Modeling Neural Mechanisms of the Control of Respiration

摘要

We have developed computational models of biological neural mechanisms thatprovide the genesis and control of network oscillations, specific patterns of oscillation, and the control of different phases in the patterns. These models are built up across several levels of biological complexity theory, beginning with individual ionic channel kinetics and ending in whole system behavior, and are grounded in accurate biological detail at every level . A major contribution of these models in the area of complexity theory, since it is possible to observe in simulation by which the interactions in these biological nonlinear dynamic systems produce emergent properties which a re greater than the sum of their parts. The understanding of the interactions is leading to the ability to manipulate the behavior of these nonlinear dynamics systems. In some models each neuron class is represented by a population 25 neurons, and manipulation of these networks is leading to important insights in the area of biological parallel processing. All of these results are finding interest for applications within process technology and process control, as algorithms or as inspiration for novel approaches to nonlinear control problems.