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Variability, compensation and homeostasis in neuron and network function

Nature Reviews Neuroscience volume 7, pages 563–574 (2006)Cite this article

Key Points

  • Whereas neurons may live for scores of years, ion channels and receptors turnover in the membrane in minutes, hours, days or weeks. This means that neurons are constantly rebuilding themselves and neuronal circuits are in a constant state of molecular flux.

  • Homeostatic mechanisms that help to regulate intrinsic excitability and synaptic strength are needed to stabilize circuit performance.

  • Computational models have demonstrated that similar activity patterns can be produced by different underlying mechanisms.

  • Experimental work indicates that the densities of ion channels can vary by as much as two- to fourfold across neurons of the same type in different animals, and that mRNA expression in the same neuron type can also vary in about the same range.

  • Intuitions about channel function that are developed on the basis of rapid pharmacological manipulations may fail to predict the results of long-term genetic manipulations of the same channel because of slow, compensatory mechanisms.

  • Much future work is needed to define the combinations of parameters that can give rise to a desired pattern of activity in neurons and networks, to discover the molecular mechanisms that regulate target activity levels, and to uncover the mechanisms by which compensatory regulation of channel expression occurs.

Abstract

Neurons in most animals live a very long time relative to the half-lives of all of the proteins that govern excitability and synaptic transmission. Consequently, homeostatic mechanisms are necessary to ensure stable neuronal and network function over an animal's lifetime. To understand how these homeostatic mechanisms might function, it is crucial to understand how tightly regulated synaptic and intrinsic properties must be for adequate network performance, and the extent to which compensatory mechanisms allow for multiple solutions to the production of similar behaviour. Here, we use examples from theoretical and experimental studies of invertebrates and vertebrates to explore several issues relevant to understanding the precision of tuning of synaptic and intrinsic currents for the operation of functional neuronal circuits.

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Figure 1: Conductances active below threshold can strongly influence neuronal activity and synaptic integration.
Figure 2: Neurons with similar intrinsic properties have different ratios of conductances.
Figure 3: Comparison of short-term pharmacological manipulations and long-term genetic deletions.
Figure 4: Similar network behaviour with different underlying conductances.
Figure 5: Variability of tuning of inhibitory and excitatory synaptic inputs in neurons in the cat primary visual cortex.
Figure 6: Constancy of network performance despite major size changes during growth.

References

  1. Hanwell, D., Ishikawa, T., Saleki, R. & Rotin, D. Trafficking and cell surface stability of the epithelial Na+ channel expressed in epithelial Madin–Darby canine kidney cells. J. Biol. Chem. 277, 9772–9779 (2002).

    Article  CAS  PubMed  Google Scholar 

  2. Monjaraz, E. et al. L-type calcium channel activity regulates sodium channel levels in rat pituitary GH3 cells. J. Physiol. (Lond.) 523, 45–55 (2000).

    Article  CAS  Google Scholar 

  3. Jugloff, D. G., Khanna, R., Schlichter, L. C. & Jones, O. T. Internalization of the Kv1.4 potassium channel is suppressed by clustering interactions with PSD-95. J. Biol. Chem. 275, 1357–1364 (2000).

    Article  CAS  PubMed  Google Scholar 

  4. Staub, O. et al. Regulation of stability and function of the epithelial Na+ channel (ENaC) by ubiquitination. EMBO J. 16, 6325–6336 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Bruneau, E. G., Macpherson, P. C., Goldman, D., Hume, R. I. & Akaaboune, M. The effect of agrin and laminin on acetylcholine receptor dynamics in vitro. Dev. Biol. 288, 248–258 (2005).

    Article  CAS  PubMed  Google Scholar 

  6. LeMasson, G., Marder, E. & Abbott, L. F. Activity-dependent regulation of conductances in model neurons. Science 259, 1915–1917 (1993). This theoretical paper was the first attempt to suggest that neuronal excitability might be controlled by a negative feedback, homeostatic mechanism in which the neuron's target activity is maintained despite channel turnover.

    Article  CAS  PubMed  Google Scholar 

  7. Liu, Z., Golowasch, J., Marder, E. & Abbott, L. F. A model neuron with activity-dependent conductances regulated by multiple calcium sensors. J. Neurosci. 18, 2309–2320 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Marder, E. & Prinz, A. A. Modeling stability in neuron and network function: the role of activity in homeostasis. Bioessays 24, 1145–1154 (2002).

    Article  CAS  PubMed  Google Scholar 

  9. Turrigiano, G. G. & Nelson, S. B. Homeostatic plasticity in the developing nervous system. Nature Rev. Neurosci. 5, 97–107 (2004). An outstanding review article that discusses homeostatic regulation of synaptic strength and intrinsic excitability.

    Article  CAS  Google Scholar 

  10. Davis, G. W. Homeostatic control of neural activity: from phenomenology to molecular design. Annu. Rev. Neurosci. 20 Mar 2006 (doi:10.1146/annurev.neuro.28.061604.135751). This review article provides a discussion of the outstanding questions relevant to homeostatic regulation. In particular, it addresses what is known about how targets for homeostatic regulation might be set.

  11. Turrigiano, G. G., Leslie, K. R., Desai, N. S., Rutherford, L. C. & Nelson, S. B. Activity-dependent scaling of quantal amplitude in neocortical neurons. Nature 391, 892–896 (1998). This now classic paper provided the first direct demonstration that a neuron slowly regulates the strength of all of its synapses in a multiplicative fashion.

    Article  CAS  PubMed  Google Scholar 

  12. Turrigiano, G. G. & Nelson, S. B. Hebb and homeostasis in neuronal plasticity. Curr. Opin. Neurobiol. 10, 358–364 (2000).

    Article  CAS  PubMed  Google Scholar 

  13. Desai, N. S., Rutherford, L. C. & Turrigiano, G. G. Plasticity in the intrinsic excitability of cortical pyramidal neurons. Nature Neurosci. 2, 515–520 (1999). Provides a direct demonstration of changes in current densities as a response to activity deprivation. Working with cultured cortical neurons, the authors show upregulation of Na+ currents and downregulation of K+ currents in response to 48 h of TTX treatment.

    Article  CAS  PubMed  Google Scholar 

  14. Zhang, W. & Linden, D. J. The other side of the engram: experience-driven changes in neuronal intrinsic excitability. Nature Rev. Neurosci. 4, 885–900 (2003).

    Article  CAS  Google Scholar 

  15. Aizenman, C. D., Akerman, C. J., Jensen, K. R. & Cline, H. T. Visually driven regulation of intrinsic neuronal excitability improves stimulus detection in vivo. Neuron 39, 831–842 (2003).

    Article  CAS  PubMed  Google Scholar 

  16. Hille, B. Ion Channels of Excitable Membranes (Sinauer, Sunderland, Massachusetts, 2001).

    Google Scholar 

  17. Connor, J. A. & Stevens, C. F. Prediction of repetitive firing behaviour from voltage clamp data on an isolated neurone soma. J. Physiol. (Lond.) 213, 31–53 (1971).

    Article  CAS  Google Scholar 

  18. Connor, J. A. & Stevens, C. F. Voltage clamp studies of a transient outward membrane current in gastropod neural somata. J. Physiol. (Lond.) 213, 21–30 (1971).

    Article  CAS  Google Scholar 

  19. Meech, R. W. Calcium-dependent potassium activation in nervous tissues. Annu. Rev. Biophys. Bioeng. 7, 1–18 (1978).

    Article  CAS  PubMed  Google Scholar 

  20. Connor, J. A., Walter, D. & McKown, R. Neural repetitive firing: modifications of the Hodgkin–Huxley axon suggested by experimental results from crustacean axons. Biophys. J. 18, 81–102 (1977).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Sah, P. & Faber, E. S. Channels underlying neuronal calcium-activated potassium currents. Prog. Neurobiol. 66, 345–353 (2002).

    Article  CAS  PubMed  Google Scholar 

  22. Pennefather, P., Lancaster, B., Adams, P. R. & Nicoll, R. A. Two distinct Ca-dependent K currents in bullfrog sympathetic ganglion cells. Proc. Natl Acad. Sci. USA 82, 3040–3044 (1985).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Day, M. et al. Dendritic excitability of mouse frontal cortex pyramidal neurons is shaped by the interaction among HCN, Kir2, and Kleak channels. J. Neurosci. 25, 8776–8787 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Ma, M. & Koester, J. The role of potassium currents in frequency-dependent spike broadening in Aplysia R20 neurons: a dynamic clamp analysis. J. Neurosci. 16, 4089–4101 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Swensen, A. M. & Bean, B. P. Ionic mechanisms of burst firing in dissociated Purkinje neurons. J. Neurosci. 23, 9650–63 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Chen, K. et al. Persistently modified h-channels after complex febrile seizures convert the seizure-induced enhancement of inhibition to hyperexcitability. Nature Med. 7, 331–337 (2001). In contrast to most studies that depend on pharmacological manipulations to demonstrate homeostatic regulation, here the authors exploit a disease paradigm, febrile seizures, to study the interaction between synaptic and intrinsic excitability.

    Article  CAS  PubMed  Google Scholar 

  27. French, C. R., Sah, P., Buckett, K. J. & Gage, P. W. A voltage-dependent persistent sodium current in mammalian hippocampal neurons. J. Gen. Physiol. 95, 1139–1157 (1990).

    Article  CAS  PubMed  Google Scholar 

  28. Hoffman, D. A., Magee, J. C., Colbert, C. M. & Johnston, D. K+ channel regulation of signal propagation in dendrites of hippocampal pyramidal neurons. Nature 387, 869–875 (1997).

    Article  CAS  PubMed  Google Scholar 

  29. Fraser, D. D. & MacVicar, B. A. Low-threshold transient calcium current in rat hippocampal lacunosum-moleculare interneurons: kinetics and modulation by neurotransmitters. J. Neurosci. 11, 2812–2820 (1991).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Ngo-Anh, T. J. et al. SK channels and NMDA receptors form a Ca2+-mediated feedback loop in dendritic spines. Nature Neurosci. 8, 642–649 (2005).

    Article  CAS  PubMed  Google Scholar 

  31. Vervaeke, K., Hu, H., Graham, L. J. & Storm, J. F. Contrasting effects of the persistent Na+ current on neuronal excitability and spike timing. Neuron 49, 257–270 (2006).

    Article  CAS  PubMed  Google Scholar 

  32. Magee, J. C. Dendritic hyperpolarization-activated currents modify the integrative properties of hippocampal CA1 pyramidal neurons. J. Neurosci. 18, 7613–7624 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Ramakers, G. M. & Storm, J. F. A postsynaptic transient K+ current modulated by arachidonic acid regulates synaptic integration and threshold for LTP induction in hippocampal pyramidal cells. Proc. Natl Acad. Sci. USA 99, 10144–10149 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Gillessen, T. & Alzheimer, C. Amplification of EPSPs by low Ni2+- and amiloride-sensitive Ca2+ channels in apical dendrites of rat CA1 pyramidal neurons. J. Neurophysiol. 77, 1639–1643 (1997).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  36. Pape, H. C. & McCormick, D. A. Noradrenaline and serotonin selectively modulate thalamic burst firing by enhancing a hyperpolarization-activated cation current. Nature 340, 715–718 (1989).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  38. Golowasch, J., Goldman, M. S., Abbott, L. F. & Marder, E. Failure of averaging in the construction of a conductance-based neuron model. J. Neurophysiol. 87, 1129–1131 (2002).

    Article  PubMed  Google Scholar 

  39. Foster, W. R., Ungar, L. H. & Schwaber, J. S. Significance of conductances in Hodgkin–Huxley models. J. Neurophysiol. 70, 2502–2518 (1993).

    Article  CAS  PubMed  Google Scholar 

  40. Taylor, A. L., Hickey, T. J., Prinz, A. A. & Marder, E. Structure and visualization of high-dimensional conductance spaces. J. Neurophysiol. (in the press).

  41. Goldman, M. S., Golowasch, J., Marder, E. & Abbott, L. F. Global structure, robustness, and modulation of neuronal models. J. Neurosci. 21, 5229–5238 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Schulz, D. J., Goaillard, J. M. & Marder, E. Variable channel expression in identified single and electrically coupled neurons in different animals. Nature Neurosci. 9, 356–362 (2006). Combines voltage clamp analyses and real-time PCR measurements of mRNA copy number in single neurons, and finds that both measures vary considerably in the single LP neuron from different animals. Although pyloric dilator neurons also show considerable animal-to-animal variability, the two electrically coupled neurons from the same animal show very similar levels of channel mRNA expression.

    Article  CAS  PubMed  Google Scholar 

  43. Swensen, A. M. & Bean, B. P. Robustness of burst firing in dissociated purkinje neurons with acute or long-term reductions in sodium conductance. J. Neurosci. 25, 3509–3520 (2005). A fascinating study that raises many important issues. Among them is the observation that individual cerebellar Purkinje neurons that show almost identical patterns of electrical activity have quite different ratios of inward Na+ and Ca2+ currents.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Baro, D. J. et al. Quantitative single-cell-reverse transcription-PCR demonstrates that A- current magnitude varies as a linear function of shal gene expression in identified stomatogastric neurons. J. Neurosci. 17, 6597–6610 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Golowasch, J., Abbott, L. F. & Marder, E. Activity-dependent regulation of potassium currents in an identified neuron of the stomatogastric ganglion of the crab Cancer borealis. J. Neurosci. 19, RC33 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Liss, B. et al. Tuning pacemaker frequency of individual dopaminergic neurons by Kv4.3L and KChip3.1 transcription. EMBO J. 20, 5715–24 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Harris-Warrick, R. M. & Flamm, R. E. Multiple mechanisms of bursting in a conditional bursting neuron. J. Neurosci. 7, 2113–2128 (1987).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Harris-Warrick, R. M., Coniglio, L. M., Barazangi, N., Guckenheimer, J. & Gueron, S. Dopamine modulation of transient potassium current evokes phase shifts in a central pattern generator network. J. Neurosci. 15, 342–358 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Guckenheimer, J., Gueron, S. & Harris-Warrick, R. M. Mapping the dynamics of a bursting neuron. Phil. Trans. R. Soc. Lond. B 341, 345–359 (1993).

    Article  CAS  Google Scholar 

  50. Guckenheimer, J., Harris-Warrick, R., Peck, J. & Willms, A. Bifurcation, bursting, and spike frequency adaptation. J. Comput. Neurosci. 4, 257–277 (1997).

    Article  CAS  PubMed  Google Scholar 

  51. Prinz, A. A., Thirumalai, V. & Marder, E. The functional consequences of changes in the strength and duration of synaptic inputs to oscillatory neurons. J. Neurosci. 23, 943–954 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Piedras-Renteria, E. S. et al. Presynaptic homeostasis at CNS nerve terminals compensates for lack of a key Ca2+ entry pathway. Proc. Natl Acad. Sci. USA 101, 3609–3614 (2004). Remarkably, genetic knockouts of the P/Q type Ca2+ channel have relatively little effect on synaptic transmission, because of compensation by other mechanisms.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Thiagarajan, T. C., Lindskog, M. & Tsien, R. W. Adaptation to synaptic inactivity in hippocampal neurons. Neuron 47, 725–737 (2005).

    Article  CAS  PubMed  Google Scholar 

  54. Vahasoyrinki, M., Niven, J., Hardie, R., Weckstrom, M. & Juusola, M. Robustness of neural coding in Drosophila photoreceptors in the absence of slow delayed rectifier K+ channels. J. Neurosci. 26, 2652–2660 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. MacLean, J. N., Zhang, Y., Johnson, B. R. & Harris-Warrick, R. M. Activity-independent homeostasis in rhythmically active neurons. Neuron 37, 109–120 (2003). In this paper, the authors inject mRNA for shal, resulting in large (three- to fourfold) increases in I A without changes in firing, because the upregulation in I A is accompanied by compensatory changes in I H . Because short-term manipulations of I A result in changes in activity, this paper directly illustrates the difference between short-term pharmacological manipulation of a current and long-term changes that are accompanied by compensation.

    Article  CAS  PubMed  Google Scholar 

  56. MacLean, J. N. et al. Activity-independent coregulation of IA and Ih in rhythmically active neurons. J. Neurophysiol. 94, 3601–3617 (2005).

    Article  PubMed  Google Scholar 

  57. Tierney, A. J. & Harris-Warrick, R. M. Physiological role of the transient potassium current in the pyloric circuit of the lobster stomatogastric ganglion. J. Neurophysiol. 67, 599–609 (1992).

    Article  CAS  PubMed  Google Scholar 

  58. Zhang, Y. et al. Overexpression of a hyperpolarization-activated cation current (Ih) channel gene modifies the firing activity of identified motor neurons in a small neural network. J. Neurosci. 23, 9059–9067 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Nusbaum, M. P. & Marder, E. A modulatory proctolin-containing neuron (MPN). II. State-dependent modulation of rhythmic motor activity. J. Neurosci. 9, 1600–1607 (1989).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Kiehn, O. & Harris-Warrick, R. M. 5-HT modulation of hyperpolarization-activated inward current and calcium-dependent outward current in a crustacean motor neuron. J. Neurophysiol. 68, 496–508 (1992).

    Article  CAS  PubMed  Google Scholar 

  61. Harris-Warrick, R. M. et al. Distributed effects of dopamine modulation in the crustacean pyloric network. Ann. NY Acad. Sci. 860, 155–167 (1998).

    Article  CAS  PubMed  Google Scholar 

  62. Elson, R. C. & Selverston, A. I. Mechanisms of gastric rhythm generation in isolated stomatogastric ganglion of spiny lobsters: bursting pacemaker potentials, synaptic interactions and muscarinic modulation. J. Neurophysiol. 68, 890–907 (1992).

    Article  CAS  PubMed  Google Scholar 

  63. Szucs, A., Abarbanel, H. D., Rabinovich, M. I. & Selverston, A. I. Dopamine modulation of spike dynamics in bursting neurons. Eur. J. Neurosci. 21, 763–772 (2005).

    Article  PubMed  Google Scholar 

  64. Turrigiano, G., Abbott, L. F. & Marder, E. Activity-dependent changes in the intrinsic properties of cultured neurons. Science 264, 974–977 (1994).

    Article  CAS  PubMed  Google Scholar 

  65. Siegel, M., Marder, E. & Abbott, L. F. Activity-dependent current distributions in model neurons. Proc. Natl Acad. Sci. USA 91, 11308–11312 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Stemmler, M. & Koch, C. How voltage-dependent conductances can adapt to maximize the information encoded by neuronal firing rate. Nature Neurosci. 2, 521–527 (1999).

    Article  CAS  PubMed  Google Scholar 

  67. Bito, H., Deisseroth, K. & Tsien, R. W. CREB phosphorylation and dephosphorylation: a Ca2+-and stimulus duration-dependent switch for hippocampal gene expression. Cell 87, 1203–1214 (1996).

    Article  CAS  PubMed  Google Scholar 

  68. Deisseroth, K., Mermelstein, P. G., Xia, H. & Tsien, R. W. Signaling from synapse to nucleus: the logic behind the mechanisms. Curr. Opin. Neurobiol. 13, 354–365 (2003).

    Article  CAS  PubMed  Google Scholar 

  69. Deisseroth, K. & Tsien, R. W. Dynamic multiphosphorylation passwords for activity-dependent gene expression. Neuron 34, 179–182 (2002).

    Article  CAS  PubMed  Google Scholar 

  70. Morozov, A. et al. Rap1 couples cAMP signaling to a distinct pool of p42/44MAPK regulating excitability, synaptic plasticity, learning, and memory. Neuron 39, 309–325 (2003).

    Article  CAS  PubMed  Google Scholar 

  71. Pittenger, C. & Kandel, E. R. In search of general mechanisms for long-lasting plasticity: Aplysia and the hippocampus. Phil. Trans. R. Soc. Lond. B 358, 757–763 (2003).

    Article  Google Scholar 

  72. Schorge, S., Gupta, S., Lin, Z., McEnery, M. W. & Lipscombe, D. Calcium channel activation stabilizes a neuronal calcium channel mRNA. Nature Neurosci. 2, 785–790 (1999).

    Article  CAS  PubMed  Google Scholar 

  73. Sugino, K. et al. Molecular taxonomy of major neuronal classes in the adult mouse forebrain. Nature Neurosci. 9, 99–107 (2006). The authors use microarrays to classify populations of neurons in the mouse forebrain as part of an attempt to determine how many different types of neuronal class exist in major brain neurons.

    Article  CAS  PubMed  Google Scholar 

  74. Kamme, F. et al. Single-cell microarray analysis in hippocampus CA1: demonstration and validation of cellular heterogeneity. J. Neurosci. 23, 3607–3615 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Tietjen, I., Rihel, J. & Dulac, C. G. Single-cell transcriptional profiles and spatial patterning of the mammalian olfactory epithelium. Int. J. Dev. Biol. 49, 201–207 (2005).

    Article  CAS  PubMed  Google Scholar 

  76. Tanaka, H. et al. Proteasomal degradation of Kir6.2 channel protein and its inhibition by a Na+ channel blocker aprindine. Biochem. Biophys. Res. Commun. 331, 1001–1006 (2005).

    Article  CAS  PubMed  Google Scholar 

  77. Prinz, A. A., Billimoria, C. P. & Marder, E. Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons. J. Neurophysiol. 90, 3998–4015 (2003).

    Article  PubMed  Google Scholar 

  78. Prinz, A. A., Bucher, D. & Marder, E. Similar network activity from disparate circuit parameters. Nature Neurosci. 7, 1345–1352 (2004). The authors constructed >20 million model networks, then characterized their behaviour. The salient result of this study is that very similar output patterns can result from dramatically different sets of underlying parameters.

    Article  CAS  PubMed  Google Scholar 

  79. Davis, G. W. & Bezprozvanny, I. Maintaining the stability of neural function: a homeostatic hypothesis. Annu. Rev. Physiol. 63, 847–869 (2001).

    Article  CAS  PubMed  Google Scholar 

  80. Turrigiano, G. G. & Nelson, S. B. Thinking globally, acting locally: AMPA receptor turnover and synaptic strength. Neuron 21, 933–935 (1998).

    Article  CAS  PubMed  Google Scholar 

  81. Soto-Trevino, C., Thoroughman, K. A., Marder, E. & Abbott, L. F. Activity-dependent modification of inhibitory synapses in models of rhythmic neural networks. Nature Neurosci. 4, 297–303 (2001).

    Article  CAS  PubMed  Google Scholar 

  82. Paradis, S., Sweeney, S. T. & Davis, G. W. Homeostatic control of presynaptic release is triggered by postsynaptic membrane depolarization. Neuron 30, 737–749 (2001).

    Article  CAS  PubMed  Google Scholar 

  83. Mody, I. Aspects of the homeostaic plasticity of GABAA receptor-mediated inhibition. J. Physiol. (Lond.) 562, 37–46 (2005).

    Article  CAS  Google Scholar 

  84. Rutherford, L. C., DeWan, A., Lauer, H. M. & Turrigiano, G. G. Brain-derived neurotrophic factor mediates the activity-dependent regulation of inhibition in neocortical cultures. J. Neurosci. 17, 4527–4535 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Kilman, V., van Rossum, M. C. & Turrigiano, G. G. Activity deprivation reduces miniature IPSC amplitude by decreasing the number of postsynaptic GABAA receptors clustered at neocortical synapses. J. Neurosci. 22, 1328–1337 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Erickson, J. D., De Gois, S., Varoqui, H., Schafer, M. K. & Weihe, E. Activity-dependent regulation of vesicular glutamate and GABA transporters: a means to scale quantal size. Neurochem. Int. 48, 643–649 (2006).

    Article  CAS  PubMed  Google Scholar 

  87. Swanwick, C. C., Murthy, N. R. & Kapur, J. Activity-dependent scaling of GABAergic synapse strength is regulated by brain-derived neurotrophic factor. Mol. Cell. Neurosci. 31, 481–492 (2006).

    Article  CAS  PubMed  Google Scholar 

  88. De Gois, S. et al. Homeostatic scaling of vesicular glutamate and GABA transporter expression in rat neocortical circuits. J. Neurosci. 25, 7121–7133 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Bucher, D., Prinz, A. A. & Marder, E. Animal-to-animal variability in motor pattern production in adults and during growth. J. Neurosci. 25, 1611–1619 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Manor, Y., Nadim, F., Abbott, L. F. & Marder, E. Temporal dynamics of graded synaptic transmission in the lobster stomatogastric ganglion. J. Neurosci. 17, 5610–5621 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Thirumalai, V., Prinz, A. A., Johnson, C. D. & Marder, E. Red pigment concentrating hormone strongly enhances the strength of the feedback to the pyloric rhythm oscillator but has little effect on pyloric rhythm period. J. Neurophysiol. 95, 1762–1770 (2006).

    Article  CAS  PubMed  Google Scholar 

  92. Tobin, A. E. & Calabrese, R. L. Myomodulin increases Ih and inhibits the NA/K pump to modulate bursting in leech heart interneurons. J. Neurophysiol. 94, 3938–3950 (2005).

    Article  CAS  PubMed  Google Scholar 

  93. Rabbah, P. & Nadim, F. Synaptic dynamics do not determine proper phase of activity in a central pattern generator. J. Neurosci. 25, 11269–11278 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Eisen, J. S. & Marder, E. A mechanism for production of phase shifts in a pattern generator. J. Neurophysiol. 51, 1375–1393 (1984).

    Article  CAS  PubMed  Google Scholar 

  95. Olsen, Ø. H. & Calabrese, R. L. Activation of intrinsic and synaptic currents in leech heart interneurons by realistic waveforms. J. Neurosci. 16, 4958–4970 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Sorensen, M., DeWeerth, S., Cymbalyuk, G. & Calabrese, R. L. Using a hybrid neural system to reveal regulation of neuronal network activity by an intrinsic current. J. Neurosci. 24, 5427–5438 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Hartline, D. K., Russell, D. F., Raper, J. A. & Graubard, K. Special cellular and synaptic mechanisms in motor pattern generation. Comp. Biochem. Physiol. 91C, 115–131 (1988).

    Google Scholar 

  98. Goulding, M. & Pfaff, S. L. Development of circuits that generate simple rhythmic behaviors in vertebrates. Curr. Opin. Neurobiol. 15, 14–20 (2005).

    Article  CAS  PubMed  Google Scholar 

  99. Turrigiano, G. G., Marder, E. & Abbott, L. F. Cellular short-term memory from a slow potassium conductance. J. Neurophysiol. 75, 963–966 (1996).

    Article  CAS  PubMed  Google Scholar 

  100. Santhakumar, V. & Soltesz, I. Plasticity of interneuronal species diversity and parameter variance in neurological diseases. Trends Neurosci. 27, 504–510 (2004).

    Article  CAS  PubMed  Google Scholar 

  101. Aradi, I. & Soltesz, I. Modulation of network behaviour by changes in variance in interneuronal properties. J. Physiol. (Lond.) 538, 227–251 (2002).

    Article  CAS  Google Scholar 

  102. Aradi, I., Santhakumar, V., Chen, K. & Soltesz, I. Postsynaptic effects of GABAergic synaptic diversity: regulation of neuronal excitability by changes in IPSC variance. Neuropharmacology 43, 511–522 (2002).

    Article  CAS  PubMed  Google Scholar 

  103. Aradi, I., Santhakumar, V. & Soltesz, I. Impact of heterogeneous perisomatic IPSC populations on pyramidal cell firing rates. J. Neurophysiol. 91, 2849–2858 (2004).

    Article  PubMed  Google Scholar 

  104. Foldy, C., Aradi, I., Howard, A. & Soltesz, I. Diversity beyond variance: modulation of firing rates and network coherence by GABAergic subpopulations. Eur. J. Neurosci. 19, 119–130 (2004).

    Article  PubMed  Google Scholar 

  105. Monier, C., Chavane, F., Baudot, P., Graham, L. J. & Fregnac, Y. Orientation and direction selectivity of synaptic inputs in visual cortical neurons: a diversity of combinations produces spike tuning. Neuron 37, 663–680 (2003).

    Article  CAS  PubMed  Google Scholar 

  106. Marino, J. et al. Invariant computations in local cortical networks with balanced excitation and inhibition. Nature Neurosci. 8, 194–201 (2005).

    Article  CAS  PubMed  Google Scholar 

  107. Schummers, J., Marino, J. & Sur, M. Synaptic integration by V1 neurons depends on location within the orientation map. Neuron 36, 969–978 (2002).

    Article  CAS  PubMed  Google Scholar 

  108. Chiba, A., Kamper, G. & Murphey, R. K. Response properties of interneurons of the cricket cercal sensory system are conserved in spite of changes in peripheral receptors during maturation. J. Exp. Biol. 164, 205–226 (1992).

    Article  Google Scholar 

  109. Pulver, S. R., Bucher, D., Simon, D. J. & Marder, E. Constant amplitude of postsynaptic responses for single presynaptic action potentials but not bursting input during growth of an identified neuromuscular junction in the lobster, Homarus americanus. J. Neurobiol. 62, 47–61 (2005).

    Article  PubMed  Google Scholar 

  110. Hill, A. A., Edwards, D. H. & Murphey, R. K. The effect of neuronal growth on synaptic integration. J. Comput. Neurosci. 1, 239–254 (1994).

    Article  CAS  PubMed  Google Scholar 

  111. Olsen, O., Nadim, F., Hill, A. A. & Edwards, D. H. Uniform growth and neuronal integration. J. Neurophysiol. 76, 1850–1857 (1996).

    Article  CAS  PubMed  Google Scholar 

  112. Hochner, B. & Spira, M. E. Preservation of motoneuron electrotonic characteristics during postembryonic growth. J. Neurosci. 7, 261–270 (1987).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Golowasch, J., Casey, M., Abbott, L. F. & Marder, E. Network stability from activity-dependent regulation of neuronal conductances. Neural Comput. 11, 1079–1096 (1999).

    Article  CAS  PubMed  Google Scholar 

  114. Luther, J. A. et al. Episodic bouts of activity accompany recovery of rhythmic output by a neuromodulator- and activity-deprived adult neural network. J. Neurophysiol. 90, 2720–2730 (2003).

    Article  PubMed  Google Scholar 

  115. Mizrahi, A. et al. Long-term maintenance of channel distribution in a central pattern generator neuron by neuromodulatory inputs revealed by decentralization in organ culture. J. Neurosci. 21, 7331–7339 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Thoby-Brisson, M. & Simmers, J. Neuromodulatory inputs maintain expression of a lobster motor pattern-generating network in a modulation-dependent state: evidence from long-term decentralization in vitro. J. Neurosci. 18, 2212–2225 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Thoby-Brisson, M. & Simmers, J. Transition to endogenous bursting after long-term decentralization requires de novo transcription in a critical time window. J. Neurophysiol. 84, 596–599 (2000).

    Article  CAS  PubMed  Google Scholar 

  118. Thoby-Brisson, M. & Simmers, J. Long-term neuromodulatory regulation of a motor pattern-generating network: maintenance of synaptic efficacy and oscillatory properties. J. Neurophysiol. 88, 2942–2953 (2002).

    Article  PubMed  Google Scholar 

  119. Bekoff, A. Spontaneous embryonic motility: an enduring legacy. Int. J. Dev. Neurosci. 19, 155–160 (2001).

    Article  CAS  PubMed  Google Scholar 

  120. Ben-Ari, Y. Developing networks play a similar melody. Trends Neurosci. 24, 353–360 (2001).

    Article  CAS  PubMed  Google Scholar 

  121. Feller, M. B. Spontaneous correlated activity in developing neural circuits. Neuron 22, 653–656 (1999).

    Article  CAS  PubMed  Google Scholar 

  122. O'Donovan, M. J. The origin of spontaneous activity in developing networks of the vertebrate nervous system. Curr. Opin. Neurobiol. 9, 94–104 (1999).

    Article  CAS  PubMed  Google Scholar 

  123. Marder, E. & Rehm, K. J. Development of central pattern generating circuits. Curr. Opin. Neurobiol. 15, 86–93 (2005).

    Article  CAS  PubMed  Google Scholar 

  124. Fénelon, V. S., Casasnovas, B., Simmers, J. & Meyrand, P. Development of rhythmic pattern generators. Curr. Opin. Neurobiol. 8, 705–709 (1998).

    Article  PubMed  Google Scholar 

  125. O'Donovan, M. J., Bonnot, A., Wenner, P. & Mentis, G. Z. Calcium imaging of network function in the developing spinal cord. Cell Calcium 37, 443–450 (2005).

    Article  CAS  PubMed  Google Scholar 

  126. Wenner, P. & O'Donovan, M. J. Mechanisms that initiate spontaneous network activity in the developing chick spinal cord. J. Neurophysiol. 86, 1481–1498 (2001).

    Article  CAS  PubMed  Google Scholar 

  127. Gonzalez-Islas, C. & Wenner, P. Spontaneous network activity in the embryonic spinal cord regulates AMPAergic and GABAergic synaptic strength. Neuron 49, 563–575 (2006).

    Article  CAS  PubMed  Google Scholar 

  128. Greenspan, R. J. The flexible genome. Nature Rev. Genet. 2, 383–387 (2001). An important philosophical discussion of what we can expect from attempting a genetic analysis of behaviour, given the complex interrelationships of biochemical and molecular signalling networks.

    Article  CAS  PubMed  Google Scholar 

  129. Greenspan, R. J. E pluribus unum, ex uno plura: quantitative and single-gene perspectives on the study of behavior. Annu. Rev. Neurosci. 27, 79–105 (2004).

    Article  CAS  PubMed  Google Scholar 

  130. Alon, U., Surette, M. G., Barkai, N. & Leibler, S. Robustness in bacterial chemotaxis. Nature 397, 168–171 (1999).

    Article  CAS  PubMed  Google Scholar 

  131. Barkai, N. & Leibler, S. Robustness in simple biochemical networks. Nature 387, 913–917 (1997).

    Article  CAS  PubMed  Google Scholar 

  132. Ma'ayan, A., Blitzer, R. D. & Iyengar, R. Toward predictive models of mammalian cells. Annu. Rev. Biophys. Biomol. Struct. 34, 319–349 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Meir, E., von Dassow, G., Munro, E. & Odell, G. M. Robustness, flexibility, and the role of lateral inhibition in the neurogenic network. Curr. Biol. 12, 778–786 (2002).

    Article  CAS  PubMed  Google Scholar 

  134. Miesenbock, G. & Kevrekidis, I. G. Optical imaging and control of genetically designated neurons in functioning circuits. Annu. Rev. Neurosci. 28, 533–563 (2005).

    Article  CAS  PubMed  Google Scholar 

  135. Miesenbock, G. & Morris, R. G. New technologies. Curr. Opin. Neurobiol. 15, 557–559 (2005).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This work was supported by grants from the National Institutes of Health (NIH) and the McDonnell Foundation. We thank L. Abbott for years of conversation about many of these issues and for reading an early version of this manuscript, and P. Baudot for helpful discussions. We are grateful to all the members of the Brandeis University community who have played an important part in the generation of much of the data and many of the ideas presented here.

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  1. Volen Center and Biology Department, MS 013 Brandeis University, 415 South Street, Waltham, 02454, Massachusetts, USA

    Eve Marder & Jean-Marc Goaillard

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  1. Eve Marder
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  2. Jean-Marc Goaillard
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Correspondence to Eve Marder.

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Marder's laboratory

Glossary

Synaptic scaling

Process by which neurons regulate the strength of all of their synapses to help maintain a target activity level.

Conductance densities

The conductance density is conductance divided by surface area. Conductance for a given channel is calculated from the current and reversal potential, and the surface area is estimated from capacitance measurements.

Transient outward current

(IA). This is caused by a voltage-gated K+ channel that opens when the neuron is depolarized and then inactivates (closes) rapidly. To remove the inactivation, the neuron must be hyperpolarized. IA often plays a part in determining the frequency of action potential firing.

Afterhyperpolarization

The membrane hyperpolarization that follows an action potential.

Window currents

A sustained current at a membrane potential that occurs if the voltage dependence of activation and inactivation overlap at that membrane potential.

Kleak

K+ current active at hyperpolarized membrane potentials that contributes to the resting potential.

Hyperpolarization/cyclic nucleotide gated channels

(HCN channels). These are a family of mixed cation conductances that activate when the cell is hyperpolarized.

Inwardly rectifying potassium channels

(Kir2 channels). These are K+ channels that pass inward current much better than outward current. These channels often play an important part in setting the resting potential by contributing an outward current when the neuron is close to its resting potential. However, when the neuron is depolarized, the outward current that develops is less than would be expected from the increase in driving force.

Pyloric dilator neurons

There are two electrically coupled pyloric dilator neurons in each stomatogastric ganglion. These neurons are also electrically coupled to the anterior burster neuron, and the anterior burster and pyloric dilator neurons together form the pacemaker kernel for the pyloric rhythm. The pyloric dilator neurons are also motor neurons that innervate muscles that dilate the pyloric region of the stomach.

Long-term potentiation

(LTP). A long-lasting increase in the amplitude of synaptic potentials as a result of specific patterns of presynaptic stimulation. LTP is often thought to be a cellular correlate of changes in networks underlying learning.

Long-term depression

(LTD). A long-lasting decrease in synaptic strength that is induced by specific patterns of presynaptic activation.

Synaptic weights

The strengths of synaptic potentials are often called synaptic weights. This term is commonly used in computational and network modelling studies.

Pyloric rhythm

One of the motor patterns produced by the crustacean stomatogastric ganglion. The pyloric rhythm is an example of a central pattern generator, and consists of an oscillatory motor discharge with a frequency of ∼1 Hz. It is one of the best understood small circuits.

Half-centre oscillator

An oscillatory circuit produced by reciprocal inhibition. Half-centre oscillators are thought to be important components of many central pattern-generating circuits.

Lateral pyloric neuron

Each stomatogastric ganglion has a single lateral pyloric neuron, which fires in alternation with the pyloric dilator neurons in the pyloric rhythm. The lateral pyloric neuron provides the only feedback from the pyloric circuit to the pacemaker neurons, and is also a motor neuron that innervates the constrictor muscles of the stomach.

Central pattern generator

A neural circuit that produces rhythmic motor patterns without requiring timed sensory input.

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Marder, E., Goaillard, JM. Variability, compensation and homeostasis in neuron and network function. Nat Rev Neurosci 7, 563–574 (2006). https://doi.org/10.1038/nrn1949

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