Classification of stimuli based on stimulus-response curves and their variability.

Neuronal responses evoked in sensory neurons by static stimuli of various intensities are usually characterized by their input-output transfer function, i.e. by plotting the firing frequency (or any other measurable neuron response) versus the corresponding stimulus intensity. The aim of the present article is to determine the stimulus intensities which can be considered as "the most important" from two different points of view: transferring as much information as possible and coding the intensity as precisely as possible. These two problems are very different because, for example, an informative signal may be difficult to identify. We show that the role of noise is crucial in both problems. To obtain the range of stimuli which are the best identified, we propose to use measures based on Fisher information as known from the theory of statistical inference. To classify the most important stimuli from the point of view of information transfer, we suggest methods based on information theory. We show that both the most identifiable signal and the most informative signal are not unique. To study this, a generic model of input-output transfer function is analyzed under the influence of several different types of noise. Finally, the methods are illustrated on a model and data pertaining to olfactory sensory neurons.