ABSTRACT
Modeling of neural systems can be conducted at several different levels of abstraction. At the most detailed level one typically models neurons in terms of their constituent ionic currents and conductances, while at a more general level neurons may be represented by their spike rates alone without any consideration of individual spikes. These two levels of description correspond to data reported in terms of individual spike trains and post stimulus time histogram data respectively. At the most general level, one may simply dispense with dynamics and simulate only the steady state responses of neurons. I shall elucidate these levels by briefly presenting three models of visual processing: nonlinear dynamics of human visual neurons, categorical processing in motion perception, and global information pooling in higher level form vision. The success of modeling at these very different levels of abstraction elucidates a number of general principles for modeling in neuroscience.

ABSTRACT
Computational ecology is an emerging multidisciplinary field, similar in concept to the cell and molecular emphasis of bioinformatics, which applies modern computational methodology to key problems at higher levels of biological organization. The goal of computational ecology is to combine realistic models of ecological systems with the often large data sets available to aid in analyzing these systems, utilizing techniques of modern computational science to manage the data, visualize model behavior, and statistically examine the complex dynamics which arises. Success in applying this to problems such as analyzing ecological impacts of hydrologic planning across the Everglades region requires diverse expertise in field biology, complex systems theory, computational science, remote sensing, and mathematical modeling, as well as the development of mechanisms to link approaches that are at the forefront of research in many of these fields. Ongoing are efforts to develop multimodels, a mixture of modeling approaches based upon the inherent temporal scales and spatial extent of various trophic components, linked together by spatially-explicit information on underlying environmental (e.g., water, soil structure, etc.), biotic (e.g.vegetation), and anthropogenic factors (e.g., land-use). For more details, see http://www.tiem.utk.edu/~gross/siam.withfigures.ps