A Distinct Class of Bursting Neurons with Strong Gamma Synchronization and Stimulus Selectivity in Monkey V1

Onorato, Irene; Neuenschwander, Sergio; Hoy, Jennifer L.; Lima, Bruss; Rocha, Katia-Simone; Broggini, Ana Clara; Uran, Cem; Spyropoulos, Georgios; Klon-Lipok, Johanna; Womelsdorf, Thilo; Fries, Pascal; Niell, Cristopher M.; Singer, Wolf; Vinck, Martin

doi:10.1016/j.neuron.2019.09.039

Cited by 56 publications

(100 citation statements)

References 142 publications

(335 reference statements)

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“…We analyzed data from a total of 6 adult macaque monkeys ( macaca mulatta ), referred to as monkey H, I, J, L, P and T. Monkeys I and L are/were female, the others male. The experiments were approved by the responsible regional or local authority, which was the Regierungspräsidium Darmstadt, Germany, for monkeys H, I, J, L and T, and the ethics committee of the Radboud University, Nijmegen, Netherlands, for monkey P. Parts of the data have been used in other publications (Lima et al, 2010; Onorato et al, 2019; Vinck et al, 2010a; Womelsdorf et al, 2012).…”

Section: Star Methodsmentioning

confidence: 99%

“…Single units were isolated through semi-automated spike sorting (Onorato et al, 2019). First, we performed semi-automatic clustering with the KlustaKwik 3.0 software.…”

Section: Methods Detailsmentioning

confidence: 99%

“…Note that for the analysis of the relationship between individual gamma cycles and spiking activity, we used a band-pass filter (3 rd order, two-pass Butterworth, with a pass-band of 40-90 Hz for monkey J and 25-55 Hz for monkey L). In this case, we used an additional criterion to reject epochs of spurious oscillatory activity (Onorato et al, 2019): We ran the same cycle-selection procedure on the pre-stimulus period, in which narrow-band gamma-band oscillations are virtually absent. For the pre-stimulus period, we obtained the mean μ pre and standard deviation σ pre of the distribution of amplitudes.…”

Section: Methods Detailsmentioning

confidence: 99%

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Spontaneous variability in gamma dynamics described by a linear harmonic oscillator driven by noise

Spyropoulos

Dowdall

Schölvinck

et al. 2019

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SUMMARYCircuits of excitatory and inhibitory neurons can generate rhythmic activity in the gamma frequency-range (30-80Hz). Individual gamma-cycles show spontaneous variability in amplitude and duration. The mechanisms underlying this variability are not fully understood. We recorded local-field-potentials (LFPs) and spikes from awake macaque V1, and developed a noise-robust method to detect gamma-cycle amplitudes and durations. Amplitudes and durations showed a weak but positive correlation. This correlation, and the joint amplitude-duration distribution, is well reproduced by a dampened harmonic oscillator driven by stochastic noise. We show that this model accurately fits LFP power spectra and is equivalent to a linear PING (Pyramidal Interneuron Network Gamma) circuit. The model recapitulates two additional features of V1 gamma: (1) Amplitude-duration correlations decrease with oscillation strength; (2) Amplitudes and durations exhibit strong and weak autocorrelations, respectively, depending on oscillation strength. Finally, longer gamma-cycles are associated with stronger spike-synchrony, but lower spike-rates in both (putative) excitatory and inhibitory neurons. In sum, V1 gamma-dynamics are well described by the simplest possible model of gamma: A linear harmonic oscillator driven by noise.

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Section: Star Methodsmentioning

confidence: 99%

“…Single units were isolated through semi-automated spike sorting (Onorato et al, 2019). First, we performed semi-automatic clustering with the KlustaKwik 3.0 software.…”

Section: Methods Detailsmentioning

confidence: 99%

Section: Methods Detailsmentioning

confidence: 99%

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Spontaneous variability in gamma dynamics described by a linear harmonic oscillator driven by noise

Spyropoulos

Dowdall

Schölvinck

et al. 2019

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“…Importantly, in vivo recordings throughout the brain reveal that groups of neurons with NS or BS EAPs play functionally distinct roles in behavior (Anastassiou et al, 2015;Ison et al, 2011;Mitchell et al, 2007;Oemisch et al, 2015;Peyrache et al, 2012;Peyrache and Destexhe, 2019;Rutishauser et al, 2015;Takahashi et al, 2015;Trainito et al, 2019;Viskontas et al, 2007). Nevertheless, the assumption that all NS cells are inhibitory and all BS cells are excitatory is not always true: some excitatory cells elicit narrow spikes and some inhibitory cells elicit wider spikes (Freund and Buzsá ki, 1996;Gray and Mc-Cormick, 1996;Onorato et al, 2020;Vigneswaran et al, 2011Gouwens et al, 2019. Apart from the procedures outlined above (which are often impractical or impossible to perform in human recordings), we lack a method for relating EAPs to the spectrum of cell types identified and predicted by genetics, morphology, and electrophysiology in humans and in monkeys.…”

Section: Introductionmentioning

confidence: 99%

Cellular Classes in the Human Brain Revealed In Vivo by Heartbeat-Related Modulation of the Extracellular Action Potential Waveform

et al. 2020

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“…The experiments reported here were limited to visual cortex. Activated visual cortex shows prominent gamma-band activity (Brunet et al, 2015;Onorato et al, 2020), and the underlying mechanisms are likely related to the ones underlying the dominant role of gamma in causing spikes. If this reasoning is correct, it predicts a similar causal role of gamma in other areas, in which clear and narrowband gamma has been described (Brown et al, 1998;Csicsvari et al, 2003;Fries, 2009;Gregoriou et al, 2009;Pesaran et al, 2002).…”

Section: Discussionmentioning

confidence: 99%

Gamma-rhythmic input causes spike output

Lewis

Wunderle

et al. 2020

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1 Lewis, Ni et al., Gamma-rhythmic input causes spike output SummaryThe gamma rhythm has been implicated in neuronal communication, but causal evidence remains indirect. We measured spike output of local neuronal networks and emulated their synaptic input through optogenetics. Opsins provide currents through somato-dendritic membranes, similar to synapses, yet under experimental control with high temporal precision. We expressed Channelrhodopsin-2 in excitatory neurons of cat visual cortex and recorded neuronal responses to light with different temporal characteristics. Sine waves of different frequencies entrained neuronal responses with a reliability that peaked for input frequencies in the gamma band. Crucially, we also presented white-noise sequences, because their temporal unpredictability enables analysis of causality. Neuronal spike output was caused specifically by the input's gamma component. This gamma-specific transfer function is likely an emergent property of in-vivo networks with feedback inhibition. The method described here could reveal the transfer function between the input to any one and the output of any other neuronal group. IntroductionAt the heart of the brain's processing abilities is the communication among neuronal groups. And at the heart of the brain's cognitive functions is the flexible modulation of this neuronal communication. Numerous studies suggest that neuronal communication is affected by neuronal synchronization. For example, computational models indicate that synaptic inputs to a neuron have a higher impact when they are synchronized (Salinas and Sejnowski, 2000); intracellular neuronal recordings demonstrate reduced spike thresholds for synchronized inputs (Azouz and Gray, 2003); and recordings from awake macaque monkey reveal that neurons transmitting attended stimulus information show enhanced gamma-band synchronization (Fries et al., 2008) that in turn speeds behavioral responses (Womelsdorf et al., 2006). The communication between two neuronal groups is also affected by the synchronization between them, a concept termed Communication-through-Coherence or CTC (Fries, 2005(Fries, , 2015. Computational models suggest that gamma-rhythmic inputs can entrain a postsynaptic neuronal target group and thereby enhance their impact, while at the same time reducing the impact of competing inputs (Börgers and Kopell, 2008); recordings from cats and macaques show that phase relations between neuronal groups affect their power-power covariation (Womelsdorf et al., 2007) and their transfer entropy (Besserve et al., 2015); and recordings from macaques performing selective attention tasks demonstrate that interareal gamma-band synchronization is enhanced along anatomical projections transmitting attended stimulus information (Bosman et al., 2012;Grothe et al., 2012), and that this synchronization actually improves behavioral performance (Rohenkohl et al., 2018).Thus, recordings of neuronal activity during visual stimulation and task performance have provided strong correlative evidence fo...

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A Distinct Class of Bursting Neurons with Strong Gamma Synchronization and Stimulus Selectivity in Monkey V1

Cited by 56 publications

References 142 publications

Spontaneous variability in gamma dynamics described by a linear harmonic oscillator driven by noise

Spontaneous variability in gamma dynamics described by a linear harmonic oscillator driven by noise

Cellular Classes in the Human Brain Revealed In Vivo by Heartbeat-Related Modulation of the Extracellular Action Potential Waveform

Gamma-rhythmic input causes spike output

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