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The Possible Role of Spike Patterns in Cortical Information Processing

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Abstract

When the same visual stimulus is presented across many trials, neurons in the visual cortex receive stimulus-related synaptic inputs that are reproducible across trials (S) and inputs that are not (N). The variability of spike trains recorded in the visual cortex and their apparent lack of spike-to-spike correlations beyond that implied by firing rate fluctuations, has been taken as evidence for a low S/N ratio. A recent re-analysis of in vivo cortical data revealed evidence for spike-to-spike correlations in the form of spike patterns. We examine neural dynamics at a higher S/N in order to determine what possible role spike patterns could play in cortical information processing. In vivo-like spike patterns were obtained in model simulations. Superpositions of multiple sinusoidal driving currents were especially effective in producing stable long-lasting patterns. By applying current pulses that were either short and strong or long and weak, neurons could be made to switch from one pattern to another. Cortical neurons with similar stimulus preferences are located near each other, have similar biophysical properties and receive a large number of common synaptic inputs. Hence, recordings of a single neuron across multiple trials are usually interpreted as the response of an ensemble of these neurons during one trial. In the presence of distinct spike patterns across trials there is ambiguity in what would be the corresponding ensemble, it could consist of the same spike pattern for each neuron or a set of patterns across neurons. We found that the spiking response of a neuron receiving these ensemble inputs was determined by the spike-pattern composition, which, in turn, could be modulated dynamically as a means for cortical information processing.

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References

  • Baker SN, Lemon RN (2000) Precise spatiotemporal repeating patterns in monkey primary and supplementary motor areas occur at chance levels. J. Neurophysiol. 84: 1770–1780.

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  • Brecht M, Schneider M, Sakmann B, Margrie TW (2004) Whisker movements evoked by stimulation of single pyramidal cells in rat motor cortex. Nature 427: 704–710.

    Google Scholar 

  • Brette R, Guigon E (2003) Reliability of spike timing is a general property of spiking model neurons. Neural Computation. 15: 279–308.

    Google Scholar 

  • Brunel N, Chance FS, Fourcaud N, Abbott LF (2001) Effects of synaptic noise and filtering on the frequency response of spiking neurons. Phys. Rev. Lett. 86: 2186–2189.

    Google Scholar 

  • Bryant HL, Segundo JP (1976) Spike Initiation by Transmembrane current: A white-noise analysis. J. Physiol. 260: 279– 314.

    Google Scholar 

  • Buracas GT, Zador AM, DeWeese MR, Albright TD (1998) Efficient discrimination of temporal patterns by motion-sensitive neurons in primate visual cortex. Neuron 20: 959–969.

    Google Scholar 

  • Cecchi G, Sigman M, Alonso J, Martinez L, Chialvo D, Magnasco M (2000) Noise in neurons is message dependent. Proc. Natl. Acad. Sci. 97: 5557–5561.

    Google Scholar 

  • Coombes S, Bressloff PC (1999) Mode locking and Arnold tongues in integrate-and-fire neural oscillators. Phys. Rev. E. 60: 2086–2096.

    Google Scholar 

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

    Google Scholar 

  • Dayan P, Abbott LF (2001) Theoretical Neuroscience. MIT Press, Cambridge.

    Google Scholar 

  • de Ruyter van Steveninck RR, Lewen GD, Strong SP, Koberle R, Bialek W (1997) Reproducibility and variability in neural spike trains. Science 275: 1805–1808.

    Google Scholar 

  • Deweese MR, Zador AM (2004) Shared and private variability in the auditory cortex. J. Neurophysiol. 92: 1840–1855.

    Google Scholar 

  • Fellous JM, Houweling AR, Modi RH, Rao RPN, Tiesinga PHE, Sejnowski TJ (2001) Frequency dependence of spike timing reliability in cortical pyramidal cells and interneurons. J. Neurophysiol. 85: 1782–1787.

    Google Scholar 

  • Fellous J-M, Thomas PJ, Tiesinga PH, Sejnowski TJ (2004) Discovering spike patterns in neuronal responses. J. Neurosci. 24: 2989–3001.

    Google Scholar 

  • Fourcaud-Trocme N, Hansel D, van Vreeswijk C, Brunel N (2003) How spike generation mechanisms determine the neuronal response to fluctuating inputs. J. Neurosci. 23: 11628– 11640.

    Google Scholar 

  • Gardiner CW (1985) Handbook of Stochastic Methods, 2nd edition. Springer, Berlin.

    Google Scholar 

  • Gerald CF, Wheatley PO (1999) Applied Numerical Analysis, 6th edition. Addison-Wesley, Reading, California.

    Google Scholar 

  • Gutkin B, Ermentrout GB, Rudolph M (2003) Spike generating dynamics and the conditions for spike-time precision in cortical neurons. J. Compu. Neurosci. 15: 91–103.

    Google Scholar 

  • Haas JS, White JA (2002) Frequency selectivity of layer II stellate cells in the medial entorhinal cortex. J. Neurophysiol. 88: 2422–2429.

    Google Scholar 

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

    Google Scholar 

  • Hunter JD, Milton JG (2003) Amplitude and frequency dependence of spike timing: Implications for dynamic regulation. J. Neurophysiol. 90: 387–394.

    Google Scholar 

  • Ikegaya Y, Aaron G, Cossart R, Aronov D, Lampl I, Ferster D, Yuste R (2004) Synfire chains and cortical songs: Temporal modules of cortical activity. Science 304: 559–564.

    Google Scholar 

  • Izhikevich EM, Gally JA, Edelman GM (2004) Spike-timing Dynamics of Neuronal Groups. Cereb Cortex. 14: 933–944.

    Google Scholar 

  • Jensen RV (1998) Synchronization of randomly driven nonlinear oscillators. Phys. Rev. E. 58: R6907–R6910.

    Google Scholar 

  • Jensen RV (2002) Synchronization of driven nonlinear oscillators. Amer. J. Phys. 70: 607–619.

    Google Scholar 

  • Knight BW (1972a) Dynamics of encoding in a population of neurons. J. Gen. Physiol. 59: 734–766.

    Google Scholar 

  • Knight BW (1972b) The relationship between the firing rate of a single neuron and the level of activity in a population of neurons. Experimental evidence for resonant enhancement in the population response. J. Gen. Physiol. 59: 767–778.

    Google Scholar 

  • Koch C (1999) Biophysics of Computation. Oxford: Oxford University Press.

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  • Nowak LG, Sanchez-Vives MV, McCormick DA (1997) Influence of low and high frequency inputs on spike timing in visual cortical neurons. Cereb Cortex 7: 487–501.

    Google Scholar 

  • Oram MW, Wiener MC, Lestienne R, Richmond BJ (1999) Stochastic nature of precisely timed spike patterns in visual system neuronal responses. J. Neurophysiol 81: 3021–3033.

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  • Reyes AD (2003) Synchrony-dependent propagation of firing rate in iteratively constructed networks in vitro. Nat. Neurosci. 6: 593–599.

    Google Scholar 

  • Risken H (1989) The Fokker-Planck Equation, 2nd edition. Springer, Berlin.

    Google Scholar 

  • Ritt J (2003) Evaluation of entrainment of a nonlinear neural oscillator to white noise. Phys. Rev. E. 68: 041915.

    Google Scholar 

  • Rudolph M, Destexhe A (2003) Tuning neocortical pyramidal neurons between integrators and coincidence detectors. J. Comput. Neurosci. 14: 239–251.

    Google Scholar 

  • Schreiber S, Fellous JM, Tiesinga P, Sejnowski TJ (2004) Influence of ionic conductances on spike timing reliability of cortical neurons for suprathreshold rhythmic inputs. J. Neurophysiol. 91: 194–205.

    Google Scholar 

  • Shepherd G (1998) Synaptic Organization of the Brain, 4th ed.

  • Szucs A, Vehovszky A, Molnar G, Pinto RD, Abarbanel HD (2004) Reliability and precision of neural spike timing: simulation of spectrally broadband synaptic inputs. Neurosci. 126: 1063– 1073.

    Google Scholar 

  • Tiesinga P, Fellous J-M, Salinas E, Jose JV, Sejnowski T (2004) Synchronization as a mechanism for attentional gain modulation. Neurocomput. 58-60: 641–646.

    Google Scholar 

  • Tiesinga PHE (2002) Precision and reliability of periodically and quasiperiodically driven integrate-and-fire neurons. Phys. Rev. E. 65: Art. no. 041913.

    Google Scholar 

  • Tiesinga PHE (2004) Chaos-induced modulation of reliability boosts output firing rate in downstream cortical areas. Phys. Rev. E. 69: 031912.

    Google Scholar 

  • Tiesinga PHE, Jose JV (2000) Robust gamma oscillations in networks of inhibitory hippocampal interneurons. Network-Comput. Neural Syst. 11: 1–23.

    Google Scholar 

  • Tiesinga PHE, Fellous JM, Sejnowski TJ (2002) Attractor reliability reveals deterministic structure in neuronal spike trains. Neural Comput. 14: 1629–1650.

    Google Scholar 

  • Toups JV, Fellous J-M, Tiesinga P (2004) Statistical validation of spike patterns revealed by fuzzy clustering algorithms. 2004 Abstracts Society for Neuroscience: 984.922.

  • Wang XJ, Buzsaki G (1996) Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model. J. Neurosci. 16: 6402–6413.

    Google Scholar 

  • Zhang LI, Tao HW, Holt CE, Harris WA, Poo M (1998) A critical window for cooperation and competition among developing retinotectal synapses. Nature. 395: 37–44.

    Google Scholar 

Download references

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Tiesinga, P.H.E., Toups, J.V. The Possible Role of Spike Patterns in Cortical Information Processing. J Comput Neurosci 18, 275–286 (2005). https://doi.org/10.1007/s10827-005-0330-2

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  • DOI: https://doi.org/10.1007/s10827-005-0330-2

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