Dependence of firing pattern on intrinsic ionic conductances: Sensitive and insensitive combinations
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.
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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.The Journal of Neuroscience : The Official Journal of the Society for Neuroscience 08/2001; 21(14):5229-38. · 6.75 Impact Factor
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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. DOI:10.1098/rstb.2010.0029 · 6.31 Impact Factor