Neural spike train similarity algorithm detects differences in temporal patterning of bat echolocation call sequences

Abstract

Bats emit echolocation sounds in complex temporal sequences that change to accommodate dynamic surroundings. Efforts to quantify how these patterns change have included analysis of inter-pulse intervals, sonar sound groups, and changes in individual signal parameters. No standardized method has been adopted for quantifying whether sequences of echolocation calls are similar or different beyond these individual dimensions. Here, a new method is presented for assessing the similarity in temporal structure between trains of bat echolocation sounds. The spike-train similarity space (SSIMS) algorithm, originally designed for neural data analysis, was applied to determine which features of the environment influence temporal patterning of echolocation sounds emitted by flying big brown bats (Eptesicus fuscus). Using a relational point-process framework, SSIMS was able to discriminate between pulse sequences recorded in different flight environments, as well as to separate flights depending on the bat’s expectation of its surroundings based on previous experience.