Nonrenewal statistics of electrosensory afferent spike trains: Implications for the detection of weak sensory signals

The ability of an animal to detect weak sensory signals is limited, in part , by statistical fluctuations in the spike activity of sensory afferent ner ve fibers. In weakly electric fish, probability coding (P-type) electrosens ory afferents encode amplitude modulations of the fish's self-generated ele ctric field and provide information necessary for electrolocation. This stu dy characterizes the statistical properties of baseline spike activity in P -type afferents of the brown ghost knifefish, Apteronotus leptorhynchus. Sh ortterm variability, as measured by the interspike interval (ISI) distribut ion, is moderately high with a mean ISI coefficient of variation of 44%. An alysis of spike train variability on longer time scales, however, reveals a remarkable degree of regularity. The regularizing effect is maximal for ti me scales on the order of a few hundred milliseconds, which matches functio nally relevant time scales for natural behaviors such as prey detection. Us ing high-order interval analysis, count analysis, and Markov-order analysis we demonstrate that the observed regularization is associated with memory effects in the ISI sequence which arise from an underlying nonrenewal proce ss. In most cases, a Markov process of at least fourth-order was required t o adequately describe the dependencies. Using an ideal observer paradigm, w e illustrate how regularization of the spike train can significantly improv e detection performance for weak signals. This study emphasizes the importa nce of characterizing spike train variability on multiple time scales, part icularly when considering limits on the detectability of weak sensory signa ls.