While moving through natural environments, organisms rarely encounter random and temporally independent visual inputs. Instead, they see environment-specific stimuli and stimulus categories repeatedly. A specific environment comes with its own distribution of probable edge orientations, object categories, and visual image statistics in general (Torralba and Oliva, 2003), and as organisms spend extended periods in the same environment, their visual input is likely to be autocorrelated (Dong and Atick, 1995) and self-repeating (Wilming et al., 2013).

This input repetition presents an opportunity: If an organism manages to tune its input processing to the input it is presented with within short timescales, it will be able to process probable future inputs optimally. Several theories have been formulated on algorithms the visual system might use to achieve such tuning to the input statistics in the long run (Olshausen and Field, 1996; Rao and Ballard, 1999), but the specific implementations, as well as changes in input processing over short to medium timescales, are still a matter of active inquiry.

Stimulus repetition has been shown to lead to a reduction of firing rates in stimulus-driven neurons (Desimone, 1996; Li et al., 1993) and a decreased hemodynamic response (Grill-Spector et al., 2006; Stern et al., 1996) in visual areas, a phenomenon generally called repetition suppression. Importantly, this decrease of neuronal activity does not lead to decreases in detection performance. Instead, detection performance generally stays stable or even improves over stimulus repetitions (Fiorentini and Berardi, 1980; Grill-Spector et al., 2006).

But how does the brain keep or improve behavioral performance with less neuronal activity? Potentially, repetition suppression might specifically target neurons coding for redundant, already predicted, information (Auksztulewicz and Friston, 2016). Alternatively, behavior might rely primarily on the neurons most responsive to the repeated input, which might be exempted from repetition suppression (Desimone, 1996; Homann et al., 2017) or might even undergo repetition enhancement (Lim et al., 2015). Consistent with the latter, a further possibility is that the remaining, non-suppressed neurons fire more synchronously, effectively compensating for decreased firing rates via increased temporal overlap between action potentials (Gotts et al., 2012).

In area V1, such an increase in synchronous neuronal firing and oscillatory power in the gamma band has been reported (Brunet et al., 2014). Specifically, gamma-band power in the local field potential increased with the logarithm of the number of repetitions, accompanied by increased V1–V4 coherence and gamma spike-field locking in V4.

However, several questions remain open: (1) Are the changes in the neuronal circuits that underlie the observed gamma-power increase specific to the stimulus that induced them, or do they equally affect the processing of other stimuli? (2) Do gamma enhancements persist over a time frame of several minutes and the intervening presentation of other stimuli, or do they vanish quickly? (3) Does repetition-related gamma enhancement exist in humans and for untrained, novel stimuli?

In this study, we recorded source-localized (Gross et al., 2001; Van Veen et al., 1997) MEG in 30 participants while they were presented with a continuous sequence of repeated oriented gratings. We found that the repetition-related gamma enhancement effect is clearly present in humans, is stimulus specific, and persists over time and deadaptation.

Stimuli as well as trial and session structure are illustrated in Figure 1. In short, subjects initiated each trial by fixating a central fixation spot. After a baseline (1 s), a central static grating with one of four possible orientations (22.5°, 67.5°, 112.5°, or 157.5° from the horizontal) was shown. After a period of 0.3–2 s, the grating changed orientation by up to 0.9 degrees, while decreasing in contrast. Subjects were required to report the direction of the orientation change. Each grating orientation was repeated for 120 trials in a blocked fashion (blocks A–D). After those blocks, the oriented grating of the first block was repeated again for another 120 trials (block A2). Except for the change in grating orientation, there was no change or break between the blocks.

Figure 1

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To investigate how behavioral and neuronal responses (‘responses of interest’, e.g., gamma power, event-related field amplitude/ERFs) were affected by stimulus repetition, while controlling for other factors, we fitted separate random intercept linear regression models to each response of interest over all subjects. Each used the same independent variables (stimulus-specific repetition number, general trial number, microsaccade rate, and further covariates, see Materials and methods). As several of these responses showed different trajectories over repetitions 1–10 versus over all repetitions (e.g., an early decrease and an overall increase), we fitted two overlapping predictors for repetitions 1–10 and repetitions 1–120.

To analyze how early and late changes in different behavioral and neuronal responses correlated with each other, we fitted per-subject linear regression models to the responses of interest (separately for repetitions 1–10 and 11–120, as overlapping trajectories cannot be disentangled on a per-subject basis), using the same independent variables as above. Subsequently, the per-subject repetition-number coefficients were correlated between the behavioral and neuronal responses of interest.

Stimuli induce gamma responses in visual areas

As expected, grating stimuli produced robust responses in visual areas: Dipoles in V1/V2 showed a clear visual ERF and a stimulus-driven gamma-band response (Figure 2a–d, Figure 2—figure supplement 1c,d). The gamma-band response was strongest in areas V1 and V2 and extended into temporal and parietal lobes (Figure 2e). Furthermore, a stimulus-driven decrease in source-localized alpha and beta power could be seen in temporal/parietal and parietal/frontal areas, respectively (Figure 2—figure supplement 1c). Frequency bands were determined (Haller et al., 2018) based on subject-individual spectra of stimulus-induced power changes, if possible (see Materials and methods for details).

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Stimulus repetition induces early decreases and later increases in gamma power that are both stimulus specific

The strength of the gamma-band response (measured as gamma power during stimulation/gamma power during trial-mean baseline) changed across repeated presentations of the same stimulus (Figure 3). Across stimulus repetitions, gamma showed a biphasic pattern that could be well described by a linear decrease until a breakpoint at the 10th repetition, followed by a linear increase (see Materials and methods). Across the first 10 stimulus repetitions after a stimulus-block start, gamma dropped by 30.8 pp (percentage points) (${\displaystyle C{I}_{95\mathrm{\%}}=\left[22.4\mathrm{p}\mathrm{p}38.75\mathrm{p}\mathrm{p}\right]}$, ${\displaystyle p<2\ast {10}^{-14}}$); we will refer to this as the early gamma-power decrease. In addition, over all repetitions, gamma continually increased with repetitions by about 0.40 pp/repetition of a specific stimulus (${\displaystyle C{I}_{95\mathrm{\%}}=\left[0.33\mathrm{p}\mathrm{p}0.46\mathrm{p}\mathrm{p}\right]}$, ${\displaystyle p<2\ast {10}^{-16}}$), which corresponds to an average increase of 48 pp over the 120 presented repetitions; we will refer to this as the gamma-power increase. We also fitted the pattern of gamma over all repetitions with the sum of an exponential decay and a linear increase and found the early decrease to have a time constant of 3.5 repetitions (${\displaystyle C{I}_{95\mathrm{\%}}=\left[2.34.6\right]}$). When the visual stimulus was switched at the beginning of a new block, this pattern of early decrease and subsequent increase repeated. This demonstrates that these repetition-related gamma increases were specific to the repeated stimulus because block boundaries were only constituted by switches in stimulus orientation.

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In addition, we observed a stimulus-unspecific effect of trial number: The strength of the gamma-band response increased with total trial number by about 0.07 pp/total trial number (${\displaystyle C{I}_{95\mathrm{\%}}=\left[0.06\mathrm{p}\mathrm{p}0.09\mathrm{p}\mathrm{p}\right]}$, ${\displaystyle p<2\ast {10}^{-16}}$), which corresponds to an average increase of 44 pp over the total 600 trials of the experiment.

Furthermore, the gamma-power enhancement across stimulus repetitions partially persisted over more than 25 min of intervening presentation of other orientations: Induced gamma power during block A2 was on average a further 7.80 pp above the level predicted by all other factors (including total trial number, ${\displaystyle C{I}_{95\mathrm{\%}}=\left[0.90\mathrm{p}\mathrm{p}14.87\mathrm{p}\mathrm{p}\right]}$, $p=0.024$, Figure 3c).

Both the early decrease in gamma power and the gamma-power increase source-localized to visual cortical areas and were strongest in V1, V2, V3, and V4 (Figure 3e,f; for sensor-level analysis, see Figure 2—figure supplement 1c and Figure 3—figure supplement 1e,f). The repetition-related increase was specific to the gamma band (Figure 3d,g). Furthermore, it was specific to the trial epoch with visually induced gamma: Power in the gamma-band during the pre-stimulus baseline did not show an association with stimulus repetition number ($p=0.36$, Figure 3—figure supplement 1a).

We controlled for changes in the rate of microsaccades (MSs). The MS rate had been included as a covariate in the gamma-power regression, which revealed that a higher MS rate was not significantly associated with stronger gamma power ($p=0.17$). Furthermore, MS rate did not change with stimulus repetition number ($p=0.66$) and slightly decreased with total trial number by 0.0004 sac/s/trial (${\displaystyle C{I}_{95\mathrm{\%}}=\left[-0.0005-0.0002\right]}$, ${\displaystyle p<2\ast {10}^{-3}}$, Figure 3—figure supplement 1b). These observations together show that the gamma-power increase could not have been driven by changes in MS rate.

The per-subject magnitude of changes in gamma power over both the first 10 stimulus repetitions and over later stimulus repetitions was related neither to the per-subject magnitude of changes in accuracy over early or late stimulus repetitions nor to the per-subject magnitude of changes in reaction time over early or late stimulus repetitions (all ${\displaystyle p>0.22}$).

Pupil constriction shows early decrease and then stabilizes

The switch of stimuli between blocks might have induced a change in arousal and pupil size. With the stimuli used here, stimulus presentations led to reliable pupil constrictions, as induced by the pupillary light reflex. Pupil constriction (the difference between pupil size before stimulus onset and 0.5 s–1.2 s after stimulus onset, Figure 4b, Figure 4—figure supplement 1a) decreased over the first 10 repetitions (${\displaystyle p<3\ast {10}^{-10}}$), but was not influenced by further stimulus repetitions ($p=0.68$, Figure 4b,c) nor total trial number (${\displaystyle p=0.64}$). As for gamma, we also fitted the pattern of pupil constriction over all repetitions with the sum of an exponential decay and a linear increase and found the early decrease to have a time constant of 5.6 repetitions (${\displaystyle C{I}_{95\mathrm{\%}}=\left[2.68.7\right]}$). The per-subject changes in pupil constriction (averaged over blocks) were correlated to the per-subject changes in induced gamma power (averaged over blocks) with stimulus repetition over the first 10 repetitions of each stimulus, i.e. during the early gamma-power decrease ($r_{Spearman}=0.45,p=0.013$), but not over all repetitions, that is during the gamma-power increase ($p=0.20$). Thus, during the first 10 repetitions, across subjects, larger reductions in pupil constriction were accompanied by larger reductions in gamma.

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The changes in pupil constriction over repetitions were not driven by changes in pre-stimulus pupil size: Pre-stimulus pupil size did not change over the first 10 stimulus repetitions (${\displaystyle p=0.15}$) nor with further stimulus repetitions ($p=0.59$, Figure 3—figure supplement 1c).

Granger causality in the gamma band increases with stimulus repetition, especially for feedforward connections

Previous studies in macaques and humans found that Granger causality (GC) between cortical areas in the gamma band is stronger in the anatomically defined feedforward direction, whereas GC in the alpha-beta band is stronger in the feedback direction (Bastos et al., 2015; Michalareas et al., 2016). We repeated the core analysis of Michalareas et al., 2016 for the present dataset and found a similar pattern of results (Figure 5a,b, Figure 5—figure supplement 1).

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The above-described effects of stimulus repetition on gamma might be accompanied by corresponding changes in Granger causality. The analysis of MEG source-level GC requires a sufficient number of data points. Therefore, we compared GC computed over trials 11–50 (i.e., excluding the first ten trials showing the early gamma-power decrease) with GC computed over trials 81–120. Figure 5a,b shows two example interareal GC spectra with repetition-related increases in feedforward gamma GC. Across all area pairs, significant GC changes with stimulus repetition were strongly clustered in the gamma band, while no significant changes in the alpha-beta band were found (Figure 5c). Correspondingly, we focused the following analyses on the gamma band.

The estimation of the GC metric can be affected by the signal-to-noise ratios (SNRs) of the respective sources. One conservative test of GC directionality time-reverses the involved signals, which leaves the SNRs unchanged, but reverses temporal relations (Haufe et al., 2012; Vinck et al., 2015). Therefore, GC directionality that switches upon time reversal is most likely not due to SNR differences. In the following, we report only repetition-related effects that were both significant before time reversal and significant, with opposite directionality, after time reversal (Vinck et al., 2015).

With stimulus repetition, across all between-area pairs, feedforward gamma GC increased from V1 to V2/V3/V4, and from V3/V3AB/V4 to several areas further up the dorsal and ventral streams (Figure 5d,e). Feedback GC onto areas V1–V4 also increased. Across all significant repetition-related GC changes, feedforward connections increased more strongly than feedback ones (Figure 5f, ${\displaystyle p<0.001}$). We considered whether the observed changes in gamma GC were purely driven by changes in gamma power. The respective gamma-power changes (calculated similarly to the GC changes) are shown as a colored vertical bar to the right of Figure 5e. As can be seen, gamma-power changes tended to decrease with hierarchical level. By contrast, gamma-GC changes were strongest for GC that originated from intermediate levels and was directed to high levels. Furthermore, gamma-GC changes remained significantly above zero when we regressed out gamma-power changes in both areas of the area pairs (${\displaystyle C{I}_{95\mathrm{\%}}=\left[\mathrm{0.00050.0010}\right]}$, ${\displaystyle p<2\ast {10}^{-6}}$). This demonstrates that the changes in gamma GC were not purely driven by changes in the signal-to-noise ratio of the gamma band.

In summary, repeated presentations of a visual stimulus induced gamma-band activity in early and intermediate visual areas that decreased over the initial 10 repetitions and subsequently increased over further repetitions. Crucially, when stimuli were switched, this pattern repeated. This strongly suggests that the changes in the neuronal circuits that underlie the observed gamma-power increase are specific to the repeated stimulus and do not equally affect the processing of other stimuli. Gamma peak frequency increased over repetitions and did not show distinct changes for the first few repetitions. The stimulus-specific increases in gamma power and frequency with repetitions showed a stimulus-specific memory effect, in the sense that some enhancement persisted over 25 minutes of stimulation with different stimuli. This suggests that the repetition-driven network changes are at least partially persistent. Furthermore, gamma-band Granger causality increased with stimulus repetitions, especially from early visual areas in the anatomically defined feedforward direction. In addition, the magnitude of early ERF components decreased linearly with stimulus repetitions.

The repetition-related changes occurred over two different timescales, potentially indicative of two distinct but co-occurring processes. Over the first 10 stimulus repetitions, gamma power and pupil constriction decreased, and the slopes of their decreases were correlated across subjects. Over the remaining repetitions, gamma power increased continuously, while pupil constriction remained at the lower level. This pattern of changes is consistent with the superposition of an exponential decay, seen in gamma power and pupil constriction, with a slow and steady increase, seen in gamma power. In support of this scenario, two other parameters of neuronal activity showed such changes over all repetitions of a given stimulus: Gamma peak frequency steadily increased, and the magnitude of an early ERF component steadily decreased.

By extending research on gamma repetition enhancement from non-human primate local field potential recordings to human source-localized MEG, we could show remarkable similarities between gamma-band activities and their repetition-related changes, measurable in both species and recording techniques – see the companion paper (Peter et al., 2020). Notably, the existing studies with animals are limited to two to four subjects and thereby to an inference on those samples, whereas the present MEG study recorded from 30 subjects and thereby allowed an inference on the population.

Analyzing MEG recordings in source space suffers from uncertainties in spatial localization. Nevertheless, careful head stabilization and exclusion of participants with excessive head movements, as implemented in this study, enables a spatial resolution between 0.45 mm and 7 mm (Nasiotis et al., 2017). When analyzing Granger causality, it is important to stress that GC does not necessarily imply the existence of true neuronal interactions between time series, but merely implies predictability of one time series by another (Kispersky et al., 2011). Additionally, common noise and field spread in signals analyzed using GC can lead to spurious inferred connectivity, which can, however, be mostly alleviated using time-reversal testing, as used in this study (Haufe et al., 2012; Vinck et al., 2015).

Potential mechanism of late gamma increase as local circuit learning

Oscillatory neuronal activity can interact with Hebbian spike-timing-dependent plasticity (STDP). This can, for example, lead to changes in synaptic weights between excitatory neurons (E-E) that enhance their temporal synchronization and establish excitatory cell assemblies (Arthur et al., 2005; Cassenaer and Laurent, 2007; Suri and Sejnowski, 2002) as well as shorten oscillatory cycles (Börgers, 2017). However, changes in E-E synaptic weights would not explain the observed decreases in firing rates and ERFs and increases in inhibitory gamma locking (reported here; Brunet et al., 2014; Peter et al., 2020). We would like to speculate on a possible neuronal mechanism consistent with these findings as well as the reported increases in gamma power, frequency, and interareal gamma coherence (Figure 6).

Figure 6

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When visual stimulation induces gamma-band activity in awake primate V1 (Brunet et al., 2015; Jia et al., 2011; Kreiter and Singer, 1992; Uran et al., 2020), the resulting gamma cycles contain systematic sequences: The better a neuron is driven by a given stimulus, the earlier it spikes in the gamma cycle (Fries et al., 2007; Havenith et al., 2011; König et al., 1995; Vinck et al., 2010). This is likely due to the fact that the gamma cycle contains a characteristic sequence of excitation and inhibition (Atallah and Scanziani, 2009; Csicsvari et al., 2003; Hasenstaub et al., 2005; Vinck et al., 2013). Excitation triggers inhibition, and when inhibition decays, the most driven neurons are the first to overcome inhibition and spike. Their spiking leads to a new rise in inhibition, and only sufficiently driven neurons spike before the rising inhibition prevents the least driven neurons from spiking at all (de Almeida et al., 2009). Thus, on average, the most driven excitatory neurons (E_strong) spike first, followed by spiking of local inhibitory neurons (I_local), while less driven excitatory neurons (E_weak) spike during and after the inhibitory neurons, if at all. This sets up an E_strong-I_local-E_weak spiking sequence.

If two neurons spike for some time with a systematic temporal relationship, this can lead to changes in their mutual synaptic inputs, a phenomenon referred to as spike timing-dependent plasticity (STDP) (Caporale and Dan, 2008; Hennequin et al., 2017). The precise pattern of synaptic strengthening and weakening as a function of the relative spike timing varies across neuron types and brain areas (Hennequin et al., 2017). One well-established pattern is referred to as Hebbian STDP: Synapses from the leading neuron spiking few milliseconds before the lagging neuron are strengthened, whereas synapses in the other direction are weakened. This pattern has, e.g., been described for synapses of excitatory neurons onto inhibitory neurons in rat visual cortex (Huang et al., 2013). This Hebbian STDP, together with the abovementioned E_strong-I_local-E_weak sequence during gamma cycles, would lead to a strengthening of the synapses from E_strong to I_local, and to a weakening of the synapses from E_weak to I_local. Note that the timescales of spike sequences in the gamma cycle and of spike relationships leading to STDP are in reasonably good agreement. For synapses of inhibitory neurons onto excitatory neurons, the described STDP patterns are overall more diverse. Yet, a Hebbian-type I-to-E STDP has been found in rat entorhinal cortex (Haas et al., 2006). Together with the gamma-related E_strong-I_local-E_weak sequence, this could lead to strengthening of synapses from I_local to E_weak and weakening of synapses from I_local to E_strong.

Through this interplay between the gamma cycle and STDP, the activation of E_strong neurons during the repeated presentation of a given stimulus would increase the impact of E_strong onto I_local neurons. E_strong spiking would trigger I_local spiking with more efficiency and shorter latency, leading to stronger and earlier I_local spiking, and thereby more gamma-locked I_local spiking. This could explain the observed shorter gamma cycles (i.e., higher gamma frequency) and overall stronger gamma power measured here and in macaques (Brunet et al., 2014; Peter et al., 2020), and the increasing gamma locking of inhibitory neurons (Brunet et al., 2014). At the same time, these stronger and more synchronized bouts of I_local spiking would enhance the impact of I_local neurons onto E_weak neurons. Additionally, the inhibition of E_weak neurons would be further enhanced by the strengthened I_local-to-E_weak synapses. The strong bouts of I_local spiking might in principle also enhance the I_local feedback inhibition onto E_strong neurons. However, this effect might be balanced by the weakened I_local-to-E_strong synapses. In sum, this could lead to maintained firing of E_strong together with reduced firing of E_weak neurons, and thereby explain overall reduced firing rates and implement a winner-take-all mechanism that sharpens the population firing rate representation (de Almeida et al., 2009; Homann et al., 2017; Lim et al., 2015).

Beyond these local effects, the overall increase in gamma strength and the stronger focusing of E_strong spiking during the early gamma cycle would likely enhance the impact of the local E_strong neurons onto their postsynaptic target neurons in other areas (Salinas and Sejnowski, 2000). This is consistent with the observed repetition-related enhancement of V1–V4 gamma coherence (Brunet et al., 2014) and of feedforward gamma GC (this study). Thereby, the sharpened neuronal population response might be communicated more efficiently (Lewis et al., 2021). Increased synchronization would compensate for overall lower firing rates, thereby allowing the visual system to keep or improve behavioral performance with less neuronal activity (Gotts et al., 2012). Such changes should be specific to the activated cell assembly, extend over time and be robust to deadaptation, as shown in this study.

Per-trial data and code for statistical analyses have been uploaded and are available at https://doi.org/10.5281/zenodo.4269714. Preprocessing code has been uploaded as source code file 1.

1. 1. Benjamin JS
   2. Alina P
   3. Heike S
   4. Pascal F

   (2020) Zenodo

   Dataset for Stimulus-specific plasticity in human visual gamma-band activity and functional connectivity.

   https://doi.org/10.5281/zenodo.4269714

1. Conference

   1. Arthur JV
   2. Boahen K
   3. Sholkopf B
   4. Weiss Y

   (2005)

   Learning in Silicon: timing is everything

   In NIPS'05: Proceedings of the 18th International Conference on Neural Information Processing. pp. 75–82.

   • Google Scholar
2. 1. Atallah BV
   2. Scanziani M

   (2009) Instantaneous modulation of gamma oscillation frequency by balancing excitation with inhibition

   Neuron 62:566–577.

   https://doi.org/10.1016/j.neuron.2009.04.027

   • PubMed
   • Google Scholar
3. 1. Auksztulewicz R
   2. Friston K

   (2016) Repetition suppression and its contextual determinants in predictive coding

   Cortex 80:125–140.

   https://doi.org/10.1016/j.cortex.2015.11.024

   • PubMed
   • Google Scholar
4. 1. Barone P
   2. Batardiere A
   3. Knoblauch K
   4. Kennedy H

   (2000) Laminar distribution of neurons in extrastriate Areas projecting to visual Areas V1 and V4 correlates with the hierarchical rank and indicates the operation of a distance rule

   The Journal of Neuroscience 20:3263–3281.

   https://doi.org/10.1523/JNEUROSCI.20-09-03263.2000

   • PubMed
   • Google Scholar
5. 1. Bastos AM
   2. Vezoli J
   3. Bosman CA
   4. Schoffelen JM
   5. Oostenveld R
   6. Dowdall JR
   7. De Weerd P
   8. Kennedy H
   9. Fries P

   (2015) Visual Areas exert feedforward and feedback influences through distinct frequency channels

   Neuron 85:390–401.

   https://doi.org/10.1016/j.neuron.2014.12.018

   • PubMed
   • Google Scholar
6. 1. Bates D
   2. Mächler M
   3. Bolker B
   4. Walker S

   (2015) Fitting linear Mixed-Effects models using lme4

   Journal of Statistical Software 67:i01.

   https://doi.org/10.18637/jss.v067.i01

   • Google Scholar
7. 1. Bazhenov M
   2. Stopfer M
   3. Sejnowski TJ
   4. Laurent G

   (2005) Fast odor learning improves reliability of odor responses in the locust antennal lobe

   Neuron 46:483–492.

   https://doi.org/10.1016/j.neuron.2005.03.022

   • PubMed
   • Google Scholar
8. 1. Benson NC
   2. Jamison KW
   3. Arcaro MJ
   4. Vu AT
   5. Glasser MF
   6. Coalson TS
   7. Van Essen DC
   8. Yacoub E
   9. Ugurbil K
   10. Winawer J
   11. Kay K

   (2018) The human connectome project 7 tesla retinotopy dataset: description and population receptive field analysis

   Journal of Vision 18:23.

   https://doi.org/10.1167/18.13.23

   • PubMed
   • Google Scholar
9. 1. Benwell CSY
   2. London RE
   3. Tagliabue CF
   4. Veniero D
   5. Gross J
   6. Keitel C
   7. Thut G

   (2019) Frequency and power of human alpha oscillations drift systematically with time-on-task

   NeuroImage 192:101–114.

   https://doi.org/10.1016/j.neuroimage.2019.02.067

   • PubMed
   • Google Scholar
10. 1. Beshel J
    2. Kopell N
    3. Kay LM

    (2007) Olfactory bulb gamma oscillations are enhanced with task demands

    Journal of Neuroscience 27:8358–8365.

    https://doi.org/10.1523/JNEUROSCI.1199-07.2007

    • PubMed
    • Google Scholar
11. 1. Binda P
    2. Pereverzeva M
    3. Murray SO

    (2013) Attention to bright surfaces enhances the pupillary light reflex

    Journal of Neuroscience 33:2199–2204.

    https://doi.org/10.1523/JNEUROSCI.3440-12.2013

    • PubMed
    • Google Scholar
12. 1. Blair RC
    2. Higgins JJ
    3. Karniski W
    4. Kromrey JD

    (1994) A study of multivariate permutation tests which may replace Hotelling's T2 Test in Prescribed Circumstances

    Multivariate Behavioral Research 29:141–163.

    https://doi.org/10.1207/s15327906mbr2902_2

    • PubMed
    • Google Scholar
13. Book

    1. Börgers C

    (2017)

    An Introduction to Modeling Neuronal Dynamics

    Switzerland: Springer.

    • Google Scholar
14. 1. Brunet NM
    2. Bosman CA
    3. Vinck M
    4. Roberts M
    5. Oostenveld R
    6. Desimone R
    7. De Weerd P
    8. Fries P

    (2014) Stimulus repetition modulates gamma-band synchronization in primate visual cortex

    PNAS 111:3626–3631.

    https://doi.org/10.1073/pnas.1309714111

    • PubMed
    • Google Scholar
15. 1. Brunet N
    2. Bosman CA
    3. Roberts M
    4. Oostenveld R
    5. Womelsdorf T
    6. De Weerd P
    7. Fries P

    (2015) Visual cortical gamma-band activity during free viewing of natural images

    Cerebral Cortex 25:918–926.

    https://doi.org/10.1093/cercor/bht280

    • PubMed
    • Google Scholar
16. 1. Caporale N
    2. Dan Y

    (2008) Spike timing-dependent plasticity: a hebbian learning rule

    Annual Review of Neuroscience 31:25–46.

    https://doi.org/10.1146/annurev.neuro.31.060407.125639

    • PubMed
    • Google Scholar
17. 1. Cassenaer S
    2. Laurent G

    (2007) Hebbian STDP in mushroom bodies facilitates the synchronous flow of olfactory information in locusts

    Nature 448:709–713.

    https://doi.org/10.1038/nature05973

    • PubMed
    • Google Scholar
18. 1. Chaudhuri R
    2. Knoblauch K
    3. Gariel MA
    4. Kennedy H
    5. Wang XJ

    (2015) A Large-Scale circuit mechanism for hierarchical dynamical processing in the primate cortex

    Neuron 88:419–431.

    https://doi.org/10.1016/j.neuron.2015.09.008

    • PubMed
    • Google Scholar
19. 1. Csicsvari J
    2. Jamieson B
    3. Wise KD
    4. Buzsáki G

    (2003) Mechanisms of gamma oscillations in the Hippocampus of the behaving rat

    Neuron 37:311–322.

    https://doi.org/10.1016/S0896-6273(02)01169-8

    • PubMed
    • Google Scholar
20. 1. de Almeida L
    2. Idiart M
    3. Lisman JE

    (2009) A second function of gamma frequency oscillations: an E%-max winner-take-all mechanism selects which cells fire

    The Neurosci 29:7497–7503.

    https://doi.org/10.1523/JNEUROSCI.6044-08.2009

    • PubMed
    • Google Scholar
21. 1. de Gee JW
    2. Colizoli O
    3. Kloosterman NA
    4. Knapen T
    5. Nieuwenhuis S
    6. Donner TH

    (2017) Dynamic modulation of decision biases by brainstem arousal systems

    eLife 6:e23232.

    https://doi.org/10.7554/eLife.23232

    • PubMed
    • Google Scholar
22. 1. Desimone R

    (1996) Neural mechanisms for visual memory and their role in attention

    PNAS 93:13494–13499.

    https://doi.org/10.1073/pnas.93.24.13494

    • PubMed
    • Google Scholar
23. 1. Dhamala M
    2. Rangarajan G
    3. Ding M

    (2008) Estimating Granger causality from Fourier and wavelet transforms of time series data

    Physical Review Letters 100:018701.

    https://doi.org/10.1103/PhysRevLett.100.018701

    • PubMed
    • Google Scholar
24. 1. Dong DW
    2. Atick JJ

    (1995) Statistics of natural time-varying images

    Network: Computation in Neural Systems 6:345–358.

    https://doi.org/10.1088/0954-898X_6_3_003

    • Google Scholar
25. 1. Engbert R
    2. Longtin A
    3. Kliegl R

    (2002) A dynamical model of saccade generation in reading based on spatially distributed lexical processing

    Vision Research 42:621–636.

    https://doi.org/10.1016/S0042-6989(01)00301-7

    • PubMed
    • Google Scholar
26. 1. Fiorentini A
    2. Berardi N

    (1980) Perceptual learning specific for orientation and spatial frequency

    Nature 287:43–44.

    https://doi.org/10.1038/287043a0

    • PubMed
    • Google Scholar
27. 1. Fischl B

    (2012) FreeSurfer

    NeuroImage 62:774–781.

    https://doi.org/10.1016/j.neuroimage.2012.01.021

    • PubMed
    • Google Scholar
28. 1. Fries P
    2. Nikolić D
    3. Singer W

    (2007) The gamma cycle

    Trends in Neurosciences 30:309–316.

    https://doi.org/10.1016/j.tins.2007.05.005

    • PubMed
    • Google Scholar
29. 1. Friese U
    2. Rahm B
    3. Hassler U
    4. Kaiser J
    5. Gruber T

    (2012a) Repetition suppression and effects of familiarity on blood oxygenation level dependent signal and gamma-band activity

    NeuroReport 23:757–761.

    https://doi.org/10.1097/WNR.0b013e328356b173

    • PubMed
    • Google Scholar
30. 1. Friese U
    2. Supp GG
    3. Hipp JF
    4. Engel AK
    5. Gruber T

    (2012b) Oscillatory MEG gamma band activity dissociates perceptual and conceptual aspects of visual object processing: a combined repetition/conceptual priming study

    NeuroImage 59:861–871.

    https://doi.org/10.1016/j.neuroimage.2011.07.073

    • PubMed
    • Google Scholar
31. 1. Glasser MF
    2. Coalson TS
    3. Robinson EC
    4. Hacker CD
    5. Harwell J
    6. Yacoub E
    7. Ugurbil K
    8. Andersson J
    9. Beckmann CF
    10. Jenkinson M
    11. Smith SM
    12. Van Essen DC

    (2016a) A multi-modal parcellation of human cerebral cortex

    Nature 536:171–178.

    https://doi.org/10.1038/nature18933

    • PubMed
    • Google Scholar
32. 1. Glasser MF
    2. Smith SM
    3. Marcus DS
    4. Andersson JL
    5. Auerbach EJ
    6. Behrens TE
    7. Coalson TS
    8. Harms MP
    9. Jenkinson M
    10. Moeller S
    11. Robinson EC
    12. Sotiropoulos SN
    13. Xu J
    14. Yacoub E
    15. Ugurbil K
    16. Van Essen DC

    (2016b) The human connectome project's neuroimaging approach

    Nature Neuroscience 19:1175–1187.

    https://doi.org/10.1038/nn.4361

    • PubMed
    • Google Scholar
33. 1. Gotts SJ
    2. Chow CC
    3. Martin A

    (2012) Repetition priming and repetition suppression: a case for enhanced efficiency through neural synchronization

    Cognitive Neuroscience 3:227–237.

    https://doi.org/10.1080/17588928.2012.670617

    • PubMed
    • Google Scholar
34. 1. Grill-Spector K
    2. Henson R
    3. Martin A

    (2006) Repetition and the brain: neural models of stimulus-specific effects

    Trends in Cognitive Sciences 10:14–23.

    https://doi.org/10.1016/j.tics.2005.11.006

    • PubMed
    • Google Scholar
35. 1. Gross J
    2. Kujala J
    3. Hamalainen M
    4. Timmermann L
    5. Schnitzler A
    6. Salmelin R

    (2001) Dynamic imaging of coherent sources: studying neural interactions in the human brain

    PNAS 98:694–699.

    https://doi.org/10.1073/pnas.98.2.694

    • PubMed
    • Google Scholar
36. 1. Haas JS
    2. Nowotny T
    3. Abarbanel HD

    (2006) Spike-timing-dependent plasticity of inhibitory synapses in the entorhinal cortex

    Journal of Neurophysiology 96:3305–3313.

    https://doi.org/10.1152/jn.00551.2006

    • PubMed
    • Google Scholar
37. Preprint

    1. Haller M
    2. Donoghue T
    3. Peterson E
    4. Varma P
    5. Sebastian P
    6. Gao R
    7. Noto T
    8. Knight RT
    9. Shestyuk A
    10. Voytek B

    (2018) Parameterizing neural power spectra

    bioRxiv.

    https://doi.org/10.1101/299859

    • Google Scholar
38. 1. Hasenstaub A
    2. Shu Y
    3. Haider B
    4. Kraushaar U
    5. Duque A
    6. McCormick DA

    (2005) Inhibitory postsynaptic potentials carry synchronized frequency information in active cortical networks

    Neuron 47:423–435.

    https://doi.org/10.1016/j.neuron.2005.06.016

    • PubMed
    • Google Scholar
39. Conference

    1. Haufe S
    2. Nikulin VV
    3. Nolte G

    (2012)

    Alleviating the influence of weak data asymmetries on Granger-Causal analyses

    In International Conference on Latent Variable Analysis and Signal Separation pp. pp. 25–33.

    • Google Scholar
40. 1. Havenith MN
    2. Yu S
    3. Biederlack J
    4. Chen NH
    5. Singer W
    6. Nikolić D

    (2011) Synchrony makes neurons fire in sequence, and stimulus properties determine who is ahead

    Journal of Neuroscience 31:8570–8584.

    https://doi.org/10.1523/JNEUROSCI.2817-10.2011

    • PubMed
    • Google Scholar
41. 1. Hennequin G
    2. Agnes EJ
    3. Vogels TP

    (2017) Inhibitory plasticity: balance, control, and codependence

    Annual Review of Neuroscience 40:557–579.

    https://doi.org/10.1146/annurev-neuro-072116-031005

    • PubMed
    • Google Scholar
42. Preprint

    1. Homann J
    2. Koay SA
    3. Glidden AM
    4. Tank DW
    5. Berry MJ

    (2017) Predictive coding of novel versus familiar stimuli in the primary visual cortex

    bioRxiv.

    https://doi.org/10.1101/197608

    • Google Scholar
43. 1. Huang S
    2. Huganir RL
    3. Kirkwood A

    (2013) Adrenergic gating of hebbian spike-timing-dependent plasticity in cortical interneurons

    Journal of Neuroscience 33:13171–13178.

    https://doi.org/10.1523/JNEUROSCI.5741-12.2013

    • PubMed
    • Google Scholar
44. 1. Huang G
    2. Ramachandran S
    3. Lee TS
    4. Olson CR

    (2018) Neural correlate of visual familiarity in macaque area V2

    The Journal of Neuroscience 38:8967–8975.

    https://doi.org/10.1523/JNEUROSCI.0664-18.2018

    • PubMed
    • Google Scholar
45. 1. Jia X
    2. Smith MA
    3. Kohn A

    (2011) Stimulus selectivity and spatial coherence of gamma components of the local field potential

    Journal of Neuroscience 31:9390–9403.

    https://doi.org/10.1523/JNEUROSCI.0645-11.2011

    • PubMed
    • Google Scholar
46. 1. Kispersky T
    2. Gutierrez GJ
    3. Marder E

    (2011) Functional connectivity in a rhythmic inhibitory circuit using Granger causality

    Neural Systems & Circuits 1:9.

    https://doi.org/10.1186/2042-1001-1-9

    • PubMed
    • Google Scholar
47. 1. Knapen T
    2. de Gee JW
    3. Brascamp J
    4. Nuiten S
    5. Hoppenbrouwers S
    6. Theeuwes J

    (2016) Cognitive and ocular factors jointly determine pupil responses under equiluminance

    PLOS ONE 11:e0155574.

    https://doi.org/10.1371/journal.pone.0155574

    • PubMed
    • Google Scholar
48. 1. Kok P
    2. van Lieshout LL
    3. de Lange FP

    (2016) Local expectation violations result in global activity gain in primary visual cortex

    Scientific Reports 6:37706.

    https://doi.org/10.1038/srep37706

    • PubMed
    • Google Scholar
49. 1. König P
    2. Engel AK
    3. Roelfsema PR
    4. Singer W

    (1995) How precise is neuronal synchronization?

    Neural Computation 7:469–485.

    https://doi.org/10.1162/neco.1995.7.3.469

    • PubMed
    • Google Scholar
50. 1. Kreiter AK
    2. Singer W

    (1992) Oscillatory neuronal responses in the visual cortex of the awake macaque monkey

    European Journal of Neuroscience 4:369–375.

    https://doi.org/10.1111/j.1460-9568.1992.tb00884.x

    • PubMed
    • Google Scholar
51. 1. Lewis CM
    2. Ni J
    3. Wunderle T
    4. Jendritza P
    5. Lazar A
    6. Diester I
    7. Fries P

    (2021) Cortical gamma-band resonance preferentially transmits coherent input

    Cell Reports 35:109083.

    https://doi.org/10.1016/j.celrep.2021.109083

    • PubMed
    • Google Scholar
52. 1. Li L
    2. Miller EK
    3. Desimone R

    (1993) The representation of stimulus familiarity in anterior inferior temporal cortex

    Journal of Neurophysiology 69:1918–1929.

    https://doi.org/10.1152/jn.1993.69.6.1918

    • PubMed
    • Google Scholar
53. 1. Lim S
    2. McKee JL
    3. Woloszyn L
    4. Amit Y
    5. Freedman DJ
    6. Sheinberg DL
    7. Brunel N

    (2015) Inferring learning rules from distributions of firing rates in cortical neurons

    Nature Neuroscience 18:1804–1810.

    https://doi.org/10.1038/nn.4158

    • PubMed
    • Google Scholar
54. 1. Luke SG

    (2017) Evaluating significance in linear mixed-effects models in R

    Behavior Research Methods 49:1494–1502.

    https://doi.org/10.3758/s13428-016-0809-y

    • Google Scholar
55. 1. Manahova ME
    2. Mostert P
    3. Kok P
    4. Schoffelen JM
    5. de Lange FP

    (2018) Stimulus familiarity and expectation jointly modulate neural activity in the visual ventral stream

    Journal of Cognitive Neuroscience 30:1366–1377.

    https://doi.org/10.1162/jocn_a_01281

    • PubMed
    • Google Scholar
56. 1. Maris E
    2. Oostenveld R

    (2007) Nonparametric statistical testing of EEG- and MEG-data

    Journal of Neuroscience Methods 164:177–190.

    https://doi.org/10.1016/j.jneumeth.2007.03.024

    • PubMed
    • Google Scholar
57. 1. Michalareas G
    2. Vezoli J
    3. van Pelt S
    4. Schoffelen JM
    5. Kennedy H
    6. Fries P

    (2016) Alpha-Beta and gamma rhythms subserve feedback and feedforward influences among human visual cortical Areas

    Neuron 89:384–397.

    https://doi.org/10.1016/j.neuron.2015.12.018

    • PubMed
    • Google Scholar
58. 1. Naber M
    2. Frässle S
    3. Rutishauser U
    4. Einhäuser W

    (2013) Pupil size signals novelty and predicts later retrieval success for declarative memories of natural scenes

    Journal of Vision 13:11.

    https://doi.org/10.1167/13.2.11

    • PubMed
    • Google Scholar
59. 1. Nasiotis K
    2. Clavagnier S
    3. Baillet S
    4. Pack CC

    (2017) High-resolution retinotopic maps estimated with magnetoencephalography

    NeuroImage 145:107–117.

    https://doi.org/10.1016/j.neuroimage.2016.10.017

    • PubMed
    • Google Scholar
60. 1. Olshausen BA
    2. Field DJ

    (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images

    Nature 381:607–609.

    https://doi.org/10.1038/381607a0

    • PubMed
    • Google Scholar
61. 1. Oostenveld R
    2. Fries P
    3. Maris E
    4. Schoffelen JM

    (2011) FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data

    Computational Intelligence and Neuroscience 2011:1–9.

    https://doi.org/10.1155/2011/156869

    • PubMed
    • Google Scholar
62. 1. Peinkhofer C
    2. Knudsen GM
    3. Moretti R
    4. Kondziella D

    (2019) Cortical modulation of pupillary function: systematic review

    PeerJ 7:e6882.

    https://doi.org/10.7717/peerj.6882

    • PubMed
    • Google Scholar
63. Preprint

    1. Peter A
    2. Stauch BJ
    3. Shapcott K
    4. Kouroupaki K
    5. Schmiedt JT
    6. Klein L
    7. Klon-Lipok J
    8. Dowdall JR
    9. Schölvinck ML
    10. Vinck M
    11. Singer W
    12. Schmid MC
    13. Fries P

    (2020) Stimulus-specific plasticity of macaque V1 spike rates and gamma

    bioRxiv.

    https://doi.org/10.1101/2020.11.13.381418

    • Google Scholar
64. 1. Rao RP
    2. Ballard DH

    (1999) Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects

    Nature Neuroscience 2:79–87.

    https://doi.org/10.1038/4580

    • PubMed
    • Google Scholar
65. 1. Salinas E
    2. Sejnowski TJ

    (2000) Impact of correlated synaptic input on output firing rate and variability in simple neuronal models

    The Journal of Neuroscience 20:6193–6209.

    https://doi.org/10.1523/JNEUROSCI.20-16-06193.2000

    • PubMed
    • Google Scholar
66. 1. Salkoff DB
    2. Zagha E
    3. Yuzgec O
    4. McCormick DA

    (2015) Synaptic mechanisms of tight spike synchrony at Gamma frequency in cerebral cortex

    Journal of Neuroscience 35:10236–10251.

    https://doi.org/10.1523/JNEUROSCI.0828-15.2015

    • Google Scholar
67. 1. Schoups A
    2. Vogels R
    3. Qian N
    4. Orban G

    (2001) Practising orientation identification improves orientation coding in V1 neurons

    Nature 412:549–553.

    https://doi.org/10.1038/35087601

    • PubMed
    • Google Scholar
68. 1. Stern CE
    2. Corkin S
    3. González RG
    4. Guimaraes AR
    5. Baker JR
    6. Jennings PJ
    7. Carr CA
    8. Sugiura RM
    9. Vedantham V
    10. Rosen BR

    (1996) The hippocampal formation participates in novel picture encoding: evidence from functional magnetic resonance imaging

    PNAS 93:8660–8665.

    https://doi.org/10.1073/pnas.93.16.8660

    • PubMed
    • Google Scholar
69. 1. Stopfer M
    2. Laurent G

    (1999) Short-term memory in olfactory network dynamics

    Nature 402:664–668.

    https://doi.org/10.1038/45244

    • PubMed
    • Google Scholar
70. 1. Suri RE
    2. Sejnowski TJ

    (2002) Spike propagation synchronized by temporally asymmetric hebbian learning

    Biological Cybernetics 87:440–445.

    https://doi.org/10.1007/s00422-002-0355-9

    • PubMed
    • Google Scholar
71. 1. Torralba A
    2. Oliva A

    (2003) Statistics of natural image categories

    Network: Computation in Neural Systems 14:391–412.

    https://doi.org/10.1088/0954-898X_14_3_302

    • Google Scholar
72. Preprint

    1. Uran C
    2. Peter A
    3. Lazar A
    4. Barnes W
    5. Klon-Lipok J
    6. Shapcott KA
    7. Roese R
    8. Fries P
    9. Singer W
    10. Vinck M

    (2020) Predictive coding of natural images by V1 activity revealed by self-supervised deep neural networks

    bioRxiv.

    https://doi.org/10.1101/2020.08.10.242958

    • Google Scholar
73. 1. Van Veen BD
    2. van Drongelen W
    3. Yuchtman M
    4. Suzuki A

    (1997) Localization of brain electrical activity via linearly constrained minimum variance spatial filtering

    IEEE Transactions on Biomedical Engineering 44:867–880.

    https://doi.org/10.1109/10.623056

    • PubMed
    • Google Scholar
74. 1. van Wingerden M
    2. Vinck M
    3. Lankelma JV
    4. Pennartz CM

    (2010) Learning-associated gamma-band phase-locking of action-outcome selective neurons in orbitofrontal cortex

    Journal of Neuroscience 30:10025–10038.

    https://doi.org/10.1523/JNEUROSCI.0222-10.2010

    • PubMed
    • Google Scholar
75. 1. Vinck M
    2. Lima B
    3. Womelsdorf T
    4. Oostenveld R
    5. Singer W
    6. Neuenschwander S
    7. Fries P

    (2010) Gamma-phase shifting in awake monkey visual cortex

    Journal of Neuroscience 30:1250–1257.

    https://doi.org/10.1523/JNEUROSCI.1623-09.2010

    • PubMed
    • Google Scholar
76. 1. Vinck M
    2. Womelsdorf T
    3. Buffalo EA
    4. Desimone R
    5. Fries P

    (2013) Attentional modulation of cell-class-specific gamma-band synchronization in awake monkey area v4

    Neuron 80:1077–1089.

    https://doi.org/10.1016/j.neuron.2013.08.019

    • PubMed
    • Google Scholar
77. 1. Vinck M
    2. Huurdeman L
    3. Bosman CA
    4. Fries P
    5. Battaglia FP
    6. Pennartz CM
    7. Tiesinga PH

    (2015) How to detect the Granger-causal flow direction in the presence of additive noise?

    NeuroImage 108:301–318.

    https://doi.org/10.1016/j.neuroimage.2014.12.017

    • PubMed
    • Google Scholar
78. 1. Wang Y
    2. Iliescu BF
    3. Ma J
    4. Josić K
    5. Dragoi V

    (2011) Adaptive changes in neuronal synchronization in macaque V4

    Journal of Neuroscience 31:13204–13213.

    https://doi.org/10.1523/JNEUROSCI.6227-10.2011

    • PubMed
    • Google Scholar
79. 1. Wilming N
    2. Harst S
    3. Schmidt N
    4. König P

    (2013) Saccadic momentum and facilitation of return saccades contribute to an optimal foraging strategy

    PLOS Computational Biology 9:e1002871.

    https://doi.org/10.1371/journal.pcbi.1002871

    • PubMed
    • Google Scholar
80. 1. Woloszyn L
    2. Sheinberg DL

    (2012) Effects of long-term visual experience on responses of distinct classes of single units in inferior temporal cortex

    Neuron 74:193–205.

    https://doi.org/10.1016/j.neuron.2012.01.032

    • PubMed
    • Google Scholar

Author details

1. Benjamin J Stauch

   1. Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany
   2. International Max Planck Research School for Neural Circuits, Frankfurt, Germany
   3. Brain Imaging Center, Goethe University Frankfurt, Frankfurt, Germany

   Contribution

   Conceptualization, Software, Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   benjamin.stauch@esi-frankfurt.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4484-813X

2. Alina Peter

   1. Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany
   2. International Max Planck Research School for Neural Circuits, Frankfurt, Germany

   Contribution

   Conceptualization, Formal analysis, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8497-6235

3. Heike Schuler

   Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

4. Pascal Fries

   1. Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt, Germany
   2. International Max Planck Research School for Neural Circuits, Frankfurt, Germany
   3. Brain Imaging Center, Goethe University Frankfurt, Frankfurt, Germany
   4. Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands

   Contribution

   Conceptualization, Formal analysis, Supervision, Funding acquisition, Methodology, Writing - original draft, Writing - review and editing

   For correspondence

   pascal.fries@esi-frankfurt.de

   Competing interests

   has a patent on thin-film electrodes (US20170181707A1) and is beneficiary of a respective license contract on thin-film electrodes with Blackrock Microsystems LLC (Salt Lake City, UT),). P.F. is member of the Scientific Technical Advisory Board of CorTec GmbH (Freiburg, Germany), and managing director of Brain Science GmbH (Frankfurt am Main, Germany). P.F. declares no further competing interests.

   "This ORCID iD identifies the author of this article:" 0000-0002-4270-1468

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