ABSTRACT

Computational neuroscience uses mathematical models to study how neural systems represent and transmit information. Although modeling in computational neuro-science spans a range of mathematical approaches, the discipline may be divided approximately into two schools. The first school uses detailed biophysical (Hodgkin and Huxley and their variants) models of individual neurons, networks of neurons or artificial neural network models to study emergent behaviors of neural systems. 1-58488-362-6/04/$0.00+$ 1.50

The second school, and the one we discuss here, develops signal processing algorithms and statistical methods to analyze the ever-growing volumes of data collected in neuroscience experiments. The growing complexity of neuroscience experiments makes use of appropriate data analysis methods crucial for establishing how reliably specific system properties can be identified from experimental measurements. In particular, careful data analysis is an essential complement to neural network modeling; it allows validation of neural network model predictions in addition to feeding back biologically relevant constraints and parameter values for further analytic and simulation studies. Neuroscience experiments and neural spike train data have special features that present new, exciting challenges for statistical research.