Computer simulations of neuronal signal transduction:
The role of nonlinear dynamics and noise
H.A. Braun 
, M.T. Huber 
, H. Wissing 
, M. Petersen 
,
K. Voigt
Inst. of Physiology and Dept. of Psychiatry
of the University of
Marburg, D-35037 Marburg;
Dept. of Anesthesiology of the University of Frankfurt, D-60596 Frankfurt;
Inst. of Physiology of the University of Wuerzburg, D-97070 Wuerzburg
In recent years, nerve cells have been recognized as dynamic elements, consisting of a highly nonlinear system of many different ionic conductances. Depending on the actual parameter values (nonlinear voltage dependencies, time-delays, etc.), such systems exhibit intrinsic membrane potential oscillations that are associated with rhythmic spike generation.
Here, we present a minimal model of nonlinear ionic mechanisms which develop oscillating spike-generation on depolarization. Moreover, we added a noisy term which considerably extended the activity range of the model, introducing a particular type of impulse pattern which originate from a mixture of spike-triggering and subthreshold oscillations.
Under these conditions, external modulation of the system's parameters can drastically and selectively alter the impulse patterns through slight modifications of the oscillation parameters, frequency, amplitude or base-line. Particular encoding properties can be, thereby, obtained that, probably, are of physiological significance for signal transduction in peripheral sensory cells, as well as for information processing in the central nervous system (CNS).
We have applied our model to experimental data from the following physiological paradigms: 1) "differential encoding" of environmental stimuli in multimodal sensory cells. 2) "sensitization" of neurons which, for example, are involved in pain perception. 3) "gain adjustment" of neuronal transduction characteristics as a putative component for selective attention or adaptive processes in the CNS. Our results indicate a remarkably broad functional significance of nonlinear dynamics and noise in neuronal transduction mechanisms and thereby suggest a novel apprach to the analysis of neuronal information processing.