Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Correlated neuronal activity and the flow of neural information

Nature Reviews Neuroscience volume 2, pages 539–550 (2001)Cite this article

Abstract

For years we have known that cortical neurons collectively have synchronous or oscillatory patterns of activity, the frequencies and temporal dynamics of which are associated with distinct behavioural states. Although the function of these oscillations has remained obscure, recent experimental and theoretical results indicate that correlated fluctuations might be important for cortical processes, such as attention, that control the flow of information in the brain.

Key Points

  • Cortical neurons exhibit synchronous or oscillatory activity patterns that are associated with various behavioural states. Such temporally correlated activity may serve to regulate the flow of information.

  • Coincidence detection allows neurons to be sensitive to temporal input patterns. Clusters of synapses that interact with each other can amplify the response to simultaneous activation and such interactions can be boosted by voltage-dependent channels.

  • Neurons can also sum or average their inputs to generate action potentials. Such integration depends crucially on the balance between the strength of inhibitory and excitatory inputs. Input fluctuations have the strongest effect on balanced neurons, creating rich dynamics in networks of such neurons.

  • A group of neurons can affect another, downstream group in two ways: by changing the firing rates of and/or the correlations between local neurons.

  • Cortical synchronous activity can be generated intrinsically and regulated by neuromodulators that can cause switching between oscillatory frequencies. Such changes in correlated activity might reflect changes in the functional connectivity of a circuit.

  • Correlated activity has been recorded in the primate brain and seen to covary with behaviour. For example, expectation can increase synchrony in the motor cortex, and attention can synchronize evoked activity in the somatosensory or visual cortex. Such synchronization might have functional effects: for example, gamma oscillations in the visual cortex cause the latencies of neuronal firing to become correlated. In an interocular rivalry paradigm in the cat, synchrony is highest in neurons responding to the image being perceived, although firing rates do not differ.

  • Large variations in correlations can occur in the absence of changes in firing rates, and neurons can be very sensitive to correlations. Changes in synchrony might be important for processes, such as expectation and attention, that influence the flow of information rather than stimulus representation.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Synthetic computer-generated spike trains with various correlation patterns.
Figure 2: Responses of two model neurons to four input correlation patterns.
Figure 3: Cross-correlation histograms, with and without attention, from pairs of neurons recorded in the secondary somatosensory cortex of awake monkeys.
Figure 4: Attention induces changes in synchrony in the visual cortex.

Similar content being viewed by others

References

  1. Seidemann, E., Zohary, U. & Newsome, W. T. Temporal gating of neural signals during performance of a visual discrimination task. Nature 394, 72–75 (1998).A rare inquiry into how neural signals are gated. Microstimulation pulses were applied in the middle temporal visual cortex (MT) during a visual motion discrimination task. Their effect depended critically on the timing of the pulses relative to the time of stimulus presentation.

    Article  CAS  PubMed  Google Scholar 

  2. Barlow, J. S. The Electroencephalogram: its Patterns and Origins (MIT Press, Cambridge, Massachusetts, 1993).

    Google Scholar 

  3. Borbèly, A. A., Hayaishi, O., Sejnowski, T. J. & Altman, J. S. (eds) The Regulation of Sleep (Human Frontier Science Program, Strasbourg, 2000).

    Google Scholar 

  4. Destexhe, A. & Sejnowski, T. J. Why do we sleep? Brain Res. 886, 208–223 (2000).

    Article  PubMed  Google Scholar 

  5. Chrobak, J. J. & Buzsàki, G. High-frequency oscillations in the output networks of the hippocampal–entorhinal axis of the freely-behaving rat. J. Neurosci. 16, 3056–3066 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Engel, A. K., König, P. & Schillen, T. B. Why does the cortex oscillate? Curr. Biol. 2, 332–334 (1992).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  8. Usrey, W. M. & Reid, R. C. Synchronous activity in the nervous system. Annu. Rev. Neurosci. 61, 435–456 (1999).

    CAS  Google Scholar 

  9. Shadlen, M. N. & Movshon, J. A. Synchrony unbound: a critical evaluation of the temporal binding hypothesis. Neuron 24, 67–77 (1999).

    Article  CAS  PubMed  Google Scholar 

  10. Gray, C. M. The temporal correlation hypothesis of visual feature integration: still alive and well. Neuron 24, 31–47 (1999).

    Article  CAS  PubMed  Google Scholar 

  11. Aertsen, A. & Arndt, M. Response synchronization in the visual cortex. Curr. Opin. Neurobiol. 3, 586–594 (1993).

    Article  CAS  PubMed  Google Scholar 

  12. Wehr, M. & Laurent, G. Relationship between afferent and central temporal patterns in the locust olfactory system. J. Neurosci. 19, 381–390 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Laurent, G. et al. Odor encoding as an active, dynamical process: experiments, computation, and theory. Annu. Rev. Neurosci. 24, 263–297 (2001).

    Article  CAS  PubMed  Google Scholar 

  14. Bazhenov, M. et al. Model of transient oscillatory synchronization in the locust antennal lobe. Neuron 30, 553–567 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. MacLeod, K., Bäcker, A. & Laurent, G. Who reads temporal information contained across synchronized and oscillatory spike trains? Nature 395, 693–698 (1998).This and the next paper are complementary studies on the functional role of synchrony in the olfactory system of insects. When neurons in the antennal lobe are artificially desynchronized, the responses of downstream neurons are markedly distorted and the animals' ability to discriminate odours is impaired. Similar experiments investigating the impact of oscillations on neural circuits and on behaviour should, at some point, be possible in mammals.

    Article  CAS  PubMed  Google Scholar 

  16. Stopfer, M., Bhagavan, S., Smith, B. H. & Laurent, G. Impaired odour discrimination on desynchronization of odour-encoding neural assemblies. Nature 390, 70–74 (1997).

    Article  CAS  PubMed  Google Scholar 

  17. Kashiwadani, H., Sasaki, Y. F., Uchida, N. & Mori, K. Synchronized oscillatory discharges of mitral/tufted cells with different molecular receptive ranges in the rabbit olfactory bulb. J. Neurophysiol. 82, 1786–1792 (1999).

    Article  CAS  PubMed  Google Scholar 

  18. Dan, Y., Alonso, J. M., Usrey, W. M. & Reid, R. C. Coding of visual information by precisely correlated spikes in the lateral geniculate nucleus. Nature Neurosci. 1, 501–507 (1998).

    Article  CAS  PubMed  Google Scholar 

  19. DeCharms, R. C. & Merzenich, M. M. Primary cortical representation of sounds by the coordination of action potential timing. Nature 381, 610–613 (1995).

    Article  Google Scholar 

  20. Kreiter, A. K. & Singer, W. Stimulus-dependent synchronization of neuronal responses in the visual cortex of the awake macaque monkey. J. Neurosci. 16, 2381–2396 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Perkel, D. H., Gerstein, G. L. & Moore, G. P. Neuronal spike trains and stochastic point processes. II. Simultaneous spike trains. Biophys. J. 7, 419–440 (1967).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Aertsen, A. M. H. J., Gerstein, G. L., Habib, M. K. & Palm, G. Dynamics of neuronal firing correlation: modulation of 'effective connectivity'. J. Neurophysiol. 61, 900–917 (1989).

    Article  CAS  PubMed  Google Scholar 

  23. Brody, C. D. Correlations without synchrony. Neural Comput. 11, 1537–1551 (1999).Points out several ways in which peaks might arise in cross-correlation histograms. An important reference for anyone using this method.

    Article  CAS  PubMed  Google Scholar 

  24. Vaadia, E. et al. Dynamics of neuronal interactions in monkey cortex in relation to behavioural events. Nature 373, 515–518 (1995).

    Article  CAS  PubMed  Google Scholar 

  25. Ts'o, D. Y., Gilbert, C. D. & Wiesel, T. N. Relationships between horizontal interactions and functional architecture in cat striate cortex as revealed by cross-correlation analysis. J. Neurosci. 6, 1160–1170 (1986).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Gochin, P. M., Miller, E. K., Gross, C. G. & Gerstein, G. L. Functional interactions among neurons in inferior temporal cortex of the awake macaque. Exp. Brain Res. 84, 505–516 (1991).

    Article  CAS  PubMed  Google Scholar 

  27. Nelson, J. I., Salin, P. A., Munk, M. H.-J., Arzi, M. & Bullier, J. Spatial and temporal coherence in cortico–cortical connections: a cross-correlation study in areas 17 and 18 in the cat. Vis. Neurosci. 9, 21–37 (1992).

    Article  CAS  PubMed  Google Scholar 

  28. Brosch, M. & Schreiner, C. E. Correlations between neural discharges are related to receptive field properties in cat primary auditory cortex. Eur. J. Neurosci. 11, 3517–3530 (1999).

    Article  CAS  PubMed  Google Scholar 

  29. Livingstone, M. S. Oscillatory firing and interneuronal correlations in squirrel monkey striate cortex. J. Neurophysiol. 75, 2467–2485 (1996).

    Article  CAS  PubMed  Google Scholar 

  30. Gray, C. M. & Viana Di Prisco, G. Stimulus-dependent neuronal oscillations and local synchronization in striate cortex of the alert cat. J. Neurosci. 17, 3239–3253 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Murthy, V. N. & Fetz, E. E. Oscillatory activity in sensorimotor cortex of awake monkeys: synchronization of local field potentials and relation to behavior. J. Neurophysiol. 76, 3949–3967 (1996).

    Article  CAS  PubMed  Google Scholar 

  32. Murthy, V. N. & Fetz, E. E. Synchronization of neurons during local field potential oscillations in sensorimotor cortex of awake monkeys. J. Neurophysiol. 76, 3968–3982 (1996).

    Article  CAS  PubMed  Google Scholar 

  33. Donoghue, J. P., Sanes, J. N., Hatsopoulos, N. G. & Gaàl, G. Neural discharge and local field potential oscillations in primate motor cortex during voluntary movements. J. Neurophysiol. 79, 159–173 (1998).

    Article  CAS  PubMed  Google Scholar 

  34. Abeles, M. Role of the cortical neuron: integrator or coincidence detector? Isr. J. Med. Sci. 18, 83–92 (1982).

    CAS  PubMed  Google Scholar 

  35. Softky, W. R. Sub-millisecond coincidence detection in active dendritic trees. Neuroscience 58, 13–41 (1993).

    Article  Google Scholar 

  36. Softky, W. R. & Koch, C. The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs. J. Neurosci. 13, 334–350 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. König, P., Engel, A. K. & Singer, W. Integrator or coincidence detector? The role of the cortical neuron revisited. Trends Neurosci. 19, 130–137 (1996).

    Article  PubMed  Google Scholar 

  38. Carr, C. E. & Konishi, M. A circuit for detection of interaural time differences in the brain stem of the barn owl. J. Neurosci. 10, 3227–3246 (1990).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Agmon-Snir, H., Carr, C. E. & Rinzel, J. The role of dendrites in auditory coincidence detection Science 393, 268–272 (1998).

    CAS  Google Scholar 

  40. Mel, B. W. Synaptic integration in an excitable dendritic tree. J. Neurophysiol. 70, 1086–1101 (1993).

    Article  CAS  PubMed  Google Scholar 

  41. Margulis, M. & Tang, C. M. Temporal integration can readily switch between sublinear and supralinear summation. J. Neurophysiol. 79, 2809–2813 (1998).

    Article  CAS  PubMed  Google Scholar 

  42. Mel, B. W. in Dendrites (eds Stuart, G., Spruston, N. & Hausser, M.) 271–289 (Oxford Univ. Press, Oxford, 1999).

    Google Scholar 

  43. Poirazi, P. & Mel, B. W. Impact of active dendrites and structural plasticity on the memory capacity of neural tissue. Neuron 29, 779–796 (2001).

    Article  CAS  PubMed  Google Scholar 

  44. Archie, K. A. & Mel, B. W. Structural plasticity, dendritic subunits, and the development of nonlinear visual receptive field properties. Soc. Neurosci. Abstr. 26, 1362 (2000).

  45. Azouz, R. & Gray, C. M. Dynamic spike threshold reveals a mechanism for synaptic coincidence detection in cortical neurons in vivo. Proc. Natl Acad. Sci. USA 97, 8110–8115 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Burkitt, A. N. & Clark, G. M. Analysis of integrate-and-fire neurons: synchronization of synaptic input and spike output. Neural Comput. 11, 871–901 (1999).

    Article  CAS  PubMed  Google Scholar 

  47. Diesmann, M., Gewaltig, M.-O. & Aertsen, A. Stable propagation of synchronous spiking in cortical neural networks. Nature 402, 529–533 (1999).A different perspective on coincidence detection and synchrony. A network of neurons is activated by a volley of input spikes that occurs at a certain time and with a given temporal width. The response is another volley of spikes, the timing and width of which depend on network parameters. Analytical and simulation results show that such volleys can propagate stably and with minimal time jitter through multiple layers.

    Article  CAS  PubMed  Google Scholar 

  48. Shadlen, M. N. & Newsome, W. T. Noise, neural codes and cortical organization. Curr. Opin. Neurobiol. 4, 569–579 (1994).

    Article  CAS  PubMed  Google Scholar 

  49. Shadlen, M. N. & Newsome, W. T. The variable discharge of cortical neurons: implications for connectivity, computation and information coding. J. Neurosci. 18, 3870–3896 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Bernander, Ö., Koch, C. & Usher, M. The effects of synchronized inputs at the single neuron level. Neural Comput. 6, 622–641 (1994).

    Article  Google Scholar 

  51. Murthy, V. N. & Fetz, E. E. Effects of input synchrony on the firing rate of a three-conductance cortical neuron model. Neural Comput. 6, 1111–1126 (1994).

    Article  Google Scholar 

  52. Salinas, E. & Sejnowski, T. J. Impact of correlated synaptic input on output firing rate and variability in simple neuronal models. J. Neurosci. 20, 6193–6209 (2000).Using theoretical models and computer simulations, the authors study the possible effects of input correlations on a postsynaptic neuron, as well as the properties that make the postsynaptic neuron more or less sensitive to them. A key condition for high sensitivity is the balance between excitation and inhibition; a balanced neuron might be strongly affected by small changes in input synchrony or by oscillations in input firing rates.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Bell, A. J., Mainen, Z. F., Tsodyks, M. & Sejnowski, T. J. 'Balancing' of conductances may explain irregular cortical spiking. Technical Report INC-9502, Institute for Neural Computation, Univ. California at San Diego, California 92093-0523 (1995).

  54. Troyer, T. W. & Miller, K. D. Physiological gain leads to high ISI variability in a simple model of a cortical regular spiking cell. Neural Comput. 9, 971–983 (1997).

    Article  CAS  PubMed  Google Scholar 

  55. Feng, J. & Brown, D. Impact of correlated inputs on the output of the integrate-and-fire model. Neural Comput. 12, 671–692 (2000).

    Article  CAS  PubMed  Google Scholar 

  56. Tsodyks, M. V. & Sejnowski, T. J. Rapid state switching in balanced cortical network models. Network 6, 111–124 (1995).

    Article  Google Scholar 

  57. Van Vreeswijk, C. & Sompolinsky, H. Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science 274, 1724–1726 (1996).

    Article  CAS  PubMed  Google Scholar 

  58. Van Vreeswijk, C. & Sompolinsky, H. Chaotic balanced state in a model of cortical circuits. Neural Comput. 10, 1321–1371 (1998).

    Article  CAS  PubMed  Google Scholar 

  59. Brunel, N. & Hakim, V. Fast global oscillations in networks of integrate-and-fire neurons with low firing rates. Neural Comput. 11, 1621–1671 (1999).

    Article  CAS  PubMed  Google Scholar 

  60. Doiron, B., Longtin, A., Berman, N. & Maler, L. Subtractive and divisive inhibition: effect of voltage-dependent inhibitory conductances and noise. Neural Comput. 13, 227–248 (2001).

    Article  CAS  PubMed  Google Scholar 

  61. Tiesinga, P. H. E., José, J. V. & Sejnowski, T. J. Comparison of current-driven and conductance-driven neocortical model neuron with Hodgkin–Huxley voltage-gated channels. Phys. Rev. E 62, 8413–8419 (2000).

    Article  CAS  Google Scholar 

  62. Chance, F. S. & Abbott, L. F. Multiplicative gain modulation through balanced synaptic input. Soc. Neurosci. Abstr. 26, 1064 (2000).

  63. Stevens, C. F. & Zador, A. M. Input synchrony and the irregular firing of cortical neurons. Nature Neurosci. 1, 210–217 (1998).

    Article  CAS  PubMed  Google Scholar 

  64. Svirskis, G. & Rinzel, J. Influence of temporal correlation of synaptic input on the rate and variability of firing in neurons. Biophys. J. 79, 629–637 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Salinas, E. & Sejnowski, T. J. Exact solutions for the non-leaky integrate-and-fire model neuron driven by correlated stochastic inputs. Soc. Neurosci. Abstr. 27 (in the press).

  66. Ritz, R. & Sejnowski, T. J. in Artificial Neural Networks — ICANN '97 (eds Gerstner, W., Germond, A., Hasler, M. & Nicoud, J.-D.) 79–84 (Springer, Lausanne, Switzerland, 1997).

    Book  Google Scholar 

  67. Usrey, W. M., Reppas, J. B. & Reid, R. C. Paired-spike interactions and synaptic efficacy of retinal inputs to the thalamus. Nature 395, 384–387 (1998).

    Article  CAS  PubMed  Google Scholar 

  68. José, J. V., Tiesinga, P. H. E., Fellous, J.-M., Salinas, E. & Sejnowski, T. J. Synchronization as a mechanism for attentional modulation. Soc. Neurosci. Abstr. 27 (in the press).

  69. Salinas, E. & Their, P. Gain modulation — a major computational principle of the central nervous system. Neuron 27, 15–21 (2000).

    Article  CAS  PubMed  Google Scholar 

  70. Salinas, E. & Abbott, L. F. in Progress in Brain Research Vol. 130 (ed. Nicolelis, M.) (Elsevier, Amsterdam, in the press).

  71. Salinas, E. & Sejnowski, T. J. Gain modulation in the central nervous system: where behavior, neurophysiology and computation meet. The Neuroscientist (in the press).

  72. Zhang, M. & Barash, S. Neuronal switching of sensorimotor transformations for antisaccades. Nature 408, 971–975 (2000).A study in awake monkeys showing that visually triggered sensory responses in the lateral intraparietal area (LIP) can be switched on or off depending on the contingencies of the task. As in reference 1 , the emphasis is on how neurons communicate, rather than on how they represent the sensory world.

    Article  CAS  PubMed  Google Scholar 

  73. O'Keefe, J. & Recce, M. L. Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus 3, 317–330 (1993).

    Article  CAS  PubMed  Google Scholar 

  74. Tiesinga, P. H. E., Fellous, J.-M., José, J. V. & Sejnowski, T. J. Optimal information transfer in synchronized neocortical neurons. Neurocomputing 38, 397–402 (2001).

    Article  Google Scholar 

  75. Ritz, R. & Sejnowski, T. J. Synchronous oscillatory activity in sensory systems: new vistas on mechanisms. Curr. Opin. Neurobiol. 7, 536–546 (1997).

    Article  CAS  PubMed  Google Scholar 

  76. Lytton, W. W. & Sejnowski, T. J. Simulations of cortical pyramidal neurons synchronized by inhibitory interneurons. J. Neurophysiol. 66, 1059–1079 (1991).

    Article  CAS  PubMed  Google Scholar 

  77. Cobb, S. R., Buhl, E. H., Halasy, K., Paulsen, O. & Somogyi, P. Synchronization of neural activity in hippocampus by individual GABAergic interneurons. Nature 378, 75–78 (1995).

    Article  CAS  PubMed  Google Scholar 

  78. Bush, P. & Sejnowski, T. J. Inhibition synchronizes sparsely connected cortical neurons within and between columns in realistic network models. J. Comput. Neurosci. 3, 91–110 (1996).

    Article  CAS  PubMed  Google Scholar 

  79. Jefferys, J. G. R., Traub, R. D. & Whittington, M. A. Neuronal networks for induced '40 Hz' rhythms. Trends Neurosci. 19, 202–208 (1996).

    Article  CAS  PubMed  Google Scholar 

  80. Fricker, D. & Miles, R. EPSP amplification and the precision of spike timing in hippocampal neurons. Neuron 28, 559–569 (2000).

    Article  CAS  PubMed  Google Scholar 

  81. Galarreta, M. & Hestrin, S. Spike transmission and synchrony detection in networks of GABAergic interneurons. Science 292, 2295–2299 (2001).

    Article  CAS  PubMed  Google Scholar 

  82. Galarreta, M. & Hestrin, S. Electrical synapses between GABA-releasing interneurons. Nature Rev. Neurosci. 2, 425–433 (2001).

    Article  CAS  Google Scholar 

  83. Fellous, J.-M. & Sejnowski, T. J. Cholinergic induction of spontaneous oscillations in the hippocampal slice in the slow (0.5–2 Hz), theta (5–12 Hz) and gamma (35–70 Hz) bands. Hippocampus 10, 187–197 (2000).Three kinds of rhythmic activity are observed in a hippocampal slice preparation, and a single neuromodulator can shift the dynamics from one mode to another. A model for this concentration-dependent switching is developed in the reference below.

    Article  CAS  PubMed  Google Scholar 

  84. Tiesinga, P. H. E., Fellous, J.-M., José, J. V. & Sejnowski, T. J. Computational model of carbachol-induced delta, theta and gamma oscillations in the hippocampus. Hippocampus 11, 251–274 (2001).

    Article  CAS  PubMed  Google Scholar 

  85. Bland, B. H. The physiology and pharmacology of hippocampal formation theta rhythms. Prog. Neurobiol. 26, 1–54 (1986).

    Article  CAS  PubMed  Google Scholar 

  86. Lisman, J. E. Relating hippocampal circuitry to function: recall of memory sequences by reciprocal dentate–CA3 interactions. Neuron 22, 233–242 (1999).

    Article  CAS  PubMed  Google Scholar 

  87. Siapas, A. G. & Wilson, M. A. Coordinated interactions between hippocampal ripples and cortical spindles during slow-wave sleep. Neuron 21, 1123–1128 (1998).

    Article  CAS  PubMed  Google Scholar 

  88. Hooper, S. L. & Moulins, M. Switching of a neuron from one network to another by sensory-induced changes in membrane properties. Science 244, 1587–1589 (1989).

    Article  CAS  PubMed  Google Scholar 

  89. Weimann, J. M. & Marder, E. Switching neurons are integral members of multiple oscillatory networks. Curr. Biol. 4, 896–902 (1994).

    Article  CAS  PubMed  Google Scholar 

  90. Llinás, R. R. The intrinsic electrophysiological properties of mammalian neurons: insights into central nervous system function. Science 242, 1654–1664 (1988).

    Article  PubMed  Google Scholar 

  91. Gray, C. M. & McCormick, D. A. Chattering cells: superficial pyramidal neurons contributing to the generation of synchronous oscillations in the visual cortex. Science 274, 109–113 (1996).

    Article  CAS  PubMed  Google Scholar 

  92. Lüti, A. & McCormick, D. A. H-current: properties of a neuronal and network pacemaker. Neuron 21, 9–12 (1998).

    Article  Google Scholar 

  93. Wilson, M. & Bower, J. M. Cortical oscillations and network interactions in a computer simulation of piriform cortex. J. Neurophysiol. 67, 981–995 (1992).

    Article  CAS  PubMed  Google Scholar 

  94. Fuentes, U., Ritz, R., Gerstner, W. & Van Hemmen, J. L. Vertical signal flow and oscillations in a three-layer model of the cortex. J. Comput. Neurosci. 3, 125–136 (1996).

    Article  CAS  PubMed  Google Scholar 

  95. Timofeev, I., Grenier, F., Bazhenov, M., Sejnowski, T. J. & Steriade, M. Origin of slow cortical oscillations in deafferented cortical slabs. Cereb. Cortex 10, 1185–1199 (2000).

    Article  CAS  PubMed  Google Scholar 

  96. Von der Malsburg, C. in Models of Neural Networks II (eds Domany, E., Van Hemmen, J. L. & Schulten, K.) 95–119 (Springer, Berlin, 1994).

    Book  Google Scholar 

  97. Aertsen, A., Erb, M. & Palm, G. Dynamics of functional coupling in the cerebral cortex: an attempt at a model-based interpretation. Physica D 75, 103–128 (1994).

    Article  Google Scholar 

  98. Riehle, A., Grün, S., Diesmann, M. & Aertsen, A. Spike synchronization and rate modulation differentially involved in motor cortical function. Science 278, 1950–1953 (1997).This study exploits a simple, yet creative, behavioural model to study synchronization in the primary motor cortex. Neurons in this area become transiently synchronized when a stimulus appears, or when it is expected to appear but it does not. In the former case, mean firing rates typically change with (but independently of) synchrony, but in the latter case they typically do not.

    Article  CAS  PubMed  Google Scholar 

  99. Hsiao, S. S., Johnson, K. O. & O'Shaughnessy, D. M. Effects of selective attention of spatial form processing in monkey primary and secondary somatosensory cortex. J. Neurophysiol. 70, 444–447 (1993).

    Article  CAS  PubMed  Google Scholar 

  100. Burton, H., Sinclair, R. J., Hong, S. Y., Pruett, J. R. & Whang, K. C. Tactile-spatial and cross-modal attention effects in the second somatosensory and 7b cortical areas of rhesus monkeys. Somatosens. Mot. Res. 14, 237–267 (1997).

    Article  CAS  PubMed  Google Scholar 

  101. Johansen-Berg, H. & Lloyd, D. M. The physiology and psychology of attention to touch. Front. Biosci. 5, D894–904 (2000).

    Article  CAS  PubMed  Google Scholar 

  102. Salinas, E., Hernández, H., Zainos, A. & Romo, R. Periodicity and firing rate as candidate neural codes for the frequency of vibrotactile stimuli. J. Neurosci. 20, 5503–5515 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Niebur, E. & Koch, C. A model for the neuronal implementation of selective visual attention based on temporal correlation among neurons. J. Comput. Neurosci. 1, 141–158 (1994).

    Article  CAS  PubMed  Google Scholar 

  104. Steinmetz, P. N. et al. Attention modulates synchronized neuronal firing in primate somatosensory cortex. Nature 404, 187–190 (2000).A study in which tactile stimuli were delivered and neurons in the secondary somatosensory cortex responded to them. When attention is focused on the tactile stimuli, the neurons respond more intensely and become more synchronized than when attention is directed towards a visual display. So, attention might regulate, through changes in synchrony, the strength of the somatosensory response.

    Article  CAS  PubMed  Google Scholar 

  105. Moran, J. & Desimone, R. Selective attention gates visual processing in the extrastriate cortex. Science 229, 782–784 (1985).

    Article  CAS  PubMed  Google Scholar 

  106. Motter, B. C. Focal attention produces spatially selective processing in visual cortical areas V1, V2, and V4 in the presence of competing stimuli. J. Neurophysiol. 70, 909–919 (1993).

    Article  CAS  PubMed  Google Scholar 

  107. Connor, C. E., Preddie, D. C., Gallant, J. L. & Van Essen, D. C. Spatial attention effects in macaque area V4. J. Neurosci. 17, 3201–3214 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. McAdams, C. J. & Maunsell, J. H. R. Effects of attention on orientation tuning functions of single neurons in macaque cortical area V4. J. Neurosci. 19, 431–441 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Reynolds, J. & Desimone, R. Competitive mechanisms subserve attention in macaque areas V2 and V4. J. Neurosci. 19, 1736–1753 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Treue, S. & Martínez-Trujillo, J. C. Feature-based attention influences motion processing gain in macaque visual cortex. Nature 399, 575–579 (1999).

    Article  CAS  PubMed  Google Scholar 

  111. Kastner, S. & Ungerleider, L. Mechanisms of visual attention in human cortex. Annu. Rev. Neurosci. 23, 315–341 (2000).

    Article  CAS  PubMed  Google Scholar 

  112. Reynolds, J. H., Pasternak, T. & Desimone, R. Attention increases sensitivity of V4 neurons. Neuron 2, 703–714 (2000).

    Article  Google Scholar 

  113. Fries, P., Reynolds, J. H., Rorie, A. E. & Desimone, R. Modulation of oscillatory neuronal synchronization by selective visual attention. Science 291, 1560–1563 (2001).The responses of visual neurons were compared when attention was directed inside or outside their receptive fields, for the same stimulus. Extreme care was taken to minimize changes in mean firing rate and to measure synchrony in an unbiased way. When attention shifts to the recorded neuron's receptive field, the unit and its neighbours become more synchronized with respect to rapid (50-Hz) fluctuations, but less so with respect to slow (10-Hz) fluctuations. Attention seems to cause a complex yet stereotyped change in the dynamics of the local circuit of visual neurons.

    Article  CAS  PubMed  Google Scholar 

  114. Frost, J. D. Jr An averaging technique for detection of EEG–intracellular potential relationships. Electroencephalogr. Clin. Neurophysiol. 23, 179–181 (1967).

    Article  PubMed  Google Scholar 

  115. Goto, Y. & O'Donnell, P. Network synchrony in the nucleus accumbens in vivo. J. Neurosci. 21, 4498–4504 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Fries, P., Neuenschwander, S., Engel, A. K., Goebel, R. & Singer, W. Rapid feature selective neuronal synchronization through correlated latency shifting. Nature Neurosci. 4, 194–200 (2001).Latencies in the responses evoked by visual stimuli were measured simultaneously for pairs of neurons. These latencies covaried across trials, with stronger covariations observed for pairs that were more synchronized in the band around 50 Hz. Covariations in latency were independent of covariations in firing rate, and were not caused by common input. A functional role for oscillations in the 50-Hz range is suggested: to temporally align the responses of the synchronized neural population to a forthcoming stimulus.

    Article  CAS  PubMed  Google Scholar 

  117. Fries, P., Roelfsema, P. R., Engel, A. K., König, P. & Singer, W. Synchronization of oscillatory responses in visual cortex correlates with perception in interocular rivalry. Proc. Natl Acad. Sci. USA 94, 12699–12704 (1997).A study of V1 responses in an experimental set-up in which firing rates did not vary, but perceptual experience did. Robust changes in synchrony were observed from one perceptual condition to another. Even if the nature of the perceptual process is questioned, it is remarkable that synchrony in V1 can be so strongly modulated by changes in internal state.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Logothetis, N. K. & Schall, J. D. Neuronal correlates of subjective visual perception. Science 245, 761–763 (1989).

    Article  CAS  PubMed  Google Scholar 

  119. Leopold, D. A. & Logothetis, N. K. Activity changes in early visual cortex reflect monkeys' percepts during binocular rivalry. Nature 379, 549–553 (1996).

    Article  CAS  PubMed  Google Scholar 

  120. Braitenberg, V. & Schüz, A. Cortex: Statistics and Geometry of Neuronal Connectivity (Springer, Berlin, 1997).

  121. White, E. L. Cortical Circuits (Birkhäuser, Boston, 1989).

    Book  Google Scholar 

  122. Sejnowski, T. J. in Parallel Models of Associative Memory (eds Hinton, G. E. & Anderson, J. A.) 189–212 (Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1981).

    Google Scholar 

  123. Hopfield, J. J. & Brody, C. D. What is a moment? Transient synchrony as a collective mechanism for spatiotemporal integration. Proc. Natl Acad. Sci. USA 98, 1282–1287 (2001).A model for speech recognition in which a set of sensory units responds, a downstream population becomes activated and synchronized, and a third population further downstream responds selectively to the evoked synchrony patterns. The model shows how oscillations generated centrally could confer a functional advantage to a neural circuit.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Tuckwell, H. C. Introduction to Theoretical Neurobiology Vols 1 & 2 (Cambridge Univ. Press, New York, 1988).

    Book  Google Scholar 

  125. Koch, C. Biophysics of Computation (Oxford Univ. Press, New York, 1999).

    Google Scholar 

Download references

Acknowledgements

Research was supported by the Howard Hughes Medical Institute. We thank P. Steinmetz for providing us with Figure 3, and P. Fries for providing us with Figure 4. We also thank J. Reynolds and P. Tiesinga for helpful comments.

Author information

Author notes

  1. Emilio Salinas

    Present address: Department of Neurobiology and Anatomy, Wake Forest University School of Medicine, Medical Centre Boulevard, Winston-Salem, North Carolina, 27157-1010, USA

Authors and Affiliations

  1. Computational Neurobiology Laboratory, Howard Hughes Medical Institute, The Salk Institute for Biological Studies, 10010 North Torrey Pines Road, La Jolla, 92037, California, USA

    Emilio Salinas & Terrence J. Sejnowski

  2. Department of Biology, University of California at San Diego, La Jolla, 92093, California, USA

    Terrence J. Sejnowski

Authors
  1. Emilio Salinas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Terrence J. Sejnowski
    View author publications

    You can also search for this author in PubMed Google Scholar

Related links

Related links

MIT ENCYCLOPEDIA OF COGNITIVE SCIENCES

Binding by neural synchrony

Computational neuroscience

Computing in single neurons

Glossary

BINDING PROBLEM

The problem of binding together representations of the different properties of an object (for example, its colour, form and location).

MEMBRANE TIME CONSTANT

A quantity that depends on the capacitance and resistance of the cell membrane, and which sets a timescale for changes in voltage. A small time constant means that the membrane potential can change rapidly.

ELECTROTONICALLY DISTANT

Two points on the dendritic tree are electrotonically distant if the electrical interactions between them are minimal, regardless of the actual physical distance between the points.

STRABISMIC CAT

A condition in which the eyes are not straight or properly aligned. The misalignment reflects the failure of the eye muscles to work together. One eye may turn in (crossed eyes), turn out (wall eyes), turn up or turn down. Although some cats are congenitally strabismic, strabismus can also be achieved by cutting the tendon of one of the eye muscles.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Salinas, E., Sejnowski, T. Correlated neuronal activity and the flow of neural information. Nat Rev Neurosci 2, 539–550 (2001). https://doi.org/10.1038/35086012

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1038/35086012

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing