sensory processing [26], motor planning [27], and higher cognitive processes [28].”
Broadband shifts in LFP power spectra are correlated with single-neuron spiking in humans (2009, background): “A third example can be found in Belitski et al.(2008), which examined LFP power spectra from macaque visual cortex during movie watching.”
The Science of Neural Interface Systems (2009, neutral): “Studies have begun to investigate how information in the field potential compares with that in spikes (Belitski et al. 2008, Buneo et al. 2003, Donoghue et al. 1998, Murthy & Fetz 1996, Pesaran et al. 2002).”
Deep Networks from the Principle of Rate Reduction (2020, background): “…indicates such a convolutional network is computationally more efficient to learn and construct in the spectral (Fourier) domain, analogous to how neurons in the visual cortex encode and transit information with their spiking frequencies (Eliasmith & Anderson, 2003; Belitski et al., 2008).”
Encoding of Naturalistic Stimuli by Local Field Potential Spectra in Networks of Excitatory and Inhibitory Neurons (2008, methodology): “We aimed at simulating responses of visual cortex dynamics during the viewing of naturalistic movie stimuli, for which detailed neurophysiological data of LFP cortical responses are available [14,15].”
Spike-phase coding boosts and stabilizes information carried by spatial and temporal spike patterns. (2009, positive): “This was achieved by applying the information theoretic formalism to responses r quantified just as the binned LFP variable of interest in each stimulus window, without any consideration to the associated spiking response; see (Belitski et al., 2008) for a related analysis in the visual cortex.”
Revealing neuronal function through microelectrode array recordings (2015, neutral): “The LFP is assessed by the signal content in the low-frequency band of the recorded signal ( < 300 Hz) (Belitski et al., 2008; Buzsáki et al., 2012), while EAPs are analyzed after ﬁltering the LFP out (300–3000 Hz) (Quian Quiroga, 2009).”
Different Origins of Gamma Rhythm and High-Gamma Activity in Macaque Visual Cortex (2011, background): “In addition, recent studies have revealed that low-frequency (,10 Hz) phase is also a significant predictor of the multiunit activity [7,17,19,31,32].”
Extracting information from neuronal populations: information theory and decoding approaches (2009, background): “For example, in the primary visual cortex of anaesthetized macaque”
Primary Visual Cortex Represents the Difference Between Past and Present (2013, background): “We speculate that joint processing at various stimulus temporal frequencies is required to produce a coherent interpretation of a visual scene (Jonides et al. 1982; Rucci et al. 2007; Belitski et al. 2008; Rucci 2008; Nikolić et al. 2009; Jurjut et al. 2011; Onat, Nortmann et al. 2011; Eriksson et a”
Neural basis of global resting-state fMRI activity (2010, result): “In addition, the low and high LFP frequency bands showing positive correlation with the spontaneous fMRI signal in the present study are similar to those previously observed to carry mutually exclusive information about a complex visual stimulus in the primary visual cortex, suggesting that they ref”