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In vivo conditions influence the coding of stimulus features by bursts of action potentials

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Abstract

The functional role of burst firing (i.e. the firing of packets of action potentials followed by quiescence) in sensory processing is still under debate. Should bursts be considered as unitary events that signal the presence of a particular feature in the sensory environment or is information about stimulus attributes contained within their temporal structure? We compared the coding of stimulus attributes by bursts in vivo and in vitro of electrosensory pyramidal neurons in weakly electric fish by computing correlations between burst and stimulus attributes. Our results show that, while these correlations were strong in magnitude and significant in vitro, they were actually much weaker in magnitude if at all significant in vivo. We used a mathematical model of pyramidal neuron activity in vivo and showed that such a model could reproduce the correlations seen in vitro, thereby suggesting that differences in burst coding were not due to differences in bursting seen in vivo and in vitro. We next tested whether variability in the baseline (i.e. without stimulation) activity of ELL pyramidal neurons could account for these differences. To do so, we injected noise into our model whose intensity was calibrated to mimic baseline activity variability as quantified by the coefficient of variation. We found that this noise caused significant decreases in the magnitude of correlations between burst and stimulus attributes and could account for differences between in vitro and in vivo conditions. We then tested this prediction experimentally by directly injecting noise in vitro through the recording electrode. Our results show that this caused a lowering in magnitude of the correlations between burst and stimulus attributes in vitro and gave rise to values that were quantitatively similar to those seen under in vivo conditions. While it is expected that noise in the form of baseline activity variability will lower correlations between burst and stimulus attributes, our results show that such variability can account for differences seen in vivo. Thus, the high variability seen under in vivo conditions has profound consequences on the coding of information by bursts in ELL pyramidal neurons. In particular, our results support the viewpoint that bursts serve as a detector of particular stimulus features but do not carry detailed information about such features in their structure.

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References

  • Arganda, S., Guantes, R., & de Polavieja, G. G. (2007). Sodium pumps adapt spike bursting to stimulus statistics. Nature Neuroscience, 10, 1467–1473.

    Article  PubMed  CAS  Google Scholar 

  • Avila Akerberg, O., Krahe, R., & Chacron, M. J. (2010). Neural heterogeneities and stimulus properties affect burst coding in vivo. Neuroscience, 168, 300–313.

    Article  PubMed  CAS  Google Scholar 

  • Bastian, J., & Nguyenkim, J. (2001). Dendritic Modulation of Burst-like firing in sensory neurons. Journal of Neurophysiology, 85, 10–22.

    PubMed  CAS  Google Scholar 

  • Bastian, J., Chacron, M. J., & Maler, L. (2002). Receptive field organization determines pyramidal cell stimulus-encoding capability and spatial stimulus selectivity. The Journal of Neuroscience, 22, 4577–4590.

    PubMed  CAS  Google Scholar 

  • Bastian, J., Chacron, M. J., & Maler, L. (2004). Plastic and non-plastic cells perform unique roles in a network capable of adaptive redundancy reduction. Neuron, 41, 767–779.

    Article  PubMed  CAS  Google Scholar 

  • Berman, N. J., & Maler, L. (1998). A inhibition evoked from primary afferents in the electrosensory lateral line lobe of the weakly electric fish (Apteronotus leptorhynchus). Journal of Neurophysiology, 80, 3173–3196.

    PubMed  CAS  Google Scholar 

  • Berman, N. J., & Maler, L. (1999). Neural architecture of the electrosensory lateral line lobe: adaptations for coincidence detection, a sensory searchlight and frequency-dependent adaptive filtering. The Journal of Experimental Biology, 202, 1243–1253.

    PubMed  Google Scholar 

  • Bullock, T. H., Hopkins, C. D., Popper, A. N., & Fay, R. R. (2005). Electroreception. New York: Springer.

    Book  Google Scholar 

  • Chacron, M. J. (2006). Nonlinear information processing in a model sensory system. Journal of Neurophysiology, 95, 2933–2946.

    Article  PubMed  Google Scholar 

  • Chacron, M. J., & Bastian, J. (2008). Population coding by electrosensory neurons. Journal of Neurophysiology, 99, 1825–1835.

    Article  PubMed  Google Scholar 

  • Chacron, M. J., Doiron, B., Maler, L., Longtin, A., & Bastian, J. (2003). Non-classical receptive field mediates switch in a sensory neuron’s frequency tuning. Nature, 423, 77–81.

    Article  PubMed  CAS  Google Scholar 

  • Chacron, M. J., Longtin, A., & Maler, L. (2004). To burst or not to burst? J Comp Neurosci, 17, 127–136.

    Article  Google Scholar 

  • Chacron, M. J., Lindner, B., & Longtin, A. (2004). Noise shaping by interval correlations increases information transfer. Physical Review Letters, 92, 080601.

    Article  PubMed  Google Scholar 

  • Chacron, M. J., Maler, L., & Bastian, J. (2005a). Feedback and feedforward control of frequency tuning to naturalistic stimuli. The Journal of Neuroscience, 25, 5521–5532.

    Article  PubMed  CAS  Google Scholar 

  • Chacron, M. J., Longtin, A., & Maler, L. (2005b). Delayed excitatory and inhibitory feedback shape neural information transmission. Physical Review E, 72, 051917.

    Article  Google Scholar 

  • Chacron, M. J., Maler, L., & Bastian, J. (2005c). Electroreceptor neuron dynamics shape information transmission. Nature Neuroscience, 8, 673–678.

    Article  PubMed  CAS  Google Scholar 

  • Chacron, M. J., Lindner, B., Longtin, A., Maler, L., & Bastian, J. (2005d). Experimental and Theoretical demonstration of noise shaping by interspike interval correlations. Proceedings of SPIE, 5841, 150–163.

    Article  Google Scholar 

  • Chacron, M. J., Lindner, B., & Longtin, A. (2007). Threshold fatigue and information transfer. J Comp Neurosci, 23, 301–311.

    Article  Google Scholar 

  • Chacron, M. J., Toporikova, N., & Fortune, E. S. (2009). Differences in the time course of short-term depression across receptive fields are correlated with directional selectivity in electrosensory neurons. Journal of Neurophysiology, 102, 3270–3279.

    Article  PubMed  Google Scholar 

  • Dayan, P., & Abbott, L. F. (2001). Theoretical neuroscience: Computational and mathematical modeling of neural systems. Cambridge: MIT Press.

    Google Scholar 

  • Dean, A. F. (1981). The variability of discharge of simple cells in the cat striate cortex. Experimental Brain Research, 44, 437–440.

    Article  CAS  Google Scholar 

  • DeBusk, B. C., DeBruyn, E. J., Snider, R. K., Kabara, J. F., & Bonds, A. B. (1997). Stimulus-dependent modulation of spike burst length in cat striate cortical cells. Journal of Neurophysiology, 78, 199–213.

    PubMed  CAS  Google Scholar 

  • Destexhe, A., & Paré, D. (1999). Impact of Network activity on the integrative properties of neocortical pyramidal neurons in vivo. Journal of Neurophysiology, 81, 1531–1547.

    PubMed  CAS  Google Scholar 

  • Destexhe, A., Rudolph, M., Fellous, J. M., & Sejnowski, T. J. (2001). Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience, 107, 13–24.

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  • Doiron, B., Laing, C., Longtin, A., & Maler, L. (2002). Ghostbursting: a novel neuronal burst mechanism. Journal of Computational Neuroscience, 12, 5–25.

    Article  PubMed  Google Scholar 

  • Doiron, B., Oswald, A. M., & Maler, L. (2007). Interval coding. II. Dendrite-dependent mechanisms.[see comment]. Journal of Neurophysiology, 97, 2744–2757.

    Article  PubMed  Google Scholar 

  • Ellis, L. D., Krahe, R., Bourque, C. W., Dunn, R. J., & Chacron, M. J. (2007). Muscarinic receptors control frequency tuning through the downregulation of an A-type potassium current. Journal of Neurophysiology, 98, 1526–1537.

    Article  PubMed  CAS  Google Scholar 

  • Ellis, L. D., Mehaffey, W. H., Harvey-Girard, E., Turner, R. W., Maler, L., & Dunn, R. J. (2007). SK channels provide a novel mechanism for the control of frequency tuning in electrosensory neurons. The Journal of Neuroscience, 27, 9491–9502.

    Article  PubMed  CAS  Google Scholar 

  • Eyherabide, H. G., Rokem, A., Herz, A. V., & Samengo, I. (2008). Burst firing is a neural code in an insect auditory system. Front Comput Neurosci, 2, 3.

    Article  PubMed  Google Scholar 

  • Fortune, E. S., & Rose, G. (1997). Passive and active membrane properties contribute to the temporal filtering properties of midbrain neurons in vivo. The Journal of Neuroscience, 17, 3815–3825.

    PubMed  CAS  Google Scholar 

  • Frank, K., & Becker, M. C. (1964). Microelectrodes for recording and stimulation. In L. Nastuk (Ed.), Physical techniques in biological research, Vol. V, Part A (pp. 22–87). New York: Academic Press.

    Google Scholar 

  • Gabbiani, F., & Koch, C. (1996). Coding of time-varying signals in spike trains of integrate-and-fire neurons with random threshold. Neural Computation, 8, 44–66.

    Article  Google Scholar 

  • Gabbiani, F., Metzner, W., Wessel, R., & Koch, C. (1996). From stimulus encoding to feature extraction in weakly electric fish. Nature, 384, 564–567.

    Article  PubMed  CAS  Google Scholar 

  • Gaudry, K. S., & Reinagel, P. (2008). Information measure for analyzing specific spiking patterns and applications to LGN bursts. Network, 19, 69–94.

    Article  PubMed  Google Scholar 

  • Hitschfeld, E. M., Stamper, S. A., Vonderschen, K., Fortune, E. S., & Chacron, M. J. (2009). Effects of restraint and immobilization on electrosensory behaviors of weakly electric fish. ILAR Journal, 50, 361–372.

    PubMed  CAS  Google Scholar 

  • Izhikevich, E. M. (2000). Neural Excitability, spiking, and bursting. International Journal of Bifurcations and Chaos, 10, 1171–1269.

    Article  Google Scholar 

  • Izhikevich, E. M., Desai, N. S., Walcott, E. C., & Hoppensteadt, F. C. (2003). Bursts as a unit of neural information: selective communication via resonance. Trends in Neurosciences, 26, 161–167.

    Article  PubMed  CAS  Google Scholar 

  • Jones, L. M., Lee, S., Trageser, J. C., Simons, D. J., & Keller, A. (2004). Precise temporal responses in whisker trigeminal neurons. Journal of Neurophysiology, 92, 665–668.

    Article  PubMed  Google Scholar 

  • Kepecs, A., Wang, X. J., & Lisman, J. (2002). Bursting neurons signal input slope. The Journal of Neuroscience, 22, 9053–9062.

    PubMed  CAS  Google Scholar 

  • Kloeden, P. E., & Platen, E. (1999). Numerical solutions of stochastic differential equations. Berlin: Springer.

    Google Scholar 

  • Krahe, R., & Gabbiani, F. (2004). Burst firing in sensory systems. Nature Reviews. Neuroscience, 5, 13–23.

    Article  PubMed  CAS  Google Scholar 

  • Krahe, R., Bastian, J., & Chacron, M. J. (2008). Temporal processing across multiple topographic maps in the electrosensory system. Journal of Neurophysiology, 100, 852–867.

    Article  PubMed  Google Scholar 

  • Lemon, N., & Turner, R. W. (2000). Conditional spike backpropagation generates burst discharge in a sensory neuron. Journal of Neurophysiology, 84, 1519–1530.

    PubMed  CAS  Google Scholar 

  • Lesica, N. A., & Stanley, G. B. (2004). Encoding of natural scene movies by tonic and burst spikes in the lateral geniculate nucleus. The Journal of Neuroscience, 24, 10731–10740.

    Article  PubMed  CAS  Google Scholar 

  • Lindner, B., Chacron, M. J., & Longtin, A. (2005). Integrate-and-fire neurons with threshold noise: a tractable model of how interspike interval correlations affect neuronal signal transmission. Physical Review. E, 72, 021911.

    Article  Google Scholar 

  • Lisman, J. E. (1997). Bursts as a unit of neural information: making unreliable synapses reliable. Trends in Neurosciences, 20, 38–43.

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  • Manwani, A., & Koch, C. (1999). Detecting and estimating signals in noisy cable structure, I: neuronal noise sources. Neural Computation, 11, 1797–1829.

    Article  PubMed  CAS  Google Scholar 

  • Marsat, G., & Maler, L. (2010). Neural heterogeneity and efficient population codes for communication signals. Journal of Neurophysiology, 104, 2543–2555.

    Article  PubMed  Google Scholar 

  • Marsat, G., & Pollack, G. S. (2006). A behavioral role for feature detection by sensory bursts. The Journal of Neuroscience, 26, 10542–10547.

    Article  PubMed  CAS  Google Scholar 

  • Marsat, G., & Pollack, G. S. (2010). The structure and size of sensory bursts encode stimulus information but only size affects behavior. Journal of Comparative Physiology. A: Neuroethology, Sensory, Neural, and Behavioral Physiology, 196, 315–320.

    Article  PubMed  Google Scholar 

  • Marsat, G., Proville, R. D., & Maler, L. (2009). Transient signals trigger synchronous bursts in an identified population of neurons. Journal of Neurophysiology, 102, 714–723.

    Article  PubMed  Google Scholar 

  • Martinez-Conde, S., Macknik, S. L., & Hubel, D. H. (2002). The function of bursts of spikes during visual fixation in the awake primate lateral geniculate nucleus and primary visual cortex. Proceedings of the National Academy of Sciences of the United States of America, 99, 13920–13925.

    Article  PubMed  CAS  Google Scholar 

  • Mayer, M. L., & Westbrook, G. L. (1987). Permeation and block of N-methyl-D-aspartic acid receptor channels by divalent cations in mouse cultured central neurones. Journal de Physiologie, 394, 501–527.

    CAS  Google Scholar 

  • Mehaffey, W. H., Maler, L., & Turner, R. W. (2008). Intrinsic frequency tuning in ELL pyramidal cells varies across electrosensory maps. Journal of Neurophysiology, 99, 2641–2655.

    Article  PubMed  Google Scholar 

  • Mehaffey, W. H., Ellis, L. D., Krahe, R., Dunn, R. J., & Chacron, M. J. (2008). Ionic and neuromodulatory regulation of burst discharge controls frequency tuning. Journal of Physiology - Paris, 102, 195–208.

    Article  Google Scholar 

  • Metzner, W., & Juranek, J. (1997). A sensory brain map for each behavior? PNAS, 94, 14798–14803.

    Article  PubMed  CAS  Google Scholar 

  • Metzner, W., Koch, C., Wessel, R., & Gabbiani, F. (1998). Feature extraction by burst-like spike patterns in multiple sensory maps. The Journal of Neuroscience, 18, 2283–2300.

    PubMed  CAS  Google Scholar 

  • Noonan, L., Doiron, B., Laing, C., Longtin, A., & Turner, R. W. (2003). A dynamic dendritic refractory period regulates burst discharge in the electrosensory lobe of weakly electric fish. The Journal of Neuroscience, 23, 1524–1534.

    PubMed  CAS  Google Scholar 

  • Nowak, L., Bregestovski, P., Ascher, P., Herbet, A., & Prochiantz, A. (1984). Magnesium gates glutamate-activated channels in mouse central neurones. Nature, 307, 462–465.

    Article  PubMed  CAS  Google Scholar 

  • Oswald, A. M. M., Chacron, M. J., Doiron, B., Bastian, J., & Maler, L. (2004). Parallel processing of sensory input by bursts and isolated spikes. The Journal of Neuroscience, 24, 4351–4362.

    Article  PubMed  CAS  Google Scholar 

  • Oswald, A. M., Doiron, B., & Maler, L. (2007). Interval coding. I. Burst interspike intervals as indicators of stimulus intensity.[see comment]. Journal of Neurophysiology, 97, 2731–2743.

    Article  PubMed  Google Scholar 

  • Reynolds, I. J., & Miller, R. J. (1990). Allosteric modulation of N-methyl-D-aspartate receptors. Advances in Pharmacology, 21, 101–126.

    Article  PubMed  CAS  Google Scholar 

  • Rinzel, J. (1987). A formal classification of bursting mechanisms in excitable systems. In E. Teramoto & M. Yamaguti (Eds.), Mathematical Topics in Population Biology, Morphogenesis and Neurosciences, Lecture Notes in Biomathematics 71 (pp. 267–281). New York: Springer-Verlag.

    Google Scholar 

  • Sadeghi, S. G., Chacron, M. J., Taylor, M. C., & Cullen, K. E. (2007). Neural variability, detection thresholds, and information transmission in the vestibular system. The Journal of Neuroscience, 27, 771–781.

    Article  PubMed  CAS  Google Scholar 

  • Samengo, I., & Montemurro, M. A. (2010). Conversion of phase information into a spike-count code by bursting neurons. PLoS ONE, 5, e9669.

    Article  PubMed  Google Scholar 

  • Sherman, S. M. (2001). Tonic and burst firing: dual modes of thalamocortical relay. Trends in Neurosciences, 24, 122–126.

    Article  PubMed  CAS  Google Scholar 

  • Sherman, S. M., & Guillery, R. W. (2002). The role of the thalamus in the flow of information to the cortex. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 357, 1695–1708.

    Article  PubMed  Google Scholar 

  • Shumway, C. (1989). Multiple electrosensory maps in the medulla of weakly electric Gymnotiform fish. II. Anatomical differences. Journal of Neuroscience, 9, 4400–4415.

    PubMed  CAS  Google Scholar 

  • Stein, R. B., Gossen, E. R., & Jones, K. E. (2005). Neuronal variability: noise or part of the signal? Nature Reviews. Neuroscience, 6, 389–397.

    Article  PubMed  CAS  Google Scholar 

  • Theunissen, F., & Miller, J. P. (1995). Temporal encoding in the nervous system: a rigorous definition. Journal of Computational Neuroscience, 2, 149–162.

    Article  PubMed  CAS  Google Scholar 

  • Tolhurst, D. J., Movshon, J. A., & Dean, A. F. (1983). The statistical reliability of signals in single neurons in cat and monkey visual cortex. Vision Research, 23, 775–785.

    Article  PubMed  CAS  Google Scholar 

  • Toporikova, N., & Chacron, M. J. (2009). Dendritic SK channels gate information processing in vivo by regulating an intrinsic bursting mechanism seen in vitro. Journal of Neurophysiology, 102, 2273–2287.

    Article  PubMed  Google Scholar 

  • Turner, R. W., Maler, L., Deerinck, T., Levinson, S. R., & Ellisman, M. H. (1994). TTX-sensitive dendritic sodium channels underlie oscillatory discharge in a vertebrate sensory neuron. The Journal of Neuroscience, 14, 6453–6471.

    PubMed  CAS  Google Scholar 

  • Wagner, J., & Keizer, J. (1994). Effects of rapid buffers on Ca2+ diffusion and Ca2+ oscillations. Biophysical Journal, 67, 447–456.

    Article  PubMed  CAS  Google Scholar 

  • Wang, X. J., & Rinzel, J. (1995). Oscillatory and bursting properties of neurons. In M. A. Arbib (Ed.), The handbook of brain theory and neural networks (pp. 686–691). Cambridge: MIT Press.

    Google Scholar 

  • Wolfart, J., Debay, D., Le Masson, G., Destexhe, A., & Bal, T. (2005). Synaptic background activity controls spike transfer from thalamus to cortex. Nature Neuroscience, 8, 1760–1767.

    Article  PubMed  CAS  Google Scholar 

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Acknowledgements

This research was supported by CONACYT (O.A.A.) and CIHR, CFI, and CRC (M.J.C).

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Correspondence to Maurice J. Chacron.

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Akerberg, O.A., Chacron, M.J. In vivo conditions influence the coding of stimulus features by bursts of action potentials. J Comput Neurosci 31, 369–383 (2011). https://doi.org/10.1007/s10827-011-0313-4

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