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The Cerebral Cortex: A Delay-Coupled Recurrent Oscillator Network?

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Reservoir Computing

Part of the book series: Natural Computing Series ((NCS))

Abstract

The refinement of machine learning strategies and deep convolutional networks led to the development of artificial systems whose functions resemble those of natural brains, suggesting that the two systems share the same computational principles. In this chapter, evidence is reviewed which indicates that the computational operations of natural systems differ in some important aspects from those implemented in artificial systems. Natural processing architectures are characterized by recurrence and therefore exhibit high-dimensional, non-linear dynamics. Moreover, they use learning mechanisms that support self-organization. It is proposed that these properties allow for computations that are notoriously difficult to realize in artificial systems. Experimental evidence on the organization and function of the cerebral cortex is reviewed that supports this proposal.

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References

  • M. Abeles, Corticonics (Cambridge University Press, Cambridge, 1991)

    Book  Google Scholar 

  • D.G. Aronson, G.B. Ermentrout, N. Kopell, Amplitude response of coupled oscillators. Phys. D 41, 403–449 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  • A. Artola, S. Bröcher, W. Singer, Different voltage-dependent thresholds for the induction of long-term depression and long-term potentiation in slices of the rat visual cortex. Nature 347, 69–72 (1990)

    Article  Google Scholar 

  • B.V. Atallah, M. Scanziani, Instantaneous modulation of gamma oscillation frequency by balancing excitation with inhibition. Neuron 62, 566–577 (2009)

    Article  Google Scholar 

  • B.B. Averbeck, P.E. Latham, A. Pouget, Neural correlations, population coding and computation. Nat. Rev. Neurosci. 7, 358–366 (2006)

    Article  Google Scholar 

  • N. Axmacher, D.P. Schmitz, T. Wagner, C.E. Elger, J. Fell, Interactions between medial temporal lobe, prefrontal cortex, and inferior temporal regions during visual working memory: a combined intracranial EEG and functional magnetic resonance imaging study. J. Neurosci. 28, 7304–7312 (2008)

    Article  Google Scholar 

  • M. Bányai, A. Lazar, L. Klein, J. Klon-Lipok, M. Stippinger, W. Singer, G. Orbán, Stimulus complexity shapes response correlations in primary visual cortex. Proc. Natl. Acad. Sci. U.S.A. 116, 2723–2732 (2019)

    Article  Google Scholar 

  • H.B. Barlow, Single units and sensation: a neurone doctrine for perceptual psychology? Perception 1, 371–394 (1972)

    Article  Google Scholar 

  • A.M. Bastos, J. Vezoli, C.A. Bosman, J.-M. Schoffelen, R. Oostenveld, J.R. Dowdall, P. De Weerd, H. Kennedy, P. Fries, Visual areas exert feedforward and feedback influences through distinct frequency channels. Neuron 85, 390–401 (2015)

    Article  Google Scholar 

  • M.F. Bear, W. Singer, Modulation of visual cortical plasticity by acetylcholine and noradrenaline. Nature 320, 172–176 (1986)

    Article  Google Scholar 

  • G. Bellec, F. Scherr, E. Hajek, D. Salaj, R. Legenstein, W. Maass, Biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets (2019), https://arxiv.org/abs/2553450:1-34

  • P. Berkes, G. Orbán, M. Lengyel, J. Fiser, Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment. Science 331, 83–87 (2011)

    Article  Google Scholar 

  • G.Q. Bi, M.M. Poo, Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neurosci. 18, 10464–10472 (1998)

    Article  Google Scholar 

  • E.L. Bienenstock, L.N. Cooper, P.W. Munro, Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex. J. Neurosci. 2, 32–48 (1982)

    Article  Google Scholar 

  • C. Börgers, N.J. Kopell, Gamma oscillations and stimulus selection. Neural Comput. 20, 383–414 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  • W.H. Bosking, Y. Zhang, B. Schofield, D. Fitzpatrick, Orientation selectivity and the arrangement of horizontal connections in tree shrew striate cortex. J. Neurosci. 17, 2112–2127 (1997)

    Article  Google Scholar 

  • C.A. Bosman, T. Womelsdorf, R. Desimone, P. Fries, A microsaccadic rhythm modulates gamma-band synchronization and behavior. J. Neurosci. 29, 9471–9480 (2009)

    Article  Google Scholar 

  • N. Brunet, C. Bosman, M. Roberts, R. Oostenveld, T. Womelsdorf, P. De Weerd, P. Fries, Visual cortical gamma-band activity during free viewing of natural images. Cereb. Cortex 25, 918–926 (2015)

    Article  Google Scholar 

  • R.M. Bruno, B. Sakmann, Cortex is driven by weak but synchronously active thalamocortical synapses. Science 312, 1622–1627 (2006)

    Article  Google Scholar 

  • D.V. Buonomano, W. Maass, State-dependent computations: spatiotemporal processing in cortical networks. Nat. Rev. Neurosci. 10, 113–125 (2009)

    Article  Google Scholar 

  • G. Buracas, A. Zador, M. Deweese, T. Albright, Efficient discrimination of temporal patterns by motion-sensitive neurons in primate visual cortex. Neuron 20, 959–969 (1998)

    Article  Google Scholar 

  • S.P. Burns, D. Xing, M.J. Shelley, R.M. Shapley, Searching for autocoherence in the cortical network with a time-frequency analysis of the local field potential. J. Neurosci. 30, 4033–4047 (2010)

    Article  Google Scholar 

  • S.P. Burns, D. Xing, R.M. Shapley, Is gamma-band activity in the local field potential of V1 cortex a “clock” or filtered noise? J. Neurosci. 31, 9658–9664 (2011)

    Article  Google Scholar 

  • T.J. Buschman, E.K. Miller, Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science 315, 1860–1862 (2007)

    Article  Google Scholar 

  • G. Buzsáki, X.-J. Wang, Mechanisms of gamma oscillations. Annu. Rev. Neurosci. 35, 203–225 (2012)

    Article  Google Scholar 

  • G. Buzsáki, N. Logothetis, W. Singer, Scaling brain size, keeping timing: evolutionary preservation of brain rhythms. Neuron 80, 751–764 (2013)

    Article  Google Scholar 

  • M.M. Churchland et al., Stimulus onset quenches neural variability: a widespread cortical phenomenon. Nat. Neurosci. 13, 369–378 (2010)

    Article  Google Scholar 

  • O. D’Huys, I. Fischer, J. Danckaert, R. Vicente, Spectral and correlation properties of rings of delay-coupled elements: Comparing linear and nonlinear systems. Phys. Rev. E 85(056209), 1–5 (2012)

    Google Scholar 

  • K. Diba, G. Buzsáki, Forward and reverse hippocampal place-cell sequences during ripples. Nat. Neurosci. 10, 1241–1242 (2007)

    Article  Google Scholar 

  • J.J. DiCarlo, D.D. Cox, Untangling invariant object recognition. Trends Cogn. Sci. 11, 333–341 (2007)

    Article  Google Scholar 

  • M. Diesmann, M.-O. Gewaltig, A. Aertsen, Stable propagation of synchronous spiking in cortical neural networks. Nature 402, 529–533 (1999)

    Article  Google Scholar 

  • V. Ego-Stengel, M.A. Wilson, Disruption of ripple-associated hippocampal activity during rest impairs spatial learning in the rat. Hippocampus 20, 1–10 (2010)

    Google Scholar 

  • G.B. Ermentrout, D. Kleinfeld, Traveling electrical waves in cortex: insights from phase dynamics and speculation on a computational role. Neuron 29, 33–44 (2001)

    Article  Google Scholar 

  • J. Fell, E. Ludowig, B.P. Staresina, T. Wagner, T. Kranz, C.E. Elger, N. Axmacher, Medial temporal theta/alpha power enhancement precedes successful memory encoding: Evidence based on intracranial EEG. J. Neurosci. 31, 5392–5397 (2011)

    Article  Google Scholar 

  • D.J. Felleman, D.C. van Essen, Distributed hierarchical processing in the primate cerebral cortex. Cereb. Cortex 1, 1–47 (1991)

    Article  Google Scholar 

  • U. Frey, R.G.M. Morris, Synaptic tagging and long-term potentiation. Nature 385, 533–536 (1997)

    Article  Google Scholar 

  • P. Fries, A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends Cogn. Sci. 9, 474–480 (2005)

    Article  Google Scholar 

  • P. Fries, J.H. Reynolds, A.E. Rorie, R. Desimone, Modulation of oscillatory neuronal synchronization by selective visual attention. Science 291, 1560–1563 (2001a)

    Article  Google Scholar 

  • P. Fries, S. Neuenschwander, A.K. Engel, R. Goebel, W. Singer, Rapid feature selective neuronal synchronization through correlated latency shifting. Nat. Neurosci. 4, 194–200 (2001b)

    Article  Google Scholar 

  • P. Fries, D. Nikolic, W. Singer, The gamma cycle. Trends Neurosci. 30, 309–316 (2007)

    Article  Google Scholar 

  • R.A.W. Galuske, M.H.J. Munk, W. Singer, Relation between gamma oscillations and neuronal plasticity in the visual cortex. Proc. Natl. Acad. Sci. USA, 116, 23317–23375

    Google Scholar 

  • C.D. Gilbert, T.N. Wiesel, Columnar specificity of intrinsic horizontal and corticocortical connections in cat visual cortex. J. Neurosci. 9, 2432–2442 (1989)

    Article  Google Scholar 

  • L. Glass, J. Sun, Periodic forcing of a limit-cycle oscillator: fixed points, Arnold tongues, and the global organization of bifurcations. Phys. Rev. E 50(6), 5077–5084 (1994)

    Article  Google Scholar 

  • M.F. Glasser, T.S. Coalson, E.C. Robinson, C.D. Hacker, J. Harwell, E. Yacoub, K. Ugurbil, J. Andersson, C.F. Beckmann, M. Jenkinson, S.M. Smith, D.C. Van Essen, A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016)

    Article  Google Scholar 

  • C.M. Gray, W. Singer, Stimulus-specific neuronal oscillations in orientation columns of cat visual cortex. Proc. Natl. Acad. Sci. U.S.A. 86, 1698–1702 (1989)

    Article  Google Scholar 

  • C.M. Gray, P. König, A.K. Engel, W. Singer, Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature 338, 334–337 (1989)

    Article  Google Scholar 

  • C.G. Gross, C.E. Rocha-Miranda, D.B. Bender, Visual properties of neurons in inferotemporal cortex of the macaque. J. Neurophysiol. 35, 96–111 (1972)

    Article  Google Scholar 

  • G. Hahn, T. Petermann, M.N. Havenith, Y. Yu, W. Singer, D. Plenz, D. Nikolic, Neuronal avalanches in spontaneous activity in vivo. J. Neurophysiol. 104, 3312–3322 (2010)

    Article  Google Scholar 

  • C. Hartmann, A. Lazar, B. Nessler, J. Triesch, Where’s the noise? Key features of spontaneous activity and neural variability arise through learning in a deterministic network. PLoS Comput. Biol. 11(12), e1004640, 1–35 (2015)

    Google Scholar 

  • D.O. Hebb, The Organization of Behavior (Wiley, New York, 1949)

    Google Scholar 

  • T. Hirabayashi, D. Takeuchi, K. Tamura, Y. Miyashita, Microcircuits for hierarchical elaboration of object coding across primate temporal areas. Science 341, 191–195 (2013)

    Article  Google Scholar 

  • S. Hochreiter, J. Schmidhuber, Long short-term memory. Neural Comput. 9, 1735–1780 (1997)

    Article  Google Scholar 

  • J.J. Hopfield, Learning algorithms and probability distributions in feed-forward and feed-back networks. Proc. Natl. Acad. Sci. U.S.A. 84, 8429–8433 (1987)

    Article  MathSciNet  Google Scholar 

  • D.H. Hubel, T.N. Wiesel, Receptive fields and functional architecture of monkey striate cortex. J. Physiol. (Lond.) 195, 215–243 (1968)

    Article  Google Scholar 

  • M.F. Iacaruso, I.T. Gasler, S.B. Hofer, Synaptic organization of visual space in primary visual cortex. Nature 547, 449–452 (2017)

    Article  Google Scholar 

  • J. Ito, P. Maldonado, W. Singer, S. Grün, Saccade-related modulations of neuronal excitability support synchrony of visually elicited spikes. Cereb. Cortex 21, 2482–2497 (2011)

    Article  Google Scholar 

  • X. Jia, D. Xing, A. Kohn, No consistent relationship between gamma power and peak frequency in macaque primary visual cortex. J. Neurosci. 33, 17–25 (2013a)

    Article  Google Scholar 

  • X. Jia, S. Tanabe, A. Kohn, Gamma and the coordination of spiking activity in early visual cortex. Neuron 77, 762–774 (2013b)

    Article  Google Scholar 

  • K. Kar, J. Kubilius, K. Schmidt, E.B. Issa, J.J. DiCarlo, Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior, 24 June 2018 (2018). http://dx.doi.org/10.1101/354753:1-20

  • T. Kenet, D. Bibitchkov, M. Tsodyks, A. Grinvald, A. Arieli, Spontaneously emerging cortical representations of visual attributes. Nature 425, 954–956 (2003)

    Article  Google Scholar 

  • C. Korndörfer, E. Ullner, J. Garcia, G. Pipa, Cortical spike synchrony as measure of input familiarity. Neural Comput. 29, 2491–2510 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  • B. Kundu, D.W. Sutterer, S.M. Emrich, B.R. Postle, Strengthened effective connectivity underlies transfer of working memory training to tests of short-term memory and attention. J. Neurosci. 33, 8705–8715 (2013)

    Article  Google Scholar 

  • Y. Kuramoto, Collective synchronization of pulse-coupled oscillators and excitable units. Phys. D 50, 15–30 (1990)

    Article  MATH  Google Scholar 

  • A.N. Landau, Neuroscience: a mechanism for rhythmic sampling in vision. Curr. Biol. 28, R830–R832 (2018)

    Article  Google Scholar 

  • G. Laurent, Dynamical representation of odors by oscillating and evolving neural assemblies. Trends Neurosci. 19, 489–496 (1996)

    Article  Google Scholar 

  • A. Lazar, G. Pipa, J. Triesch, SORN: a self-organizing recurrent neural network. Front. Comput. Neurosci. 3(23), 1–9 (2009)

    Google Scholar 

  • A. Lazar, C. Lewis, P. Fries, W. Singer, D. Nikolic, Visual exposure optimizes stimulus encoding in primary visual cortex, 20 December 2018 (2018). http://dx.doi.org/10.1101/502328:1-24

  • Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521, 436–444 (2015)

    Article  Google Scholar 

  • C.M. Lewis, A. Baldassarre, G. Committeri, G.L. Romani, M. Corbetta, Learning sculpts the spontaneous activity of the resting human brain. Proc. Natl. Acad. Sci. U.S.A. 106, 17558–17563 (2009)

    Article  Google Scholar 

  • B. Lima, W. Singer, N.-H. Chen, S. Neuenschwander, Synchronization dynamics in response to plaid stimuli in monkey V1. Cereb. Cortex 20, 1556–1573 (2010)

    Article  Google Scholar 

  • B. Lima, W. Singer, S. Neuenschwander, Gamma responses correlate with temporal expectation in monkey primary visual cortex. J. Neurosci. 31, 15919–15931 (2011)

    Article  Google Scholar 

  • S. Löwel, W. Singer, Selection of intrinsic horizontal connections in the visual cortex by correlated neuronal activity. Science 255, 209–212 (1992)

    Article  Google Scholar 

  • E. Lowet, M.J. Roberts, C.A. Bosman, P. Fries, P. De Weerd, Areas V1 and V2 show microsaccade-related 3–4-Hz covariation in gamma power and frequency. Eur. J. Neurosci. 43, 1286–1296 (2016)

    Article  Google Scholar 

  • E. Lowet, M.J. Roberts, B. Gips, P. De Weerd, A. Peter, A quantitative theory of gamma synchronization in macaque V1. eLife 6, e26642, 1–44 (2017). elifesciencesorg

    Google Scholar 

  • M. Lukoševičius, H. Jaeger, Reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 3, 127–149 (2009)

    Article  MATH  Google Scholar 

  • M. Lundqvist, J. Rose, P. Herman, S.L. Brincat, T.J. Buschman, E.K. Miller, Gamma and beta bursts underlie working memory. Neuron 90, 152–164 (2016)

    Article  Google Scholar 

  • Z.F. Mainen, T.J. Sejnowski, Reliability of spike timing in neocortical neurons. Science 268, 1503–1506 (1995)

    Article  Google Scholar 

  • P. Maldonado, C. Babul, W. Singer, E. Rodriguez, D. Berger, S. Grün, Synchronization of neuronal responses in primary visual cortex of monkeys viewing natural images. J. Neurophysiol. 100, 1523–1532 (2008)

    Article  Google Scholar 

  • N.T. Markov et al., A weighted and directed interareal connectivity matrix for macaque cerebral cortex. Cereb. Cortex 24, 17–36 (2014)

    Article  Google Scholar 

  • H. Markram, J. Lübke, M. Frotscher, B. Sakmann, Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science 275, 213–215 (1997)

    Article  Google Scholar 

  • P.M. Milner, The functional nature of neuronal oscillations. Trends Neurosci. 15, 387 (1992)

    Article  Google Scholar 

  • W.H.R. Miltner, C. Braun, M. Arnold, H. Witte, E. Taub, Coherence of gamma-band EEG activity as a basis for associative learning. Nature 397, 434–436 (1999)

    Article  Google Scholar 

  • V. Moca, L. Klein, H. Klon-Lipok, R. Muresan, W. Singer, Attention effects on the complexity of cortical dynamics  (in prep.)

    Google Scholar 

  • E.N. Niebur, H.G. Schuster, D.M. Kammen, Collective frequencies and metastability in networks of limit cycle oscillators with time delay. Phys. Rev. Lett. 67, 2753–2756 (1991)

    Article  Google Scholar 

  • D. Nikolic, S. Häusler, W. Singer, W. Maass, Distributed fading memory for stimulus properties in the primary visual cortex. PLoS Biol. 7(e1000260), 1–19 (2009)

    Google Scholar 

  • S. Pajevic, P.J. Basser, R.D. Fields, Role of myelin plasticity in oscillations and synchrony of neuronal activity. Neuroscience 276, 135–147 (2014)

    Article  Google Scholar 

  • A. Palmigiano, T. Geisel, F. Wolf, D. Battaglia, Flexible information routing by transient synchrony. Nat. Neurosci. 20, 1014–1022 (2017)

    Article  Google Scholar 

  • M. Pecka, Y. Han, E. Sader, T.D. Mrsic-Flogel, Experience-dependent specialization of receptive field surround for selective coding of natural scenes. Neuron 84, 457–469 (2014)

    Article  Google Scholar 

  • D. Plenz, T.C. Thiagarajan, The organizing principles of neuronal avalanches: cell assemblies in the cortex? Trends Neurosci. 30, 99–110 (2007)

    Article  Google Scholar 

  • V. Priesemann, M. Wibral, M. Valderrama, R. Pröpper, Q.M. Le Van, T. Geisel, J. Triesch, D. Nikolic, M.H.J. Munk, Spike avalanches in vivo suggest a driven, slightly subcritical brain state. Front. Syst. Neurosci. 8(108), 1–17 (2014)

    Google Scholar 

  • R. Quian Quiroga, L. Reddy, G. Kreiman, C. Koch, I. Fried, Invariant visual representation by single neurons in the human brain. Nature 435, 1102–1107 (2005)

    Article  Google Scholar 

  • M. Rabinovich, A. Volkovskii, P. Lecanda, R. Huerta, H.D.I. Abarbanel, G. Laurent, Dynamical encoding by networks of competing neuron groups: Winnerless competition. Phys. Rev. Lett. 87(068102), 1–4 (2001)

    Google Scholar 

  • S. Ray, J.H.R. Maunsell, Differences in gamma frequencies across visual cortex restrict their possible use in computation. Neuron 67, 885–896 (2010)

    Article  Google Scholar 

  • S. Ray, J.H.R. Maunsell, Do gamma oscillations play a role in cerebral cortex? Trends Cogn. Sci. 19, 78–85 (2015)

    Article  Google Scholar 

  • D.V.R. Reddy, A. Sen, G.L. Johnston, Time delay induced death in coupled limit cycle oscillators. Phys. Rev. Lett. 80, 5109–5112 (1998)

    Article  Google Scholar 

  • R.L. Redondo, R.G.M. Morris, Making memories last: the synaptic tagging and capture hypothesis. Nat. Rev. Neurosci. 12, 17–30 (2011)

    Article  Google Scholar 

  • P. Reinagel, R.C. Reid, Precise firing events are conserved across neurons. J. Neurosci. 22, 6837–6841 (2002)

    Article  Google Scholar 

  • P.R. Roelfsema, A.K. Engel, P. König, W. Singer, Visuomotor integration is associated with zero time-lag synchronization among cortical areas. Nature 385, 157–161 (1997)

    Article  Google Scholar 

  • F. Rosenblatt, The perceptron. A probabilistic model for information storage and organization in the brain. Psychol. Rev. 65, 386–408 (1958)

    Article  Google Scholar 

  • E. Salinas, T.J. Sejnowski, Impact of correlated synaptic input on output firing rate and variability in simple neuronal models. J. Neurosci. 20, 6193–6209 (2000)

    Article  Google Scholar 

  • W. Schultz, Dopamine reward prediction-error signalling: a two-component response. Nat. Rev. Neurosci. 17, 183–195 (2016)

    Article  Google Scholar 

  • W. Schultz, P. Dayan, P.R. Montague, A neural substrate of prediction and reward. Science 275, 1593–1599 (1997)

    Article  Google Scholar 

  • M. Siegel, T.H. Donner, R. Oostenveld, P. Fries, A.K. Engel, Neuronal synchronization along the dorsal visual pathway reflects the focus of spatial attention. Neuron 60, 709–719 (2008)

    Article  Google Scholar 

  • M. Siegel, T.J. Buschman, E.K. Miller, Cortical information flow during flexible sensorimotor decisions. Science 348, 1352–1355 (2015)

    Article  Google Scholar 

  • D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, Y. Chen, T. Lillicrap, F. Hui, L. Sifre, G. van den Driessche, T. Graepel, D. Hassabis, Mastering the game of Go without human knowledge. Nature 550, 354–359 (2017)

    Article  Google Scholar 

  • D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, T. Lillicrap, K. Simonyan, D. Hassabis, A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science 362, 1140–1144 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  • W. Singer, Synchronization of cortical activity and its putative role in information processing and learning. Annu. Rev. Physiol. 55, 349–374 (1993)

    Article  Google Scholar 

  • W. Singer, Development and plasticity of cortical processing architectures. Science 270, 758–764 (1995)

    Article  Google Scholar 

  • W. Singer, Neuronal synchrony: a versatile code for the definition of relations? Neuron 24, 49–65 (1999)

    Article  Google Scholar 

  • W. Singer, Synchronous oscillations and memory formation, in Learning Theory and Behavior ed. by J.H. Byrne. Learning and Memory: A Comprehensive Reference, vol. 1, 2nd edn. (Academic, Oxford, 2017), pp 591–597

    Google Scholar 

  • W. Singer, C.M. Gray, Visual feature integration and the temporal correlation hypothesis. Annu. Rev. Neurosci. 18, 555–586 (1995)

    Article  Google Scholar 

  • W. Singer, F. Tretter, Unusually large receptive fields in cats with restricted visual experience. Exp. Brain Res. 26, 171–184 (1976)

    Article  Google Scholar 

  • G.B. Smith, A. Sederberg, Y.M. Elyada, S.D. Van Hooser, M. Kaschube, D. Fitzpatrick, The development of cortical circuits for motion discrimination. Nat. Neurosci. 18, 252–261 (2015)

    Article  Google Scholar 

  • G.B. Smith, B. Hein, D.E. Whitney, D. Fitzpatrick, M. Kaschube, Distributed network interactions and their emergence in developing neocortex. Nat. Neurosci. 21, 1600–1608 (2018)

    Article  Google Scholar 

  • M.C. Soriano, J. Garcia-Ojalvo, C.R. Mirasso, I. Fischer, Complex photonics: Dynamics and applications of delay-coupled semiconductors lasers. Rev. Mod. Phys. 85, 421–470 (2013)

    Article  Google Scholar 

  • D.D. Stettler, A. Das, J. Bennett, C.D. Gilbert, Lateral connectivity and contextual interactions in macaque primary visual cortex. Neuron 36, 739–750 (2002)

    Article  Google Scholar 

  • D.Y. Tsao, W.A. Freiwald, R.B.H. Tootell, M.S. Livingstone, A cortical region consisting entirely of face-selective cells. Science 311, 670–674 (2006)

    Article  Google Scholar 

  • G.G. Turrigiano, S.B. Nelson, Homeostatic plasticity in the developing nervous system. Nat. Rev. Neurosci. 5, 97–107 (2004)

    Article  Google Scholar 

  • R. Van Rullen, A. Delmore, S. Thorpe, Feed-forward contour integration in primary visual cortex based on asynchronous spike propagation. Neurocomputing 38, 1003–1009 (2001)

    Google Scholar 

  • R. Van Rullen, R. Guyonneau, S.J. Thorpe, Spike times make sense. Trends Neurosci. 28, 1–4 (2005)

    Article  Google Scholar 

  • R. Vicente, L.L. Gollo, C.R. Mirasso, I. Fischer, G. Pipa, Dynamical relaying can yield zero time lag neuronal synchrony despite long conduction delays. Proc. Natl. Acad. Sci. U.S.A. 105, 17157–17162 (2008)

    Article  Google Scholar 

  • M. Vinck, C.A. Bosman, More gamma more predictions: Gamma-synchronization as a key mechanism for efficient integration of classical receptive field inputs with surround predictions. Front. Syst. Neurosci. 10(35), 1–27 (2016)

    Google Scholar 

  • C. von der Malsburg, J. Buhmann, Sensory segmentation with coupled neural oscillators. Biol. Cybern. 67, 233–242 (1992)

    Article  MATH  Google Scholar 

  • H. von Helmholtz, Handbuch der Physiologischen Opt. (Leopold Voss Verlag, Hamburg, 1867)

    Google Scholar 

  • M.A. Whittington, R.D. Traub, N. Kopell, B. Ermentrout, E.H. Buhl, Inhibition-based rhythms: experimental and mathematical observations on network dynamics. Intern. J. Psychophysiol. 38, 315–336 (2000)

    Article  Google Scholar 

  • A.T. Winfree, Biological rhythms and the behavior of populations of coupled oscillators. J. Theor. Biol. 16, 15–42 (1967)

    Article  Google Scholar 

  • J. Yamamoto, J. Suh, D. Takeuchi, S. Tonegawa, Successful execution of working memory linked to synchronized high-frequency gamma oscillations. Cell 157, 845–857 (2014)

    Article  Google Scholar 

  • O. Yizhar, L.E. Fenno, M. Prigge, F. Schneider, T.J. Davidson, D.J. O’Shea, V.S. Sohal, I. Goshen, J. Finkelstein, J.T. Paz, K. Stehfest, R. Fudim, C. Ramakrishnan, J.R. Huguenard, P. Hegemann, K. Deisseroth, Neocortical excitation/inhibition balance in information processing and social dysfunction. Nature 477, 171–178 (2011)

    Article  Google Scholar 

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Singer, W. (2021). The Cerebral Cortex: A Delay-Coupled Recurrent Oscillator Network?. In: Nakajima, K., Fischer, I. (eds) Reservoir Computing. Natural Computing Series. Springer, Singapore. https://doi.org/10.1007/978-981-13-1687-6_1

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