title: Entropy as an index of the informational state of neurons.
abstract: Techniques were developed for using the classical information theory descriptor, entropy, to quantify the "uncertainty" present in neuronal spike trains. Entropy was calculated on the basis of a method that describes the relative relationships of serially ordered interspike intervals by encoding the intervals as a series of symbols, each of which depicts the relative duration of two adjacent spike intervals. Each symbol, or set of symbols has a specific fractional entropy value, derived from its probability of occurrence; moreover, fractional entropy can describe the relative amount of "information" that is associated with the relative location of a given symbol in a string of symbols. Using spike trains from 12 single neurons in the cerebellar cortex of rats, we determined: (1) the mean and S.D. of information content of each symbol in each specific position in a group of symbols (2-4 symbols/group, based on 3-5 adjacent intervals), (2) the 4-symbol groups which had the least and the most average fractional entropy, (3) that the 4-symbol groups with both low and high fractional entropy had significant positive correlations with the probability of occurrence of those groups after a drug treatment (ethanol), and (4) that the degree of drug-induced change in the incidence of both low- and high-fractional entropy groups did not correlate with predrug entropy. Thus, the entropy of clusters of 3-5 adjacent spike intervals, when computed in this particular way, seems to be a useful measure or index of the informational state of neurons.
mesh_terms: D000200:Action Potentials; D000818:Animals; D008433:Mathematics; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D055585:Physical Phenomena; D010825:Physics; D051381:Rats; D011919:Rats, Inbred Strains