Efficient “Communication through Coherence” Requires Oscillations Structured to Minimize Interference between Signals
Open accessAbstract
The 'communication through coherence' (CTC) hypothesis proposes that selective communication among neural networks is achieved by coherence between firing rate oscillation in a sending region and gain modulation in a receiving region. Although this hypothesis has stimulated extensive work, it remains unclear whether the mechanism can in principle allow reliable and selective information transfer. Here we use a simple mathematical model to investigate how accurately coherent gain modulation can filter a population-coded target signal from task-irrelevant distracting inputs. We show that selective communication can indeed be achieved, although the structure of oscillatory activity in the target and distracting networks must satisfy certain previously unrecognized constraints. Firstly, the target input must be differentiated from distractors by the amplitude, phase or frequency of its oscillatory modulation. When distracting inputs oscillate incoherently in the same frequency band as the target, communication accuracy is severely degraded because of varying overlap between the firing rate oscillations of distracting inputs and the gain modulation in the receiving region. Secondly, the oscillatory modulation of the target input must be strong in order to achieve a high signal-to-noise ratio relative to stochastic spiking of individual neurons. Thus, whilst providing a quantitative demonstration of the power of coherent oscillatory gain modulation to flexibly control information flow, our results identify constraints imposed by the need to avoid interference between signals, and reveal a likely organizing principle for the structure of neural oscillations in the brain.
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Selective communication can be achieved through coherence between firing rate oscillation in the sending region and oscillatory gain modulation in the receiving region.
“invoking the principle of ‘communication through coherence’—that selective communication can be achieved through coherence between firing rate oscillation in the sending region and oscillatory gain modulation in the receiving region [63,64].”
From Cerebral hierarchies: predictive processing, precision and the pulvinar (2015) · cites this paper as [64]
A CA1 network which has evolved a predisposition to oscillate at theta and gamma frequencies may enable more efficient processing of the phasic input it receives in vivo.
“a CA1 network which has evolved a predisposition to oscillate at theta and gamma frequencies may enable more efficient processing of the phasic input it receives in vivo (Akam and Kullmann, 2012; Fries, 2015).”
From Interneuronal mechanisms of hippocampal theta oscillations in a full-scale model of the rodent CA1 circuit (2016) · cites this paper as Akam and Kullmann, 2012
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Cites
Neuronal Oscillations in Cortical Networks
Buzsáki, G., & Draguhn, A. (2004) · Science · doi:10.1126/science.1099745 · Cited by 6,576 · cited 1× · see citing works
“Such activity spans several orders of magnitude in frequency [31], and in several brain regions the phase of firing is actively modulated relative to a single coherent oscillation [26,28,29].”
BackgroundNeurophysiological investigation of the basis of the fMRI signal
Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., & Oeltermann, A. (2001) · Nature · doi:10.1038/35084005 · Cited by 6,542 · see citing works
The brainweb: phase synchronization and large-scale integration
Varela, F., Lachaux, J.-P., Rodriguez, E., & Martinerie, J. (2001) · Nature Reviews Neuroscience · doi:10.1038/35067550 · Cited by 4,761 · cited 1× · see citing works
“A possible function of such activity is to modulate functional connectivity among anatomically connected regions [4–6].”
BackgroundA mechanism for cognitive dynamics: neuronal communication through neuronal coherence.
Fries, P. (2005) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2005.08.011 · Cited by 4,284 · cited 5× · see citing works
“This study provides a quantitative assessment of the proposal that selective communication can be achieved by coherence between firing rate modulation in a sending region and gain modulation in a receiving region [5].”
BackgroundInfluentialModulation of Oscillatory Neuronal Synchronization by Selective Visual Attention
Fries, P., Reynolds, J. H., Rorie, A. E., & Desimone, R. (2001) · Science · doi:10.1126/science.1055465 · Cited by 2,786 · cited 2× · see citing works
“The resulting activity was consistent with in vivo data showing irregular spiking of single units [16–18] during sparsely synchronized oscillatory activity [19].”
BackgroundResultPhase relationship between hippocampal place units and the EEG theta rhythm
O'Keefe, J. M., & Recce, M. L. (1993) · Hippocampus · doi:10.1002/hipo.450030307 · Cited by 2,561 · see citing works
Neuronal gamma-band synchronization as a fundamental process in cortical computation
Fries, P. (2009) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.051508.135603 · Cited by 1,713 · cited 2× · see citing works
“The ‘communication through coherence’ (CTC) hypothesis [5,7] proposes that selective communication is achieved through coherence between firing rate oscillation in the sending region and oscillatory gain modulation in the receiving region.”
BackgroundFrequency of gamma oscillations routes flow of information in the hippocampus
Colgin, L. L., Denninger, T., Fyhn, M., Hafting, T., Bonnevie, T., Jensen, O. B., Moser, M., & Moser, E. I. (2009) · Nature · doi:10.1038/nature08573 · Cited by 1,425 · cited 1× · see citing works
“…different oscillations, from very strong modulations during hippocampal theta oscillations [47], and oscillations in the olfactory system of zebrafish [27] and locusts [48], to apparently weaker modulation in some studies of gamma oscillations in the hippocampus [49,50] and entorhinal cortex [49].”
BackgroundModulation of Neuronal Interactions Through Neuronal Synchronization
Womelsdorf, T., Schoffelen, J.-M., Oostenveld, R., Singer, W., Desimone, R., Engel, A. K., & Fries, P. (2007) · Science · doi:10.1126/science.1139597 · Cited by 1,367 · cited 2× · see citing works
“We next evaluated the proposal that changes in the inter-region coherence of oscillatory activity [5,8,10] (as distinct from changes in frequency or amplitude, or changes in a consistent phase relationship) could be used to switch on or off information propagation through a convergent pathway.”
BackgroundBrain-state- and cell-type-specific firing of hippocampal interneurons in vivo
Klausberger, T., Magill, P. J., Márton, L. F., Roberts, J. D. B., Cobden, P. M., Buzsáki, G., & Somogyi, P. (2003) · Nature · doi:10.1038/nature01374 · Cited by 1,345 · cited 1× · see citing works
“…show a wide range of modulation strengths across different oscillations, from very strong modulations during hippocampal theta oscillations [47], and oscillations in the olfactory system of zebrafish [27] and locusts [48], to apparently weaker modulation in some studies of gamma…”
BackgroundCorrelated neuronal activity and the flow of neural information
Salinas, E., & Sejnowski, T. J. (2001) · Nature Reviews Neuroscience · doi:10.1038/35086012 · Cited by 1,333 · cited 1× · see citing works
“A possible function of such activity is to modulate functional connectivity among anatomically connected regions [4–6].”
BackgroundHigh-frequency, long-range coupling between prefrontal and visual cortex during attention
Gregoriou, G. G., Gotts, S. J., Zhou, H., & Desimone, R. (2009) · Science · doi:10.1126/science.1171402 · Cited by 1,194 · cited 1× · see citing works
“Changes in oscillatory activity dependent on visual spatial attention [3,16,40,41] have been proposed to underlie the selective processing of behaviorally relevant stimuli [5,7].”
BackgroundHerrmann, C. S. (2001) · Experimental Brain Research · doi:10.1007/s002210100682 · Cited by 1,080 · cited 1× · see citing works
“We note that gamma oscillations in V1 are particularly amenable to experimental phase manipulation as they are readily entrained by flickering visual stimuli [43], as expected given the response dynamics of gamma oscillating networks in vitro [44].”
BackgroundThe physiology and pharmacology of hippocampal formation theta rhythms
Bland, B. H. (1986) · Progress in Neurobiology · doi:10.1016/0301-0082(86)90019-5 · Cited by 1,039 · cited 1× · see citing works
“Various studies have reported switching between distinct oscillation frequencies in a local network [36–38], potentially reflecting participation in distinct large scale networks utilizing different frequencies for communication.”
BackgroundTheta rhythms coordinate hippocampal-prefrontal interactions in a spatial memory task
Jones, M. W., & Wilson, M. A. (2005) · PLoS Biology · doi:10.1371/journal.pbio.0030402 · Cited by 1,036 · cited 1× · see citing works
“Task-dependent changes in the power and inter-region coherence of oscillatory network activity are observed in many brain regions and behavioral tasks [1–3].”
BackgroundGain Modulation from Background Synaptic Input
Chance, F. S., Abbott, L. F., & Reyes, A. D. (2002) · Neuron · doi:10.1016/s0896-6273(02)00820-6 · Cited by 1,012 · see citing works
Coherent 25- to 35-Hz oscillations in the sensorimotor cortex of awake behaving monkeys.
Murthy, V. N., & Fetz, E. E. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.12.5670 · Cited by 996 · cited 1× · see citing works
“Striking bursts of transient task dependent oscillatory activity are well documented in many brain regions including in motor cortex during movement preparation [32,33], the basal ganglia during cue utilization [34], and in visual cortex during working memory [35].”
BackgroundOscillations and Sparsening of Odor Representations in the Mushroom Body
Pérez-Orive, J., Mazor, O., Turner, G. C., Cassenaer, S., Wilson, R. I., & Laurent, G. (2002) · Science · doi:10.1126/science.1070502 · Cited by 792 · cited 1× · see citing works
“Despite these technical difficulties reported spike phase histograms show a wide range of modulation strengths across different oscillations, from very strong modulations during hippocampal theta oscillations [47], and oscillations in the olfactory system of zebrafish [27] and locusts [48], to apparently weaker modula…”
BackgroundAttentional stimulus selection through selective synchronization between monkey visual areas
Bosman, C. A., Schoffelen, J.-M., Brunet, N., Oostenveld, R., Bastos, A. M., Womelsdorf, T., Rubehn, B., Stieglitz, T., De Weerd, P., & Fries, P. (2012) · Neuron · doi:10.1016/j.neuron.2012.06.037 · Cited by 720 · see citing works
Hasenstaub, A. R., Shu, Y., Haider, B., Kraushaar, U., Duque, A., & McCormick, D. A. (2005) · Neuron · doi:10.1016/j.neuron.2005.06.016 · Cited by 654 · see citing works
Probabilistic population codes for Bayesian decision making
Beck, J. M., Ma, W. J., Kiani, R., Hanks, T., Churchland, A. K., Roitman, J., Shadlen, M. N., Latham, P. E., & Pouget, A. (2008) · Neuron · doi:10.1016/j.neuron.2008.09.021 · Cited by 645 · cited 1× · see citing works
“Under many noise distributions these decoders perform close to optimally, as indicated by the minimal difference in performance when compared with more sophisticated non-linear methods [22,23].”
BackgroundShunting Inhibition Modulates Neuronal Gain during Synaptic Excitation
Mitchell, S. J., & Silver, R. A. (2003) · Neuron · doi:10.1016/s0896-6273(03)00200-9 · Cited by 602 · cited 2× · see citing works
“Physiologically, gain modulation could be achieved by local interneuron circuitry modulating the distribution of membrane potentials, degree of shunting inhibition [20], or synaptic noise [21] experienced by the principal neurons of the receiving network.”
BackgroundHuerta, P. T., & Lisman, J. E. (1993) · Nature · doi:10.1038/364723a0 · Cited by 564 · cited 1× · see citing works
“Oscillatory activity can also modulate the gain of synaptic plasticity [54,55], and spike timing dependent plasticity with an oscillating post synaptic population will also produce periodic modulation of the gain for plasticity.”
BackgroundDonoghue, J. P., Sanes, J. N., Hatsopoulos, N. G., & Gaál, G. (1998) · Journal of Neurophysiology · doi:10.1152/jn.1998.79.1.159 · Cited by 538 · cited 1× · see citing works
“Striking bursts of transient task dependent oscillatory activity are well documented in many brain regions including in motor cortex during movement preparation [32,33], the basal ganglia during cue utilization [34], and in visual cortex during working memory [35].”
BackgroundNeuronal Coherence as a Mechanism of Effective Corticospinal Interaction
Schoffelen, J., Oostenveld, R., & Fries, P. (2005) · Science · doi:10.1126/science.1107027 · Cited by 519 · cited 2× · see citing works
“We next evaluated the proposal that changes in the inter-region coherence of oscillatory activity [5,8,10] (as distinct from changes in frequency or amplitude, or changes in a consistent phase relationship) could be used to switch on or off information propagation through a convergent pathway.”
BackgroundNeuronal Synchronization along the Dorsal Visual Pathway Reflects the Focus of Spatial Attention
Siegel, M., Donner, T. H., Oostenveld, R., Fries, P., & Engel, A. K. (2008) · Neuron · doi:10.1016/j.neuron.2008.09.010 · Cited by 487 · cited 1× · see citing works
“Changes in oscillatory activity dependent on visual spatial attention [3,16,40,41] have been proposed to underlie the selective processing of behaviorally relevant stimuli [5,7].”
BackgroundPavlides, C., Greenstein, Y. J., Grudman, M., & Winson, J. (1988) · Brain Research · doi:10.1016/0006-8993(88)91499-0 · Cited by 485 · cited 1× · see citing works
“Oscillatory activity can also modulate the gain of synaptic plasticity [54,55], and spike timing dependent plasticity with an oscillating post synaptic population will also produce periodic modulation of the gain for plasticity.”
BackgroundFree choice activates a decision circuit between frontal and parietal cortex
Pesaran, B., Nelson, M., & Andersen, R. (2008) · Nature · doi:10.1038/nature06849 · Cited by 479 · cited 1× · see citing works
“Task-dependent changes in the power and inter-region coherence of oscillatory network activity are observed in many brain regions and behavioral tasks [1–3].”
BackgroundReading population codes: a neural implementation of ideal observers
Denève, S., Latham, P. E., & Pouget, A. (1999) · Nature Neuroscience · doi:10.1038/11205 · Cited by 392 · cited 1× · see citing works
“These decoders are, moreover, biologically plausible as their performance corresponds to that of de-noising by networks implementing line attractor dynamics [24,25].”
BackgroundLee, H. B., Simpson, G. V., Logothetis, N. K., & Rainer, G. (2005) · Neuron · doi:10.1016/j.neuron.2004.12.025 · Cited by 388 · cited 1× · see citing works
“Striking bursts of transient task dependent oscillatory activity are well documented in many brain regions including in motor cortex during movement preparation [32,33], the basal ganglia during cue utilization [34], and in visual cortex during working memory [35].”
BackgroundCell Type-Specific Tuning of Hippocampal Interneuron Firing during Gamma Oscillations<i>In Vivo</i>
Tukker, J. J., Fuentealba, P., Hartwich, K., Somogyi, P., & Klausberger, T. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.1685-07.2007 · Cited by 304 · cited 1× · see citing works
“…different oscillations, from very strong modulations during hippocampal theta oscillations [47], and oscillations in the olfactory system of zebrafish [27] and locusts [48], to apparently weaker modulation in some studies of gamma oscillations in the hippocampus [49,50] and entorhinal cortex [49].”
BackgroundBasal Ganglia Beta Oscillations Accompany Cue Utilization
Leventhal, D., Gage, G. J., Schmidt, R., Pettibone, J. R., Case, A. C., & Berke, J. D. (2012) · Neuron · doi:10.1016/j.neuron.2011.11.032 · Cited by 299 · cited 1× · see citing works
“Striking bursts of transient task dependent oscillatory activity are well documented in many brain regions including in motor cortex during movement preparation [32,33], the basal ganglia during cue utilization [34], and in visual cortex during working memory [35].”
BackgroundMultiplexing using synchrony in the zebrafish olfactory bulb
Friedrich, R. W., Habermann, C. J., & Laurent, G. (2004) · Nature Neuroscience · doi:10.1038/nn1292 · Cited by 254 · cited 1× · see citing works
“…different oscillations, from very strong modulations during hippocampal theta oscillations [47], and oscillations in the olfactory system of zebrafish [27] and locusts [48], to apparently weaker modulation in some studies of gamma oscillations in the hippocampus [49,50] and entorhinal cortex [49].”
BackgroundOscillations and Filtering Networks Support Flexible Routing of Information
Akam, T., & Kullmann, D. M. (2010) · Neuron · doi:10.1016/j.neuron.2010.06.019 · Cited by 245 · cited 4× · see citing works
“We previously developed a biophysical model [15], which exploits network resonance effects at the boundary between asynchronous and oscillating states [51] to selectively respond to inputs oscillating at a specific frequency.”
BackgroundMethodInfluentialSelf-citationSeriès, P., Latham, P. E., & Pouget, A. (2004) · Nature Neuroscience · doi:10.1038/nn1321 · Cited by 240 · cited 1× · see citing works
“Under many noise distributions these decoders perform close to optimally, as indicated by the minimal difference in performance when compared with more sophisticated non-linear methods [22,23].”
BackgroundTheta Phase Precession in Rat Ventral Striatum Links Place and Reward Information
van der Meer, M. A. A., & Redish, A. D. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.4869-10.2011 · Cited by 234 · see citing works
Two Distinct Channels of Olfactory Bulb Output
Fukunaga, I., Berning, M., Köllő, M., Schmaltz, A., & Schaefer, A. T. (2012) · Neuron · doi:10.1016/j.neuron.2012.05.017 · Cited by 223 · see citing works
Phase precession of medial prefrontal cortical activity relative to the hippocampal theta rhythm
Jones, M. W., & Wilson, M. A. (2005) · Hippocampus · doi:10.1002/hipo.20119 · Cited by 220 · cited 2× · see citing works
“Such activity spans several orders of magnitude in frequency [31], and in several brain regions the phase of firing is actively modulated relative to a single coherent oscillation [26,28,29].”
BackgroundA Microsaccadic Rhythm Modulates Gamma-Band Synchronization and Behavior
Bosman, C. A., Womelsdorf, T., Desimone, R., & Fries, P. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.1193-09.2009 · Cited by 212 · cited 1× · see citing works
“One possibility discussed by the authors is that theta frequency resetting of gamma oscillation phase [42] across V1 and V4, combined with the frequency offset between the V1 networks, creates periods in which the attended V1 site consistently leads the unattended site.”
BackgroundBerke, J. D. (2009) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2009.06843.x · Cited by 195 · cited 1× · see citing works
“Various studies have reported switching between distinct oscillation frequencies in a local network [36–38], potentially reflecting participation in distinct large scale networks utilizing different frequencies for communication.”
Börgers, C., Epstein, S., & Kopell, N. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0809511105 · Cited by 165 · cited 1× · see citing works
“Despite extensive experimental [8–10] and computational work [11–14], it remains unclear under what conditions the CTC mechanism could allow a network to distinguish among converging population-coded signals, and how its performance depends on the structure of their oscillatory modulations.”
Backgroundvan der Meer, M. A. A. (2009) · Frontiers in Integrative Neuroscience · doi:10.3389/neuro.07.009.2009 · Cited by 156 · see citing works
Dynamics of Networks of Excitatory and Inhibitory Neurons in Response to Time-Dependent Inputs
Ledoux, E., & Brunel, N. (2011) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2011.00025 · Cited by 148 · cited 1× · see citing works
“We previously developed a biophysical model [15], which exploits network resonance effects at the boundary between asynchronous and oscillating states [51] to selectively respond to inputs oscillating at a specific frequency.”
BackgroundSparsely synchronized neuronal oscillations
Brunel, N., & Hakim, V. (2008) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.2779858 · Cited by 137 · cited 1× · see citing works
“The resulting activity was consistent with in vivo data showing irregular spiking of single units [16–18] during sparsely synchronized oscillatory activity [19].”
ResultCorticospinal Beta-Band Synchronization Entails Rhythmic Gain Modulation
van Elswijk, G., Maij, F., Schoffelen, J., Overeem, S., Stegeman, D. F., & Fries, P. (2010) · Journal of Neuroscience · doi:10.1523/jneurosci.2794-09.2010 · Cited by 111 · see citing works
Oscillatory dynamics in the hippocampus support dentate gyrus–CA3 coupling
Akam, T. E., Oren, I., Ritter, L. M., Ferenczi, E., & Kullmann, D. M. (2012) · Nature Neuroscience · doi:10.1038/nn.3081 · Cited by 104 · cited 2× · see citing works
“We note that gamma oscillations in V1 are particularly amenable to experimental phase manipulation as they are readily entrained by flickering visual stimuli [43], as expected given the response dynamics of gamma oscillating networks in vitro [44].”
BackgroundSelf-citationInformation Transfer Between Rhythmically Coupled Networks: Reading the Hippocampal Phase Code
Jensen, O. B. (2001) · Neural Computation · doi:10.1162/089976601317098510 · Cited by 60 · cited 1× · see citing works
“Several studies working in this direction [12,14,52,53] have demonstrated some degree of input selectivity on the basis of modulation, particularly for phase separated inputs [52,53].”
BackgroundThe Dependence of Spike Field Coherence on Expected Intensity
Lepage, K. Q., Kramer, M. A. M., & Eden, U. T. (2011) · Neural Computation · doi:10.1162/neco_a_00169 · Cited by 50 · cited 1× · see citing works
“The widely used measures of spike-field and spike-spike coherence do not map directly onto modulation strength as they are confounded by firing rate [46], which substantially impedes attempts to evaluate modulation strength from much of the published literature.”
BackgroundOptimal computation with attractor networks
Latham, P. E., Denève, S., & Pouget, A. (2003) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2004.01.022 · Cited by 40 · cited 1× · see citing works
“These decoders are, moreover, biologically plausible as their performance corresponds to that of de-noising by networks implementing line attractor dynamics [24,25].”
BackgroundBiased competition through variations in amplitude of γ-oscillations
Zeitler, M., Fries, P., & Gielen, S. C. A. M. (2008) · Journal of Computational Neuroscience · doi:10.1007/s10827-007-0066-2 · Cited by 39 · see citing works
Stimulus Competition by Inhibitory Interference
Tiesinga, P. H. E. (2005) · Neural Computation · doi:10.1162/0899766054796905 · Cited by 32 · cited 1× · see citing works
“Despite extensive experimental [8–10] and computational work [11–14], it remains unclear under what conditions the CTC mechanism could allow a network to distinguish among converging population-coded signals, and how its performance depends on the structure of their oscillatory modulations.”
BackgroundEstablishing communication between neuronal populations through competitive entrainment
Wildie, M. (2012) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2011.00062 · Cited by 20 · cited 1× · see citing works
“Several studies working in this direction [12,14,52,53] have demonstrated some degree of input selectivity on the basis of modulation, particularly for phase separated inputs [52,53].”
BackgroundMasuda, N. (2009) · Neural Computation · doi:10.1162/neco.2009.09-08-857 · Cited by 20 · cited 1× · see citing works
“Several studies working in this direction [12,14,52,53] have demonstrated some degree of input selectivity on the basis of modulation, particularly for phase separated inputs [52,53].”
BackgroundBauer, M., Akam, T. E., Joseph, S., Freeman, E. D., & Driver, J. (2012) · Journal of Vision · doi:10.1167/12.4.5 · Cited by 6 · cited 1× · see citing works
“A recent study found no effect of manipulating the relative phase of gamma frequency flicker between target and distracting stimuli on selective attentional processing [45], although without concurrent electrophysiological data it is unclear how effectively cortical activity was manipulated.”
BackgroundSelf-citation
Flagged first, then by citations
Cited by
Sorted by citations
Rhythms for cognition: communication through coherence
Fries, P. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.034 · Cited by 2,725 · see citing works
“Mathematical implementations of CTC that assume a sinusoidal oscillation and a linear relation between phase and excitability show that presynaptic groups, which are incoherent to the postsynaptic group, might still have a substantial impact (Akam and Kullmann, 2012).”
Lisman, J. E., & Jensen, O. (2013) · Neuron · doi:10.1016/j.neuron.2013.03.007 · Cited by 1,609 · see citing works
“Computational studies suggest that strong coherence is required for selective routing (Akam and Kullmann, 2012).”
Midfrontal conflict-related theta-band power reflects neural oscillations that predict behavior
Cohen, M. X., & Donner, T. H. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00479.2013 · Cited by 494 · see citing works
EEG-Neurofeedback as a Tool to Modulate Cognition and Behavior: A Review Tutorial
Enriquez‐Geppert, S., Huster, R. J., & Herrmann, C. S. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00051 · Cited by 526 · see citing works
“…ranging from single-neuron activity as subthreshold membrane potential oscillations and action potentials, to local activity of assemblies of neurons, and even to activity patterns of whole cortical networks in context of different brain areas (e.g., Akam and Kullmann, 2012; Buzsáki et al., 2013).”
BackgroundNeural Cross-Frequency Coupling: Connecting Architectures, Mechanisms, and Functions
Hyafil, A., Giraud, A., Fontolan, L., & Gutkin, B. S. (2015) · Trends in Neurosciences · doi:10.1016/j.tins.2015.09.001 · Cited by 498 · see citing works
“Does speci fi c long-distance communication improve when local gamma oscillations are locked to global slow oscillations [99]?”
Cerebral hierarchies: predictive processing, precision and the pulvinar
Kanai, R., Komura, Y., Shipp, S., & Friston, K. (2015) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2014.0169 · Cited by 432 · see citing works
Theta Phase Segregation of Input-Specific Gamma Patterns in Entorhinal-Hippocampal Networks
Schomburg, E. W., Fernández-Ruiz, A., Mizuseki, K., Berényi, A., Anastassiou, C. A., Koch, C., & Buzsáki, G. (2014) · Neuron · doi:10.1016/j.neuron.2014.08.051 · Cited by 465 · see citing works
“…interregional communication (Singer, 1993; Fries, 2005), this hypothesis remains hotly debated (Shadlen and Movshon, 1999; Kopell et al., 2000; Fries, 2009; Ray and Maunsell, 2010; Fell and Axmacher, 2011; Bosman et al., 2012; Akam and Kullmann, 2012; Jia et al., 2013; Srinath and Ray, 2014).”
BackgroundThe graphical brain: Belief propagation and active inference
Friston, K. J., Parr, T., & de Vries, B. (2017) · Network Neuroscience · doi:10.1162/netn_a_00018 · Cited by 415 · see citing works
Oscillatory multiplexing of population codes for selective communication in the mammalian brain
Akam, T., & Kullmann, D. M. (2014) · Nature Reviews Neuroscience · doi:10.1038/nrn3668 · Cited by 327 · see citing works
Self-citationTuning pathological brain oscillations with neurofeedback: a systems neuroscience framework
Ros, T., Baars, B. J., Lanius, R. A., & Vuilleumier, P. O. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.01008 · Cited by 263 · see citing works
Do gamma oscillations play a role in cerebral cortex?
Ray, S., & Maunsell, J. H. R. (2014) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2014.12.002 · Cited by 261 · see citing works
Neurosystems: brain rhythms and cognitive processing
Cannon, J. J., McCarthy, M. M., Lee, S., Lee, J. H., Börgers, C., Whittington, M. A., & Kopell, N. (2013) · European Journal of Neuroscience · doi:10.1111/ejn.12453 · Cited by 221 · see citing works
“Another approach to coordination of sender and target was taken by Akam & Kullmann (2012).”
BackgroundBezaire, M. J., Raikov, I., Burk, K., Vyas, D., & Soltesz, I. (2016) · eLife · doi:10.7554/elife.18566 · Cited by 198 · see citing works
Fukunaga, I., Herb, J. T., Köllő, M., Boyden, E. S., & Schaefer, A. T. (2014) · Nature Neuroscience · doi:10.1038/nn.3760 · Cited by 201 · see citing works
Principles of large-scale neural interactions
Vinck, M. A., Uran, C., Spyropoulos, G., Onorato, I., Broggini, A. C., Schneider, M., & Canales‐Johnson, A. (2023) · Neuron · doi:10.1016/j.neuron.2023.03.015 · Cited by 160 · see citing works
The reorganization of corticomuscular coherence during a transition between sensorimotor states
Mehrkanoon, S., Breakspear, M. J., & Boonstra, T. W. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.050 · Cited by 121 · see citing works
“Computational modeling demonstrates that selective communication can indeed be achieved by coherent oscillatory gain modulation, but that the structure of oscillatory activity must satisfy certain constraints (Akam and Kullmann, 2012).”
BackgroundCommunication through Resonance in Spiking Neuronal Networks
Hahn, G., Bujan, A. F., Frégnac, Y., Aertsen, A., & Kumar, A. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003811 · Cited by 91 · see citing works
“Selective gating of signals through network resonance has been suggested by previous theoretical studies [51,67].”
Vinck, M. A., & Bosman, C. A. (2016) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2016.00035 · Cited by 72 · see citing works
“(ii) Later work considered scenarios in which multiple neuronal groups have a gamma-rhythm, and examined how the integration of the synchronous input depends on the ongoing activity of the receiver (Fries, 2005, 2015; Börgers et al., 2008; Gielen et al., 2010; Akam and Kullmann, 2012).”
Control of recollection by slow gamma dominating mid-frequency gamma in hippocampus CA1
Dvořák, D., Radwan, B., Sparks, F. T., Talbot, Z. N., & Fenton, A. A. (2018) · PLoS Biology · doi:10.1371/journal.pbio.2003354 · Cited by 80 · see citing works
Where’s My Consciousness-Ometer? How to Test for the Presence and Complexity of Consciousness
Hunt, T., Ericson, M., & Schooler, J. W. (2022) · Perspectives on Psychological Science · doi:10.1177/17456916211029942 · Cited by 68 · see citing works
“Gamma synchrony has been reported both in subcortical structures (Akam & Kullmann, 2012; Steriade et al., 1993; Zhou et al., 2016) and in cortical areas (Fries, 2015; Gray & Singer, 1989; Gregoriou et al., 2009).”
BackgroundDumont, G., & Gutkin, B. S. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007019 · Cited by 63 · see citing works
Intra- and Inter-Frequency Brain Network Structure in Health and Schizophrenia
Siebenhühner, F., Weiss, S. A., Coppola, R., Weinberger, D. R., & Bassett, D. S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0072351 · Cited by 62 · see citing works
“neurophysiological network over which the brain processes information during cognitive processes [1,2].”
BackgroundSchalk, G. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00352 · Cited by 61 · see citing works
“CTC has received support from modeling studies (Akam and Kullmann, 2010, 2012) and experimental results (Saalmann et al., 2012; Roberts et al., 2013).”
BackgroundMethodGamma rhythms in the visual cortex: functions and mechanisms
Han, C., Shapley, R. M., & Xing, D. (2021) · Cognitive Neurodynamics · doi:10.1007/s11571-021-09767-x · Cited by 61 · see citing works
“…depends on the relative gamma phase (zero phase) between the spikes and the LFP. Similar to the situation with the BBS hypothesis, the CTC hypothesis was both supported (Womelsdorf et al. 2007; Gregoriou et al. 2009) and opposed (Jia et al. 2013a, Akam and Kullmann 2012).”
ResultPredictive coding of natural images by V1 firing rates and rhythmic synchronization
Uran, C., Peter, A., Lazăr, A., Barnes, W., Klon-Lipok, J., Shapcott, K. A., Roese, R., Fries, P., Singer, W., & Vinck, M. A. (2022) · Neuron · doi:10.1016/j.neuron.2022.01.002 · Cited by 64 · see citing works
“Given such variability, it remains an open problem how attention can be generically mediated via selective interareal synchronization, and how stimuli with weak g-synchronization are communicated to higher areas (for further discussion see Hermes et al. 2015; Ray and Maunsell 2015; Brunet et al. 2014; Akam and Kullman…”
BackgroundMidfrontal theta tracks action monitoring over multiple interactive time scales
Cohen, M. X. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.07.054 · Cited by 56 · see citing works
“Among other functions, oscillations are thought to provide a temporal structure for local and long-range neural communication in the brain (Akam and Kullmann, 2012; Buzsáki and Moser, 2013; Fries, 2005).”
BackgroundRoy, D., Sigala, R., Breakspear, M. J., McIntosh, A. R., Jirsa, V., Deco, G., & Ritter, P. (2014) · Brain Connectivity · doi:10.1089/brain.2014.0252 · Cited by 48 · see citing works
“Recent computational studies further suggest that selective communication is achieved by coherence mechanisms between gain (signal to noise ratio, amplitude scaling) and firing rate modulations (Akam and Kullmann, 2012).”
McLelland, D., & Rufin Vanrullen, A. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005162 · Cited by 47 · see citing works
Spontaneous variability in gamma dynamics described by a damped harmonic oscillator driven by noise
Spyropoulos, G., Saponati, M., Dowdall, J. R., Schölvinck, M. L., Bosman, C. A., Lima, B., Peter, A., Onorato, I., Klon-Lipok, J., Roese, R., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29674-x · Cited by 44 · see citing works
“(5) Longer gamma cycles were associated with stronger spikefield phase-locking (synchrony), but lower firing-rates, and were not accompanied by stronger, transient spiking activation.”
BackgroundDynamic Control of Synchronous Activity in Networks of Spiking Neurons
Hutt, A., Mierau, A., & Lefebvre, J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0161488 · Cited by 44 · see citing works
Marshall, L., & Binder, S. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00614 · Cited by 42 · see citing works
“Frontiers in Human Neuroscience www.frontiersin.org September 2013 | Volume 7 | Article 614 | 2 as the interplay between intrinsic properties of the neuron and its inputs, are matters of ongoing research (Akam and Kullmann, 2012).”
BackgroundTime-course of coherence in the human basal ganglia during voluntary movements
Talakoub, O., Neagu, B., Udupa, K., Tsang, E., Chen, R. E., Popović, M. R., & Wong, W. (2016) · Scientific Reports · doi:10.1038/srep34930 · Cited by 35 · see citing works
Thalamocortical architectures for flexible cognition and efficient learning
Scott, D. N., Mukherjee, A., Nassar, M. R., & Halassa, M. M. (2024) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2024.05.006 · Cited by 35 · see citing works
Voloh, B., Oemisch, M., & Womelsdorf, T. (2020) · Nature Communications · doi:10.1038/s41467-020-18435-3 · Cited by 35 · see citing works
Distinct feedforward and feedback pathways for cell-type specific attention effects
Spyropoulos, G., Schneider, M., van Kempen, J., Gieselmann, M. A., Thiele, A., & Vinck, M. A. (2024) · Neuron · doi:10.1016/j.neuron.2024.04.020 · Cited by 31 · see citing works
Learned control of inter-hemispheric connectivity: Effects on bimanual motor performance
Kajal, D. S., Braun, C., Mellinger, J., Sacchet, M. D., Ruíz, S., Fetz, E. E., Birbaumer, N., & Sitaram, R. (2017) · Human Brain Mapping · doi:10.1002/hbm.23663 · Cited by 29 · see citing works
“As a metric of functional coupling, correlations of amplitudes, as well as parameters measuring the extent of phase-locked activity have been introduced [Akam and Kullmann, 2012; Bastos et al., 2015; Fries, 2005, 2015].”
Neural Oscillations in Aversively Motivated Behavior
Totty, M. S., & Maren, S. (2022) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2022.936036 · Cited by 26 · see citing works
Regulation of rhythm genesis by volume-limited, astroglia-like signals in neural networks
Savtchenko, L. P., & Rusakov, D. A. (2014) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2013.0614 · Cited by 24 · see citing works
“Synchronized oscillations in neural circuits could provide, at least theoretically, a universal device for pattern recognition and information handling in the brain [1,2].”
BackgroundGriffiths, J. D., McIntosh, A. R., & Lefebvre, J. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.575143 · Cited by 22 · see citing works
“Understanding the 60 mechanisms mediating the coexistence of these rhythms, as well as state-dependent changes 61 in their properties, would yield important insight about how collective neural activity and 62 synchronization phenomena, shaped by both sensory and recurrent inputs, mediate neural 63 communication[3].”
BackgroundReyner-Parra, D., & Huguet, G. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009342 · Cited by 22 · see citing works
Phase Difference between Model Cortical Areas Determines Level of Information Transfer
Wal, M. T., & Tiesinga, P. H. E. (2017) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2017.00006 · Cited by 20 · see citing works
“Models have also identified some limits and requirements for an synchrony-based dynamic communication structure (Akam and Kullmann, 2012; Rolls et al., 2012).”
BackgroundHan, H.-B., Shin, H., Jeong, Y., Kim, J., & Choi, J. H. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2308762120 · Cited by 18 · see citing works
“These observations exemplify the communicationthroughcoherence theory (15, 16) and explain the selective neural processing via phasematching oscillation between the mPFC and BLA during freezing.”
BackgroundA general theory of coherence between brain areasPreprint
Schneider, M., Dann, B., Sheshadri, S., Scherberger, H., & Vinck, M. A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.17.156190 · Cited by 19 · see citing works
Spontaneous variability in gamma dynamics described by a linear harmonic oscillator driven by noisePreprint
Spyropoulos, G., Dowdall, J. R., Schölvinck, M. L., Bosman, C. A., Lima, B., Peter, A., Onorato, I., Klon-Lipok, J., Roese, R., Neuenschwander, S., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/793729 · Cited by 18 · see citing works
“When multiple input populations converge onto a single post-synaptic target, selective gamma synchronization can lead to selective information transmission (Akam and Kullmann, 2012; Börgers et al., 2008).”
BackgroundPittman-Polletta, B., Wang, Y., Stanley, D. A., Schroeder, C. E., Whittington, M. A., & Kopell, N. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008783 · Cited by 18 · see citing works
Spike–Gamma Phase Relationship in the Visual Cortex
Ray, S. (2022) · Annual Review of Vision Science · doi:10.1146/annurev-vision-100419-104530 · Cited by 16 · see citing works
Potential Mechanisms Underlying Intercortical Signal Regulation via Cholinergic Neuromodulators
Lee, J. H., Whittington, M. A., & Kopell, N. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.0629-15.2015 · Cited by 16 · see citing works
“Brain rhythms are believed to be involved in the flow of signals around the cortex (Fries, 2005, 2009; Buschman and Miller, 2007; Saalmann et al., 2007; Pesaran et al., 2008; Wang, 2010; Akam and Kullmann, 2012; Bosman et al., 2012).”
BackgroundDisruption of large-scale electrophysiological networks in stroke patients with visuospatial neglect
Ros, T., Michela, A., Mayer, A., Bellmann, A., Vuadens, P., Zermatten, V., Saj̈, A., & Vuilleumier, P. O. (2021) · Network Neuroscience · doi:10.1162/netn_a_00210 · Cited by 31 · see citing works
Targeted V1 comodulation supports task-adaptive sensory decisions
Haimerl, C., Ruff, D. A., Cohen, M. R., Savin, C., & Simoncelli, E. P. (2023) · Nature Communications · doi:10.1038/s41467-023-43432-7 · Cited by 16 · see citing works
Attentional effects on local V1 microcircuits explain selective V1-V4 communication
Katsanevaki, C., Bastos, A. M., Cagnan, H., Bosman, C. A., Friston, K. J., & Fries, P. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120375 · Cited by 15 · see citing works
Broadband synergy versus oscillatory redundancy in the visual cortex
Roberts, L., Äijälä, J., Burger, F., Uran, C., Jensen, M. A., Miller, K. J., Ince, R. A. A., Vinck, M. A., Hermes, D., & Canales‐Johnson, A. (2026) · Nature Communications · doi:10.1038/s41467-026-72444-2 · Cited by 14 · see citing works
“In fact, modeling studies investigating selective communication via coherence (Communication-through-Coherence; CTC) have focused on scenarios where information is relayed between areas (Akam and Kullmann, 2012).”
Predictive coding in the human olfactory system
Lyons, S. H., & Gottfried, J. A. (2025) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2025.04.005 · Cited by 10 · see citing works
Predictive coding of natural images by V1 activity revealed by self-supervised deep neural networksPreprint
Uran, C., Peter, A., Lazăr, A., Barnes, W., Klon-Lipok, J., Shapcott, K. A., Roese, R., Fries, P., Singer, W., & Vinck, M. A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.10.242958 · Cited by 10 · see citing works
“…are di ffi cult to reconcile with the putative roles of γ -oscillations in attention and feedfor-ward inter-areal communication (Hermes et al., 2015; Akam and Kullmann, 2012; Ray and Maunsell, 2015): (1) γ - and β -activity is induced only by a subset of images and highly stimulus-dependent (Ray…”
BackgroundMeyerolbersleben, L. S., Sirota, A., & Busse, L. (2025) · Neuron · doi:10.1016/j.neuron.2025.03.030 · Cited by 9 · see citing works
Rodents’ visual gamma as a biomarker of pathological neural conditions
Meneghetti, N., Vannini, E., & Mazzoni, A. (2024) · The Journal of Physiology · doi:10.1113/jp283858 · Cited by 8 · see citing works
“Communication through coherence is possible also if only synchronized rhythmic activity is present in an upstream region, as this entrains the receiver one through its fluctuating input flow (Akam & Kullmann, 2012).”
BackgroundOscillations in an artificial neural network convert competing inputs into a temporal code
Duecker, K., Idiart, M., van Gerven, M. A. J., & Jensen, O. B. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012429 · Cited by 8 · see citing works
“The communication through coherence (CTC) theory predicts that the phase relationship between two populations is critical for their communication [82, 83] (also see [84, 85] for computational implementations).”
Malleability of gamma rhythms enhances population-level correlations
Saraf, S., & Young, L.-S. (2021) · Journal of Computational Neuroscience · doi:10.1007/s10827-021-00779-4 · Cited by 8 · see citing works
Flexible and accurate decoding of neural populations through stochastic comodulationPreprint
Haimerl, C., Savin, C., & Simoncelli, E. P. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/624387 · Cited by 8 · see citing works
“The “communication through coherence” theory (Akam and Kullmann, 2012) refines this idea in an encoding-decoding framework, in which a top-down oscillatory signal projects to both encoding neurons with the same feature selectivity, and to the decoding network that needs to read out from them.”
BackgroundGolden rhythms as a theoretical framework for cross-frequency organization
Kramer, M. (2022) · Neurons Behavior Data analysis and Theory · doi:10.51628/001c.38960 · Cited by 7 · see citing works
“Like the communicationthrough-coherence (CTC) hypothesis [16, 26] and the frequency-division multiplexing hypothesis [12, 78], in the framework proposed here neural populations communicate dynamically along anatomical connections via coordinated rhythms.”
BackgroundPittman-Polletta, B., Wang, Y., Stanley, D. A., Schroeder, C. E., Whittington, M. A., & Kopell, N. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.01.11.902858 · Cited by 7 · see citing works
Krishnakumaran, R., & Ray, S. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhad270 · Cited by 6 · see citing works
Evaluating interhemispheric connectivity during midline object recognition using EEG
Das, A., Mandel, A., Shitara, H., Popa, T., Horovitz, S. G., Hallett, M., & Thirugnanasambandam, N. (2022) · PLoS ONE · doi:10.1371/journal.pone.0270949 · Cited by 5 · see citing works
Johnson, T. D., Keefe, K. R., & Rangel, L. M. (2023) · Hippocampus · doi:10.1002/hipo.23490 · Cited by 5 · see citing works
Neurophysiological correlates of purchase decision-making
Díez, I., & Marco‐Pallarés, J. (2021) · Biological Psychology · doi:10.1016/j.biopsycho.2021.108060 · Cited by 5 · see citing works
Flexible information routing in neural populations through stochastic comodulation
Haimerl, C., Savin, C., & Simoncelli, E. P. (2019) · neural information processing systems · Cited by 6 · see citing works
Uran, C., Peter, A., Lazăr, A., Barnes, W., Klon-Lipok, J., Shapcott, K. A., Roese, R., Fries, P., Singer, W., & Vinck, M. A. (2020) · MPG.PuRe (Max Planck Society) · Cited by 5 · see citing works
The Complex Behaviour of a Simple Neural Oscillator Model in the Human Cortex
Cejnar, P., Vyšata, O., Vališ, M., & Procházka, A. (2018) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2018.2883618 · Cited by 4 · see citing works
“The mechanisms of identical or generalized synchronization then could probably explain reasoning in such networks (see for example [44], [45]) followed by learning methods similar to any weight modification method like spike timing dependent plasticity (STDP) [46] succesfully used in artificial SNN models for pattern…”
MethodErmolova, M., Kozák, G., Belardinelli, P., & Ziemann, U. (2025) · Scientific Reports · doi:10.1038/s41598-025-20294-1 · Cited by 3 · see citing works
“In addition, we analyzed whether changes in the preferred phase lag, rather than changes in phase lag stability, reflect phase-dependent modulation of functional connectivity 19,20 .”
Optogenetic Mapping of Rhythmic Phase-Dependent Excitability in the Mouse Striatum
Mohapatra, M., Carmichael, J. E., Smith, K. S., & van der Meer, M. A. A. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.0218-24.2025 · Cited by 2 · see citing works
Cannon, J. J. (2016) · Neural Computation · doi:10.1162/neco_a_00915 · Cited by 2 · see citing works
Disruption of large-scale electrophysiological networks in stroke patients with visuospatial neglectPreprint
Ros, T., Michela, A., Meyer, A., Bellman, A., Vuadens, P., Zermatten, V., Saj̈, A., & Vuilleumier, P. O. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/602318 · Cited by 2 · see citing works
Control of recollection by slow gamma dominating mid-frequency gamma in hippocampus CA1Preprint
Dvorak, D., Radwan, B., Sparks, F. T., Talbot, Z. N., & Fenton, A. A. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/152488 · Cited by 1 · see citing works
Neural oscillatory dynamics of spoken word recognition
Strauß, A. (2015) · Cited by 2 · see citing works
Pulvinar Structure, Circuitry & Function in Primates☆
Shipp, S. D. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-801238-3.04740-1 · Cited by 1 · see citing works
Mondal, Y., Benito, G. V., Pena, R. F. O., & Rotstein, H. G. (2025) · Journal of Computational Neuroscience · doi:10.1007/s10827-025-00908-3 · Cited by 1 · see citing works
“Neuronal filters allow neuronal systems to select certain information or enhance the communication of specific information components over others (Hutcheon & Yarom, 2000; Tsodyks et al., 2000; Izhikevich et al., 2003; Stark et al., 2022 ; Laudansky et al., 2014; Akam & Kullmann, 2012 ).”
Meyerolbersleben, L. S., Sirota, A., & Busse, L. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.21.608936 · Cited by 1 · see citing works
“Indeed, modeling has revealed that population inputs containing oscillations can be selectively transmitted even in the presence of multiple distracting signals with comparable firing rates (Akam & Kullmann, 2012, 2014, 2010).”
BackgroundBel, A., & Rotstein, H. G. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.09.664007 · Cited by 1 · see citing works
Bel, A., Chialva, U., & Rotstein, H. G. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.12.675827 · Cited by 1 · see citing works
López‐Madrona, V. J., Giroud, J., Mercier, M. R., Lancia, L., Giordano, B. L., Trébuchon, A., Poeppel, D., Giraud, A., Arnal, L. H., & Morillon, B. (2025) · eLife · doi:10.1101/2025.10.22.683926 · Cited by 2 · see citing works
Siems, M., Cao, Y., Donner, T. H., Tsetsos, K., & Engel, A. K. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.11.21.689181 · Cited by 1 · see citing works
“Computational models have highlighted strong oscillatory activity as a prerequisite for this phase-alignment 10 .”
Oscillations in routing and chaos
Palmigiano, A. (2017) · doi:10.53846/goediss-6513
Data-Driven Modeling of Normal and Pathological Oscillations in the Hippocampus
Raikov, I., & Soltész, I. (2019) · Springer series in cognitive and neural systems · doi:10.1007/978-3-030-18830-6_17
Kramer, M. (2021) · arXiv (Cornell University)
Attentional effects on local V1 microcircuits explain selective V1-V4 communicationPreprint
Katsanevaki, C., Bastos, A. M., Cagnan, H., Bosman, C. A., Friston, K. J., & Fries, P. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.14.484223
“…and one of them is oscillatory, as long as the receiver is implemented at least as a simple spiking network with a single feed-forward interneuron layer (Akam and Kullmann, 2010), whereas filtering is much less effective when the receiver is less biologically realistic (Akam and Kullmann, 2012).”
Selective V1-to-V4 Communication of Attended Stimuli Mediated by Attentional Effects in V1Preprint
Katsanevaki, C., Bastos, A. M., Cagnan, H., Bosman, C. A., Friston, K. J., & Fries, P. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4060368
“…and one of them is oscillatory, as long as the receiver is implemented at least as a simple spiking network with a single feed-forward interneuron layer (Akam and Kullmann, 2010), whereas filtering is much less effective when the receiver is less biologically realistic (Akam and Kullmann, 2012).”
BackgroundHan, H.-B., Shin, H., Jeong, Y., Kim, J., & Choi, J. H. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.04.510912
“These observations exemplify the communication-through-coherence theory (Akam and Kullmann, 2012; Fries, 2015) and explain the selective neural processing via phase-matching oscillation between the mPFC and BLA during freezing.”
BackgroundCausal modulation of cortical amplitude coupling through dual-site amplitude-modulated tACSPreprint
Fiene, M., Siems, M., Kammerer, T., Schneider, T. R., & Engel, A. K. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.14.718451
“…amplitude coupling is thought to index more stable, large-scale neural communication across distributed brain networks, thereby shaping and constraining faster, phase-based computations (Akam & Kullmann, 2012; Engel et al., 2013; Hipp et al., 2011; Lewis et al., 2016; Siegel et al., 2012).”
Spectral dependence as a framework for neural coordination
Besosa, C., Qin, Y., Burke, S. N., & Maurer, A. P. (2026) · Current Research in Neurobiology · doi:10.1016/j.crneur.2026.100159
“This interpretation provides a putative mechanism for selective communication, in which synchronized pathways are rendered effective while non-coherent inputs, arriving out of sync with more excitable phases, are comparatively attenuated (Akam and Kullmann, 2012).”
Bel, A., Chialva, U., & Rotstein, H. G. (2026) · Biological Cybernetics · doi:10.1007/s00422-026-01047-3
“Neuronal filters describe the information processing building blocks where specific frequency components of the output are enhanced over others (Hutcheon and Yarom 2000; Tsodyks et al. 2000; Izhikevich et al. 2003; Stark et al. 2022; Laudansky et al. 2014; Akam and Kullmann 2012).”
- Authors
- Akam, T. E., & Kullmann, D. M.
- Affiliations
- UCL Queen Square Institute of Neurology (GB)
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Akam, Thomas E.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-11-08
- Volume
- 8
- Issue
- 11
- Pages
- e1002760
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC3493486
- OpenAlex
- W2033965963
- Cited by
- 93
- References
- 55
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 5 · 2023: 7 · 2022: 11 · 2021: 7 · 2020: 6 · 2019: 6 · 2018: 3 · 2017: 6 · 2016: 8 · 2015: 7 · 2014: 9 · 2013: 5
- FWCI
- 3.44
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- Wellcome Trust (095580)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Keywords
- neural oscillations, selective communication, population coding, distractor interference, signal-to-noise ratio, neural information transfer
- MeSH terms
- Animals, Brain, Cell Communication, Computer Simulation, Mammals, Models, Neurological, Nerve Net, Neurons, Computational Biology
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Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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