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 changes in these patterns have included analyses of inter-pulse intervals, sonar sound groups, and changes in individual signal parameters such as duration. Here, a 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. In these laboratory experiments, bats flew down a prescribed flight corridor through an obstacle array. These corridors varied in width (100 cm, 70 cm, or 40 cm) and shape/ path (straight or curved). SSIMS was able to discriminate between echolocation pulse trains recorded from flights in each of the corridor widths. SSIMS was also able to tell the difference between pulse trains recorded during flights where corridor shape through the obstacle array matched the previous trials (expected) as opposed to those recorded from flights with randomized corridor shape (unexpected). The results suggest that expectation and experience or memory influence the temporal pattern with which bats emit their echolocation calls.