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Role of Nonlinear Dynamical Properties of a Modelled Bursting Neuron in Information Processing and Storage

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image of Netherlands Journal of Zoology
For more content, see Archives Néerlandaises de Zoologie (Vol 1-17) and Animal Biology (Vol 53 and onwards).

Enduring changes in the electrical activity of individual neurons have generally been attributed to persistent modulation of one or more of the biophysical parameters that govern neuronal membrane conductances. An implicit assumption has been that once all parameters are fixed, the ultimate mode of electrical activity exhibited is determined. An alternative possibility is that several stable modes of activity coexist at a single set of parameters and that transient perturbations could switch the neuron from one stable mode of activity to another. By using a realistic mathematical model and computer simulations of the R15 neuron in Aplysia, we identified a new, and potentially fundamental, role for nonlinear dynamics in information processing and storage at the single-cell level. We found that transient synaptic input could shift the dynamic activity of the neuron among as many as eight different patterns, or modes, of activity. Once established, each mode persisted indefinitely or until subsequent synaptic input perturbed the neuron into another mode. We also investigated the ways in which changes in a model parameter, the slow inward calcium conductance (gSI), affected not only the intrinsic activity of R15 but also the ability of the neuron to exhibit parameter-independent mode transitions. gSI was selected since it is a key determinant of bursting activity and, also, because it is known to be modulated by dopamine and serotonin. We found that small changes in this parameter could annihilate or create coexisting modes of electrical activity. These results provide new insights into the role of nonlinear dynamics in information processing and storage at the level of the single neuron and indicate that individual neurons can have extensive parameter-independent plastic capabilities in addition to the more extensively analyzed parameter-dependent ones.

Affiliations: 1: Department of Neurobiology and Anatomy, University of Texas Medical School at Houston, P.O. Box 20708, Houston, TX 77225, U.S.A.; 2: Department of Electrical & Computer Engineering, Rice University, P.O. Box 1892, Houston, TX 77251-1892, U.S.A.


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