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  • Review Article
  • Published:

Regulation of spike timing in visual cortical circuits

Nature Reviews Neuroscience volume 9, pages 97–107 (2008)Cite this article

Key Points

  • In vitro, precise and reliable spike trains are obtained in response to fluctuating current waveforms injected at the soma. For aperiodic currents this is called stimulus locking; for periodic currents it is called phase locking.

  • Across trials, stimulus-locked spike trains contain multiple distinct spike patterns that can be uncovered using analysis procedures based on clustering.

  • In vivo, output spike trains reflect the interaction that occurs between stimulus-locked inputs and oscillatory inputs that are generated internally. As a result, precise and reliable spike trains can sometimes be obtained in response to time-varying stimulus waveforms (when the spikes are aligned to stimulus onset), or in response to cortical oscillations (when the spikes are aligned to the oscillation phase).

  • Stimulus or phase-locked spike trains across multiple neurons can lead to synchronous spike volleys, which can propagate efficiently through dendritic action potentials that pass through the different layers of the cortex. Inhibitory interneurons can coordinate synchronous volleys in pyramidal cells.

  • Dynamically modulated oscillations can support a flexible system for communicating through spike volleys in parallel with neural codes based on firing rates. Slow cortical oscillations set the excitability of neurons and gate the amplitude of the fast oscillations, whereas fast inhibitory oscillations can gate spike volleys or shift their time.

Abstract

A train of action potentials (a spike train) can carry information in both the average firing rate and the pattern of spikes in the train. But can such a spike-pattern code be supported by cortical circuits? Neurons in vitro produce a spike pattern in response to the injection of a fluctuating current. However, cortical neurons in vivo are modulated by local oscillatory neuronal activity and by top-down inputs. In a cortical circuit, precise spike patterns thus reflect the interaction between internally generated activity and sensory information encoded by input spike trains. We review the evidence for precise and reliable spike timing in the cortex and discuss its computational role.

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Figure 1: Stimulus locking in vitro and in vivo.
Figure 2: Calculating the reliability and precision of neural spike trains.
Figure 3: Uncovering spike patterns.
Figure 4: The effect of a periodic or an aperiodic drive on reliability in a model neuron.
Figure 5: Uncovering phase locking to internal activity.
Figure 6: Response of a model cortical cell to stimulus-related and oscillatory background synaptic inputs.
Figure 7: Experimental observations suggest that volleys that are generated by spike patterns are preferentially processed in the early sensory cortex.
Figure 8: Attentional modulation of synchrony and phase in a model network.

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References

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

    CAS  PubMed  Google Scholar 

  2. Bryant, H. L. & Segundo, J. P. Spike initiation by transmembrane current: a white-noise analysis. J. Physiol. 260, 279–314 (1976).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Reinagel, P. & Reid, R. Temporal coding of visual information in the thalamus. J. Neurosci. 20, 5392–5400 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Liu, R. C., Tzonev, S., Rebrik, S. & Miller, K. D. Variability and information in a neural code of the cat lateral geniculate nucleus. J. Neurophysiol. 86, 2789–2806 (2001).

    Article  CAS  PubMed  Google Scholar 

  5. Butts, D. A. et al. Temporal precision in the neural code and the timescales of natural vision. Nature 449, 92–95 (2007).

    Article  CAS  PubMed  Google Scholar 

  6. Shadlen, M. & Newsome, W. 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 

  7. Brenner, N., Strong, S. P., Koberle, R., Bialek, W. & de Ruyter van Steveninck, R. R. Synergy in a neural code. Neural Comput. 12, 1531–1552 (2000).

    Article  CAS  PubMed  Google Scholar 

  8. Reyes, A. D. Synchrony-dependent propagation of firing rate in iteratively constructed networks in vitro. Nature Neurosci. 6, 593–599 (2003).

    CAS  Google Scholar 

  9. de la Rocha, J., Doiron, B., Shea-Brown, E., Josic, K. & Reyes, A. Correlation between neural spike trains increases with firing rate. Nature 448, 802–806 (2007).

    Article  CAS  PubMed  Google Scholar 

  10. Hessler, N. A., Shirke, A. M. & Malinow, R. The probability of transmitter release at a mammalian central synapse. Nature 366, 569–572 (1993).

    Article  CAS  PubMed  Google Scholar 

  11. Zador, A. Impact of synaptic unreliability on the information transmitted by spiking neurons. J. Neurophysiol. 79, 1219–1229 (1998).

    Article  CAS  PubMed  Google Scholar 

  12. Deuchars, J., West, D. C. & Thomson, A. M. Relationships between morphology and physiology of pyramid-pyramid single axon connections in rat neocortex in vitro. J. Physiol. 478, 423–435 (1994).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Markram, H., Lubke, J., Frotscher, M., Roth, A. & Sakmann, B. Physiology and anatomy of synaptic connections between thick tufted pyramidal neurones in the developing rat neocortex. J. Physiol. 500, 409–440 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Han, X. & Boyden, E. S. Multiple-color optical activation, silencing, and desynchronization of neural activity, with single-spike temporal resolution. PLoS ONE 2, e299 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Zhang, F. et al. Multimodal fast optical interrogation of neural circuitry. Nature 446, 633–639 (2007). The authors of this paper inserted light-activated pumps in neurons and used light to modify the behaviour of C. elegans . This technique makes it possible to manipulate the activity of a large number of neurons at a high temporal resolution in order to test hypotheses regarding the function of synchronous and precise spike timing.

    Article  CAS  PubMed  Google Scholar 

  16. Tiesinga, P. H. E., Fellous, J. M. & Sejnowski, T. J. Attractor reliability reveals deterministic structure in neuronal spike trains. Neural Comput. 14, 1629–1650 (2002).

    Article  CAS  PubMed  Google Scholar 

  17. Tiesinga, P. H. E. Precision and reliability of periodically and quasiperiodically driven integrate-and-fire neurons. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 65, e041913 (2002).

    Article  CAS  Google Scholar 

  18. Fellous, J. M., Tiesinga, P. H. E., Thomas, P. J. & Sejnowski, T. J. Discovering spike patterns in neuronal responses. J. Neurosci. 24, 2989–3001 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Hunter, J., Milton, J., Thomas, P. & Cowan, J. Resonance effect for neural spike time reliability. J. Neurophysiol. 80, 1427–1438 (1998).

    Article  CAS  PubMed  Google Scholar 

  20. Cecchi, G. et al. Noise in neurons is message dependent. Proc. Natl Acad. Sci. USA 97, 5557–5561 (2000).

    Article  CAS  Google Scholar 

  21. 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  Google Scholar 

  22. Toups, J. V., Fellous, J.-M., Thomas, P. J., Tiesinga, P. H. & Sejnowski, T. J. Stability of in vitro spike patterns under variation of stimulus amplitude. Abstr. - Soc. Neurosci. 237.18 (2006).

  23. Fellous, J. M. et al. Frequency dependence of spike timing reliability in cortical pyramidal cells and interneurons. J. Neurophysiol. 85, 1782–1787 (2001).

    Article  CAS  PubMed  Google Scholar 

  24. Berry, M. & Meister, M. Refractoriness and neural precision. J. Neurosci. 18, 2200–2211 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Berry, M., Warland, D. & Meister, M. The structure and precision of retinal spike trains. Proc. Natl Acad. Sci. USA 94, 5411–5416 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Tiesinga, P. H. E. & Toups, J. V. The possible role of spike patterns in cortical information processing. J. Comput. Neurosci. 18, 275–286 (2005).

    Article  PubMed  Google Scholar 

  27. Keat, J., Reinagel, P., Reid, R. C. & Meister, M. Predicting every spike: a model for the responses of visual neurons. Neuron 30, 803–817 (2001).

    Article  CAS  PubMed  Google Scholar 

  28. Nowak, L. G., Sanchez-Vives, M. V. & McCormick, D. A. Influence of low and high frequency inputs on spike timing in visual cortical neurons. Cereb. Cortex 7, 487–501 (1997).

    Article  CAS  PubMed  Google Scholar 

  29. Hutcheon, B. & Yarom, Y. Resonance, oscillation and the intrinsic frequency preferences of neurons. Trends Neurosci. 23, 216–222 (2000).

    Article  CAS  PubMed  Google Scholar 

  30. Pike, F. G. et al. Distinct frequency preferences of different types of rat hippocampal neurones in response to oscillatory input currents. J. Physiol. 529, 205–213 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Haas, J. S. & White, J. A. Frequency selectivity of layer II stellate cells in the medial entorhinal cortex. J. Neurophysiol. 88, 2422–2429 (2002).

    Article  PubMed  Google Scholar 

  32. Schreiber, S., Fellous, J. M., Tiesinga, P. & Sejnowski, T. J. Influence of ionic conductances on spike timing reliability of cortical neurons for suprathreshold rhythmic inputs. J. Neurophysiol. 91, 194–205 (2004).

    Article  PubMed  Google Scholar 

  33. Tiesinga, P. H. E., Fellous, J. M., Jose, 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 

  34. White, J. A., Budde, T. & Kay, A. R. A bifurcation analysis of neuronal subthreshold oscillations. Biophys. J. 69, 1203–1217 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Hunter, J. D. & Milton, J. G. Amplitude and frequency dependence of spike timing: implications for dynamic regulation. J. Neurophysiol. 90, 387–394 (2003).

    Article  PubMed  Google Scholar 

  36. Beierholm, U., Nielsen, C. D., Ryge, J., Alstrom, P. & Kiehn, O. Characterization of reliability of spike timing in spinal interneurons during oscillating inputs. J. Neurophysiol. 86, 1858–1868 (2001).

    Article  CAS  PubMed  Google Scholar 

  37. Liljenstrom, H. & Hasselmo, M. E. Cholinergic modulation of cortical oscillatory dynamics. J. Neurophysiol. 74, 288–297 (1995).

    Article  CAS  PubMed  Google Scholar 

  38. Hasselmo, M. E. Neuromodulation and cortical function: modeling the physiological basis of behavior. Behav. Brain Res. 67, 1–27 (1995).

    Article  CAS  PubMed  Google Scholar 

  39. Brody, C. D. & Hopfield, J. J. Simple networks for spike-timing-based computation, with application to olfactory processing. Neuron 37, 843–852 (2003).

    Article  CAS  PubMed  Google Scholar 

  40. Pillow, J. W., Paninski, L., Uzzell, V. J., Simoncelli, E. P. & Chichilnisky, E. J. Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model. J. Neurosci. 25, 11003–11013 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Uzzell, V. J. & Chichilnisky, E. J. Precision of spike trains in primate retinal ganglion cells. J. Neurophysiol. 92, 780–789 (2004).

    Article  CAS  PubMed  Google Scholar 

  42. Meister, M. & Berry, M. J. The neural code of the retina. Neuron 22, 435–450 (1999).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Koepsell, K. et al. in COSYNE 2007 Meeting (Salt Lake City, 2007).

    Google Scholar 

  45. Kara, P., Pezaris, J. S., Yurgenson, S. & Reid, R. C. The spatial receptive field of thalamic inputs to single cortical simple cells revealed by the interaction of visual and electrical stimulation. Proc. Natl Acad. Sci. USA 99, 16261–16266 (2002).

    Article  CAS  Google Scholar 

  46. Kumbhani, R. D., Nolt, M. J. & Palmer, L. A. Precision, reliability, and information-theoretic analysis of visual thalamocortical neurons. J. Neurophysiol. 98, 2647–2663 (2007).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  48. Higley, M. J. & Contreras, D. Balanced excitation and inhibition determine spike timing during frequency adaptation. J. Neurosci. 26, 448–457 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Wehr, M. & Zador, A. M. Balanced inhibition underlies tuning and sharpens spike timing in auditory cortex. Nature 426, 442–446 (2003).

    Article  CAS  PubMed  Google Scholar 

  50. Bair, W. Spike timing in the mammalian visual system. Curr. Opin. Neurobiol. 9, 447–453 (1999).

    Article  CAS  PubMed  Google Scholar 

  51. Albrecht, D. G., Geisler, W. S., Frazor, R. A. & Crane, A. M. Visual cortex neurons of monkeys and cats: temporal dynamics of the contrast response function. J. Neurophysiol. 88, 888–913 (2002).

    Article  PubMed  Google Scholar 

  52. Gur, M., Beylin, A. & Snodderly, D. M. Response variability of neurons in primary visual cortex (V1) of alert monkeys. J. Neurosci. 17, 2914–2920 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Kara, P., Reinagel, P. & Reid, R. C. Low response variability in simultaneously recorded retinal, thalamic, and cortical neurons. Neuron 27, 635–646 (2000).

    Article  CAS  PubMed  Google Scholar 

  54. Buzsaki, G. Rhythms of the brain (Oxford Univ. Press, Oxford, 2006).

    Book  Google Scholar 

  55. Raghavachari, S. et al. Gating of human theta oscillations by a working memory task. J. Neurosci. 21, 3175–3183 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Bichot, N. P., Rossi, A. F. & Desimone, R. Parallel and serial neural mechanisms for visual search in macaque area V4. Science 308, 529–534 (2005). This paper provided support for the idea that feature-based attention is mediated by synchrony in the gamma frequency range.

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  58. Radman, T., Su, Y., An, J. H., Parra, L. C. & Bikson, M. Spike timing amplifies the effect of electric fields on neurons: implications for endogenous field effects. J. Neurosci. 27, 3030–3036 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Lin, S. C., Gervasoni, D. & Nicolelis, M. A. Fast modulation of prefrontal cortex activity by basal forebrain noncholinergic neuronal ensembles. J. Neurophysiol. 96, 3209–3219 (2006).

    Article  PubMed  Google Scholar 

  60. Rodriguez, R., Kallenbach, U., Singer, W. & Munk, M. H. Short- and long-term effects of cholinergic modulation on gamma oscillations and response synchronization in the visual cortex. J. Neurosci. 24, 10369–10378 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Makeig, S. et al. Dynamic brain sources of visual evoked responses. Science 295, 690–694 (2002).

    Article  CAS  PubMed  Google Scholar 

  62. Lakatos, P., Chen, C. M., O'Connell, M. N., Mills, A. & Schroeder, C. E. Neuronal oscillations and multisensory interaction in primary auditory cortex. Neuron 53, 279–292 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Lakatos, P. et al. An oscillatory hierarchy controlling neuronal excitability and stimulus processing in the auditory cortex. J. Neurophysiol. 94, 1904–1911 (2005).

    Article  PubMed  Google Scholar 

  64. Klausberger, T. et al. Brain-state- and cell-type-specific firing of hippocampal interneurons in vivo. Nature 421, 844–848 (2003).

    Article  CAS  PubMed  Google Scholar 

  65. Tukker, J. J., Fuentealba, P., Hartwich, K., Somogyi, P. & Klausberger, T. Cell type-specific tuning of hippocampal interneuron firing during gamma oscillations in vivo. J. Neurosci. 27, 8184–8189 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Buzsaki, G. Theta oscillations in the hippocampus. Neuron 33, 325–340 (2002).

    Article  CAS  PubMed  Google Scholar 

  67. Csicsvari, J., Hirase, H., Czurko, A., Mamiya, A. & Buzsaki, G. Oscillatory coupling of hippocampal pyramidal cells and interneurons in the behaving rat. J. Neurosci. 19, 274–287 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Lee, H., Simpson, G. V., Logothetis, N. K. & Rainer, G. Phase locking of single neuron activity to theta oscillations during working memory in monkey extrastriate visual cortex. Neuron 45, 147–156 (2005). This paper showed that phase locking in cortical area V4 during a working memory task can be more informative than the changes in the firing rate about the stimulus held in the memory.

    Article  CAS  PubMed  Google Scholar 

  69. Jacobs, J., Kahana, M. J., Ekstrom, A. D. & Fried, I. Brain oscillations control timing of single-neuron activity in humans. J. Neurosci. 27, 3839–3844 (2007). The authors of this paper presented evidence for widespread phase locking of neurons to rhythms in the delta, theta and gamma frequency ranges.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Siapas, A. G., Lubenov, E. V. & Wilson, M. A. Prefrontal phase locking to hippocampal theta oscillations. Neuron 46, 141–151 (2005). The authors of this paper showed that single units in the prefrontal cortex are locked to the LFP in the hippocampus, providing evidence for long-range communication by spike patterns.

    Article  CAS  PubMed  Google Scholar 

  71. Destexhe, A., Rudolph, M. & Pare, D. The high-conductance state of neocortical neurons in vivo. Nature Rev. Neurosci. 4, 739–751 (2003).

    Article  CAS  Google Scholar 

  72. Nir, Y. et al. Coupling between neuronal firing rate, gamma LFP, and BOLD fMRI is related to interneuronal correlations. Curr. Biol. 17, 1275–1285 (2007).

    CAS  Google Scholar 

  73. Fellous, J. M. et al. Recovering stimulus-related precision in the context of background oscillations with random trial-to-trial phase. Abstr. - Soc. Neurosci. 394.1 (2007).

    Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  75. Ikegaya, Y. et al. Synfire chains and cortical songs: temporal modules of cortical activity. Science 304, 559–564 (2004).

    Article  CAS  PubMed  Google Scholar 

  76. Aviel, Y., Mehring, C., Abeles, M. & Horn, D. On embedding synfire chains in a balanced network. Neural Comput. 15, 1321–1340 (2003).

    Article  CAS  PubMed  Google Scholar 

  77. Vogels, T. P. & Abbott, L. F. Signal propagation and logic gating in networks of integrate-and-fire neurons. J. Neurosci. 25, 10786–10795 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Cossart, R., Aronov, D. & Yuste, R. Attractor dynamics of network UP states in the neocortex. Nature 423, 283–288 (2003).

    Article  CAS  PubMed  Google Scholar 

  80. Mokeichev, A. et al. Stochastic emergence of repeating cortical motifs in spontaneous membrane potential fluctuations in vivo. Neuron 53, 413–425 (2007).

    Article  CAS  PubMed  Google Scholar 

  81. Larkman, A. & Mason, A. Correlations between morphology and electrophysiology of pyramidal neurons in slices of rat visual-cortex.I. Establishment of cell classes. J. Neurosci. 10, 1407–1414 (1990).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Stepanyants, A. & Chklovskii, D. B. Neurogeometry and potential synaptic connectivity. Trends Neurosci. 28, 387–394 (2005).

    Article  CAS  PubMed  Google Scholar 

  83. Koch, C. Biophysics of Computation (Oxford Univ. Press, Oxford, 1999).

    Google Scholar 

  84. Stuart, G., Spruston, N. & Hausser, M. (eds) Dendrites (Oxford Univ. Press, Oxford, 1999).

    Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  86. Migliore, M. & Shepherd, G. M. Emerging rules for the distributions of active dendritic conductances. Nature Rev. Neurosci. 3, 362–370 (2002).

    Article  CAS  Google Scholar 

  87. Magee, J. C. Dendritic integration of excitatory synaptic input. Nature Rev. Neurosci. 1, 181–190 (2000).

    CAS  Google Scholar 

  88. Polsky, A., Mel, B. W. & Schiller, J. Computational subunits in thin dendrites of pyramidal cells. Nature Neurosci. 7, 621–627 (2004).

    CAS  Google Scholar 

  89. Cash, S. & Yuste, R. Linear summation of excitatory inputs by CA1 pyramidal neurons. Neuron 22, 383–394 (1999).

    Article  CAS  PubMed  Google Scholar 

  90. Poirazi, P., Brannon, T. & Mel, B. W. Pyramidal neuron as two-layer neural network. Neuron 37, 989–999 (2003).

    Article  CAS  PubMed  Google Scholar 

  91. Gasparini, S. & Magee, J. C. State-dependent dendritic computation in hippocampal CA1 pyramidal neurons. J. Neurosci. 26, 2088–2100 (2006). In this study, two-photon scanning microscopy was used to apply realistic spatial patterns of synaptic inputs to CA1 pyramidal cells, allowing the authors to study synaptic integration. The authors showed that spatially clustered and temporally precise synaptic activation reliably elicits a precise action potential by activating a dendritic action potential.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Losonczy, A. & Magee, J. C. Integrative properties of radial oblique dendrites in hippocampal CA1 pyramidal neurons. Neuron 50, 291–307 (2006).

    Article  CAS  PubMed  Google Scholar 

  93. Douglas, R. J. & Martin, K. A. Neuronal circuits of the neocortex. Annu. Rev. Neurosci. 27, 419–451 (2004).

    CAS  Google Scholar 

  94. Gil, Z., Connors, B. W. & Amitai, Y. Efficacy of thalamocortical and intracortical synaptic connections: quanta, innervation, and reliability. Neuron 23, 385–397 (1999).

    Article  CAS  PubMed  Google Scholar 

  95. Ahmed, B., Anderson, J. C., Douglas, R. J., Martin, K. A. & Nelson, J. C. Polyneuronal innervation of spiny stellate neurons in cat visual cortex. J. Comp. Neurol. 341, 39–49 (1994).

    CAS  Google Scholar 

  96. Stratford, K. J., Tarczy-Hornoch, K., Martin, K. A., Bannister, N. J. & Jack, J. J. Excitatory synaptic inputs to spiny stellate cells in cat visual cortex. Nature 382, 258–261 (1996).

    Article  CAS  PubMed  Google Scholar 

  97. Kara, P. & Reid, R. C. Efficacy of retinal spikes in driving cortical responses. J. Neurosci. 23, 8547–8557 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Bruno, R. M. & Sakmann, B. Cortex is driven by weak but synchronously active thalamocortical synapses. Science 312, 1622–1627 (2006). The authors of this paper used an innovative technique to show that although the synapses from thalamocortical projection cells make up only a small fraction of the synapses on layer 4 SSCs, and have only an average unitary strength, they are effective in driving the cells because of their synchronous activation.

    Article  CAS  PubMed  Google Scholar 

  99. Berry, M. & Meister, M. Synchronous thalamic inputs drive cortical neurons reliably when excitatory and inhibitory inputs are balanced. Abstr. - Soc. Neurosci. 394.19 (2007).

  100. DeWeese, M. R. & Zador, A. M. Non-Gaussian membrane potential dynamics imply sparse, synchronous activity in auditory cortex. J. Neurosci. 26, 12206–12218 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Hirsch, J. A. et al. Synaptic physiology of the flow of information in the cat's visual cortex in vivo. J. Physiol. 540, 335–350 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Wirth, C. & Luscher, H. R. Spatiotemporal evolution of excitation and inhibition in the rat barrel cortex investigated with multielectrode arrays. J. Neurophysiol. 91, 1635–1647 (2004).

    Article  PubMed  CAS  Google Scholar 

  103. Beierlein, M., Fall, C. P., Rinzel, J. & Yuste, R. Thalamocortical bursts trigger recurrent activity in neocortical networks: layer 4 as a frequency-dependent gate. J. Neurosci. 22, 9885–9894 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Petersen, C. C. & Sakmann, B. Functionally independent columns of rat somatosensory barrel cortex revealed with voltage-sensitive dye imaging. J. Neurosci. 21, 8435–8446 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Lubke, J. & Feldmeyer, D. Excitatory signal flow and connectivity in a cortical column: focus on barrel cortex. Brain Struct. Funct. 212, 3–17 (2007).

    Google Scholar 

  106. Sarid, L., Bruno, R., Sakmann, B., Segev, I. & Feldmeyer, D. Modeling a layer 4-to-layer 2/3 module of a single column in rat neocortex: interweaving in vitro and in vivo experimental observations. Proc. Natl Acad. Sci. USA 104, 16353–16358 (2007).

    Article  CAS  Google Scholar 

  107. Thomson, A. M. & Bannister, A. P. Interlaminar connections in the neocortex. Cereb. Cortex 13, 5–14 (2003).

    Article  PubMed  Google Scholar 

  108. Haeusler, S. & Maass, W. A statistical analysis of information-processing properties of lamina-specific cortical microcircuit models. Cereb. Cortex 17, 149–162 (2007).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  110. Whittington, M. A., Traub, R. D. & Jefferys, J. G. Synchronized oscillations in interneuron networks driven by metabotropic glutamate receptor activation. Nature 373, 612–615 (1995).

    Article  CAS  PubMed  Google Scholar 

  111. Fisahn, A., Pike, F., Buhl, E. & Paulsen, O. Cholinergic induction of network oscillations at 40 Hz in the hippocampus in vitro. Nature 394, 186–189 (1998).

    Article  CAS  PubMed  Google Scholar 

  112. Hasenstaub, A. et al. Inhibitory postsynaptic potentials carry synchronized frequency information in active cortical networks. Neuron 47, 423–435 (2005). This paper showed that inhibitory conductance fluctuations have more power in the gamma frequency range than excitatory fluctuations, thus providing support for the role of inhibition in controlling the spike times.

    Article  CAS  PubMed  Google Scholar 

  113. Rieke, F., Warland, D., de Ruyter van Steveninck, R. R. & Bialek, W. Spikes: Exploring the Neural Code (MIT press, Cambridge, Massachusetts, 1997).

    Google Scholar 

  114. Rudolph, M., Pospischil, M., Timofeev, I. & Destexhe, A. Inhibition determines membrane potential dynamics and controls action potential generation in awake and sleeping cat cortex. J. Neurosci. 27, 5280–5290 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Tiesinga, P. H. E. & Sejnowski, T. J. Rapid temporal modulation of synchrony by competition in cortical interneuron networks. Neural Comput. 16, 251–275 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Tiesinga, P. H., Fellous, J. M., Salinas, E., Jose, J. V. & Sejnowski, T. J. Inhibitory synchrony as a mechanism for attentional gain modulation. J. Physiol. (Paris) 98, 296–314 (2004).

    Article  Google Scholar 

  117. Tiesinga, P. H. E. Stimulus competition by inhibitory interference. Neural Comput. 17, 2421–2453 (2005).

    Article  PubMed  Google Scholar 

  118. Buia, C. & Tiesinga, P. Attentional modulation of firing rate and synchrony in a model cortical network. J. Comput. Neurosci. 20, 247–264 (2006).

    Article  PubMed  Google Scholar 

  119. Mishra, J., Fellous, J. M. & Sejnowski, T. J. Selective attention through phase relationship of excitatory and inhibitory input synchrony in a model cortical neuron. Neural Netw. 19, 1329–1346 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  120. Niebur, E., Koch, C. & Rosin, C. An oscillation-based model for the neuronal basis of attention. Vision Res. 33, 2789–2802 (1993).

    Article  CAS  PubMed  Google Scholar 

  121. 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).

    CAS  Google Scholar 

  122. Mitchell, J. F., Sundberg, K. A. & Reynolds, J. H. Differential attention-dependent response modulation across cell classes in macaque visual area V4. Neuron 55, 131–141 (2007). The authors of this paper distinguished the spike trains of putative interneurons from those of putative excitatory neurons using the spike waveform. They found that, in absolute terms, interneurons are more strongly modulated by attention than excitatory cells.

    Article  CAS  PubMed  Google Scholar 

  123. McAdams, C. J. & Maunsell, J. H. 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 

  124. Salinas, E. & Thier, P. Gain modulation: a major computational principle of the central nervous system. Neuron 27, 15–21 (2001).

    Article  Google Scholar 

  125. Hopfield, J. J. Pattern recognition computation using action potential timing for stimulus representation. Nature 376, 33–36 (1995).

    Article  CAS  PubMed  Google Scholar 

  126. Tiesinga, P. H. E., Fellous, J. M., Jose, J. V. & Sejnowski, T. J. Information transfer in entrained cortical neurons. Network 13, 41–66 (2002).

    Article  CAS  PubMed  Google Scholar 

  127. 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 

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

    Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  130. Womelsdorf, T. et al. Modulation of neuronal interactions through neuronal synchronization. Science 316, 1609–1612 (2007). Local cortical networks project to a large number of other local networks. The authors of this study observed that communication seems to be selective, because each local network has a different phase of gamma oscillation and only pairs of networks with a good phase relationship can exchange information.

    Article  CAS  PubMed  Google Scholar 

  131. Canolty, R. T. et al. High gamma power is phase-locked to theta oscillations in human neocortex. Science 313, 1626–1628 (2006). This study reported the existence of spatially-specific and task-dependent correlations between the amplitude of high-gamma rhythms and the phase of theta oscillations.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Salinas, E. & Sejnowski, T. J. Correlated neuronal activity and the flow of neural information. Nature Rev. Neurosci. 2, 539–550 (2001).

    Article  CAS  Google Scholar 

  133. Lima-Mainen, S., Hromadka, T., Zhang, F., Deisseroth, K. & Zador, A. M. Identifying neurons with Channelrhodopsin-2 during in vivo electrophysiology in rodents. Abstr. - Soc. Neurosci. 99.3 (2007).

  134. Wickersham, I. R. et al. Monosynaptic restriction of transsynaptic tracing from single, genetically targeted neurons. Neuron 53, 639–647 (2007). This study presented a new anatomical technique for finding all the neurons that project to a given neuron, thereby providing constraints on cortical circuits.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  135. Schreiber, S., Fellous, J. M., Whitmer, D., Tiesinga, P. & Sejnowski, T. J. A new correlation-based measure of spike timing reliability. Neurocomputing 52–54, 925–931 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  136. Wiskott, L., Fellous, J. M., Kruger, N. & von der Malsburg, C. Face recognition by elastic bunch graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 19, 775–779 (1997).

    Article  Google Scholar 

  137. Victor, J. D. & Purpura, K. P. Nature and precision of temporal coding in visual cortex: a metric-space analysis. J. Neurophysiol. 76, 1310–1326 (1996).

    Article  CAS  PubMed  Google Scholar 

  138. van Rossum, M. C. A novel spike distance. Neural Comput. 13, 751–763 (2001).

    Article  CAS  PubMed  Google Scholar 

  139. Bezdek, J. C. Pattern recognition with fuzzy objective function algorithms (Plenum, New York, 1981).

    Book  Google Scholar 

  140. Larkum, M. E., Waters, J., Sakmann, B. & Helmchen, F. Dendritic spikes in apical dendrites of neocortical layer 2/3 pyramidal neurons. J. Neurosci. 27, 8999–9008 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Larkum, M. E., Zhu, J. J. & Sakmann, B. Dendritic mechanisms underlying the coupling of the dendritic with the axonal action potential initiation zone of adult rat layer 5 pyramidal neurons. J. Physiol. 533, 447–466 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank P. J. Thomas, S. Schreiber, D. Spencer, H. -P. Wang, J. V. Toups and J. V. José for their contributions to the research presented in this Review. This work was supported by the Human Frontier Science Program (P.T.), US National Institutes of Health grant R01 MH068481 (T.J.S. & P.T.) and the Howard Hughes Medical Institute (T.J.S.).

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Authors and Affiliations

  1. Physics and Astronomy Department, University of North Carolina at, Chapel Hill, 27599-3255, North Carolina, USA

    Paul Tiesinga

  2. Psychology and Applied Mathematics Departments, University of Arizona at, Tucson, 85721-0068, Arizona, USA

    Jean-Marc Fellous

  3. Computational Neurobiology Laboratory and Howard Hughes Medical Institute at the Salk Institute, La Jolla, 92037, California, USA

    Terrence J. Sejnowski

  4. Division of Biological Sciences, University of California at San Diego, La Jolla, 92039-0348, California, USA

    Terrence J. Sejnowski

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  1. Paul Tiesinga
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Glossary

Spike time

The time of occurrence of an action potential, relative to stimulus onset or another event.

Spike volleys

A set of spikes emitted at approximately the same time (typically with a temporal spread of between 1 and 10 ms) by a pool of neurons.

Feedforward information

In the context of stimulus–response circuitry, feedforward information is information that is processed in a single direction — from sensory input through perceptual analysis to motor output — without involving feedback information flowing backwards from 'higher' centres to 'lower' centres.

Top-down information

The flow of information from 'higher' to 'lower' centres, conveying knowledge derived from previous experience rather than from sensory stimulation.

Spike-time histogram

A tool for resolving the behaviour of the firing rate as a function of time, by averaging across multiple trials or multiple neurons. Mathematically, it is obtained by counting the number of spikes in each time bin and normalizing the count by the bin width, the number of trials and/or the number of neurons.

Event

A time-point relative to the stimulus onset during which a spike is found on a significant fraction of the trials.

Neuromodulator

An endogenous chemical substance that changes the intrinsic properties of a neuron and the dynamics and strength of neurotransmission. Neuromodulators can modify neuronal responses to synaptic inputs on potentially long timescales.

Afterhyperpolarization

The membrane hyperpolarization that follows the occurrence of one or several action potentials.

Eye-cup preparation

A preparation in which the retina is extracted intact so that the neural responses to activation of the photoreceptors by a visual stimulus can be recorded.

Local field potential

(LFP). The total electrical current in the vicinity of the recording electrode, reflecting the sum of events in the dendrites of a local neuronal population. It is often obtained by low-pass filtering (that is, removal of signals lower than 600 Hz ) of the recorded electrical signal.

Compartmental model

A computer model that breaks a neuron down into small electrical compartments and can simulate the propagation of electrical signals inside the neuron and across its membrane surface.

Cortical pyramidal cell

A class of neuron in the cerebral cortex with a pyramid-shaped cell body. These neurons have dendrites that extend locally and can project their axonal processes both locally and distally across many layers and brain areas.

Caged glutamate

An inactive derivative of glutamate that can be transformed into the active transmitter, usually by photolysis. This technique provides an efficient means for achieving a spatially restricted application of glutamate.

Dendritic action potential

(dAP). An action potential that is first generated in the dendrites and which then propagates towards the soma, often but not always eliciting a somatic action potential after a brief delay.

Relay cell

A type of cell in the thalamus that sends its axon to the cortex. Relay cells in the lateral geniculate nucleus receive inputs from the retina and project to spiny stellate cells in layer 4 of the primary visual cortex.

Spiny stellate cells

(SSCs). An excitatory cell type that is common in layer 4 of the sensory cortex. SSCs have axons that have a local arborization pattern and have dendrites that are covered by spines.

Basket cell

A type of interneuron that sends its axon to the cell body of the postsynaptic cell and surrounds it with a structure akin to a basket.

Dynamic clamp

A technique by which the effect of opening ionic channels (a conductance change) is simulated by injecting into a real neuron a current that is proportional to the neuron's membrane potential.

Network model

A model comprised of neurons connected by synapses that is used to study the effects of synaptic coupling on the dynamics of neural activity.

Selective attention

A cognitive process that is involved in selecting stimuli based on their behavioural relevance.

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Tiesinga, P., Fellous, JM. & Sejnowski, T. Regulation of spike timing in visual cortical circuits. Nat Rev Neurosci 9, 97–107 (2008). https://doi.org/10.1038/nrn2315

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