Article

Dependence of firing pattern on intrinsic ionic conductances: Sensitive and insensitive combinations

Volen Center for Complex Systems, Brandeis University, 415 South Street MS013 Waltham, MA 02454 USA; Department of Physics, Harvard University, Cambridge, MA 02138 USA
Neurocomputing 01/2000; DOI: 10.1016/S0925-2312(00)00155-7
Source: DBLP

ABSTRACT We construct maps of the intrinsic firing state (silent, tonically firing, or bursting) of a model neuron as a function of its maximal ionic conductances. The firing properties vary significantly only in response to changes in particular, sensitive combinations of conductances that may serve as targets for neuromodulation. Less sensitive combinations define directions along which neuronal conductances may drift without triggering significant changes in firing activity. This suggests that neurons may have similar functional properties despite widely varying conductance densities.

0 Bookmarks
 · 
37 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The electrical characteristics of many neurons are remarkably robust in the face of changing internal and external conditions. At the same time, neurons can be highly sensitive to neuromodulators. We find correlates of this dual robustness and sensitivity in a global analysis of the structure of a conductance-based model neuron. We vary the maximal conductance parameters of the model neuron and, for each set of parameters tested, characterize the activity pattern generated by the cell as silent, tonically firing, or bursting. Within the parameter space of the five maximal conductances of the model, we find directions, representing concerted changes in multiple conductances, along which the basic pattern of neural activity does not change. In other directions, relatively small concurrent changes in a few conductances can induce transitions between these activity patterns. The global structure of the conductance-space maps implies that neuromodulators that alter a sensitive set of conductances will have powerful, and possibly state-dependent, effects. Other modulators that may have no direct impact on the activity of the neuron may nevertheless change the effects of such direct modulators via this state dependence. Some of the results and predictions arising from the model studies are replicated and verified in recordings of stomatogastric ganglion neurons using the dynamic clamp.
    Journal of Neuroscience 08/2001; 21(14):5229-38. · 6.91 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Electrical activity in identical neurons across individuals is often remarkably similar and stable over long periods. However, the ionic currents that determine the electrical activity of these neurons show wide animal-to-animal amplitude variability. This seemingly random variability of individual current amplitudes may obscure mechanisms that globally reduce variability and that contribute to the generation of similar neuronal output. One such mechanism could be the coordinated regulation of ionic current expression. Studying identified neurons of the Cancer borealis pyloric network, we discovered that the removal of neuromodulatory input to this network (decentralization) was accompanied by the loss of the coordinated regulation of ionic current levels. Additionally, decentralization induced large changes in the levels of several ionic currents. The loss of coregulation and the changes in current levels were prevented by continuous exogenous application of proctolin, an endogenous neuromodulatory peptide, to the pyloric network. This peptide does not exert fast regulatory actions on any of the currents affected by decentralization. We conclude that neuromodulatory inputs to the pyloric network have a novel role in the regulation of ionic current expression. They can control, over the long term, the coordinated expression of multiple voltage-gated ionic currents that they do not acutely modulate. Our results suggest that current coregulation places constraints on neuronal intrinsic plasticity and the ability of a network to respond to perturbations. The loss of conductance coregulation may be a mechanism to facilitate the recovery of function.
    Journal of Neuroscience 09/2007; 27(32):8709-18. · 6.91 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Computational modelling is an approach to neuronal network analysis that can complement experimental approaches. Construction of useful neuron and network models is often complicated by a variety of factors and unknowns, most notably the considerable variability of cellular and synaptic properties and electrical activity characteristics found even in relatively 'simple' networks of identifiable neurons. This chapter discusses the consequences of biological variability for network modelling and analysis, describes a way to embrace variability through ensemble modelling and summarizes recent findings obtained experimentally and through ensemble modelling.
    Philosophical Transactions of The Royal Society B Biological Sciences 08/2010; 365(1551):2397-405. · 6.23 Impact Factor

Full-text (2 Sources)

View
11 Downloads
Available from
Jun 11, 2014