High-precision spatial localization of mouse vocalizations during social interaction

Abstract

Mice display a wide repertoire of vocalizations that varies with age, sex, and context. Especially during courtship, mice emit ultrasonic vocalizations (USVs) of high complexity, whose detailed structure is poorly understood. As animals of both sexes vocalize, the study of social vocalizations requires attributing single USVs to individuals. The state-of-the-art in sound localization for USVs allows spatial localization at centimeter resolution, however, animals interact at closer ranges, involving tactile, snout-snout exploration. Hence, improved algorithms are required to reliably assign USVs. We develop multiple solutions to USV localization, and derive an analytical solution for arbitrary vertical microphone positions. The algorithms are compared on wideband acoustic noise and single mouse vocalizations, and applied to social interactions with optically tracked mouse positions. A novel, (frequency) envelope weighted generalised cross-correlation outperforms classical cross-correlation techniques. It achieves a median error of ~1.4 mm for noise and ~4–8.5 mm for vocalizations. Using this algorithms in combination with a level criterion, we can improve the assignment for interacting mice. We report significant differences in mean USV properties between CBA mice of different sexes during social interaction. Hence, the improved USV attribution to individuals lays the basis for a deeper understanding of social vocalizations, in particular sequences of USVs.

Prominent Inhibitory Projections Guide Sensorimotor Computation: An Invertebrate Perspective

“…Eukaryotes with cilia and ciliated sponge larvae, for example, employ ciliary motion to navigate their environment in response to a change in illumination, chemical gradients, and gravity. [7][8][9] Although sensorimotor computation commonly leads to navigation (as well as other forms of motor control including vocalization and vocal communication [10][11][12][13][14] ), we refrain from discussing the circuits of navigation, which are reviewed elsewhere. [4] Independent of whether the mobility is provided by a ciliary organ or a muscle-based effector system, [5,6] coordinating navigation in a context where motor action is generated as a response to current (or recent history of the) sensory information constitutes the basis of sensorimotor integration.…”

PASER for automated analysis of neural signals recorded in pulsating magnetic fields

“…The massive size of the subsequent raw voltage traces will make it infeasible to store them for offline processing. Moreover, real-time spike sorting allows for experimental conditions that adapt according to the neural responses that are observed which makes room for wider scientific investigation including but not limited to the neural basis of adaptive sensorimotor computation [78,79], contextual information processing [80][81][82][83] and storage [84][85][86][87], navigation [88][89][90], and information transfer in neural circuits [46,48]. Brain-machine-interfaces (BMI), like limb prosthetics, also necessitate that spike sorting is performed in real-time on a time-scale of hundreds of milliseconds [65], as these are usually controlled by direct neuronal signalling that is measured invasively by an array of electrodes [91].…”

Development of adaptive motor control for tactile navigation

“…With a stationary tactile target that has a uniform height, surface structure, and stiffness, and explored in darkness, egocentric and allocentric sensory information available to the animal across different whisker is mostly redundant; therefore strategies described in more complex (navigational) tasks (e.g. (Shimshek et al, 2006;Celikel et al, 2007;Corsini et al, 2009;Freudenberg et al, 2013Freudenberg et al, , 2016Seib et al, 2013;Pezzulo et al, 2014;Spiers and Gilbert, 2015;Heckman et al, 2016Heckman et al, , 2017Behrens et al, 2018;Górska et al, 2018) are likely to be inconsequential. Considering that animals can perform the task equally well even with a single intact whisker (Celikel and Sakmann, 2007) and that the emergence of adaptive-whisking results in recruitment of smaller number of whiskers during tactile object localization (Fig.3C), it is plausible that adaptive whisking could be a strategy to reduce the redundancy in sensory information even before a sensory neuron encodes the information available in the periphery.…”