Neural modeling and model identification.

Abstract

The complexity of the nervous system poses challenging experimental and theoretical problems to the investigator. While experimental studies have provided a broad understanding of the physiological and anatomical role of neural elements, a comprehensive description of how the nervous system acquires and processes information is still lacking. Neural modeling addresses itself to the quantitative interpretation of neurophysiological experiments, to the assimilation of diverse experimental results into unified theories, and to the investigation of mechanisms of information-processing units, codes, and networks and their relation to capabilities of living systems. This review will emphasize neural models which can be tested by physiological or psychophysical experiments and will include the identification of model parameters based on experimental measurements. An overview of neural firing models, neural interaction models, neural variability and coding, design of input-output experiments for model identification, and description of small network interactions through multiunit and gross potential recording will be included.