Sniff-synchronized, gradient-guided olfactory search by freely moving mice

Abstract

For many organisms, searching for relevant targets such as food or mates entails active, strategic sampling of the environment. Finding odorous targets may be the most ancient search problem that motile organisms evolved to solve. While chemosensory navigation has been well characterized in micro-organisms and invertebrates, spatial olfaction in vertebrates is poorly understood. We have established an olfactory search assay in which freely-moving mice navigate noisy concentration gradients of airborne odor. Mice solve this task using concentration gradient cues and do not require stereo olfaction for performance. During task performance, respiration and nose movement are synchronized with tens of milliseconds precision. This synchrony is present during trials and largely absent during inter-trial intervals, suggesting that sniff-synchronized nose movement is a strategic behavioral state rather than simply a constant accompaniment to fast breathing. To investigate the spatiotemporal structure of these active sensing movements, we used machine learning methods to parse motion trajectories into elementary movement motifs. Motifs fall into two clusters, which correspond to investigation and approach states. Investigation motifs lock precisely to sniffing, such that the individual motifs preferentially occur at specific phases of the sniff cycle. This work clarifies sensorimotor strategies for mouse olfactory search and guides ongoing work into the underlying neural mechanisms. Bhattacharyya U, Singh Bhalla U. Robust and rapid air borne odor tracking without casting. eNeuro. 2015 11; 2(6):Eneuro.0102-15. doi: 10.1523/ENEURO.0102-15.2015. Bi S, Sourjik V. Stimulus sensing and signal processing in bacterial chemotaxis.

Using Head-Mounted Ethanol Sensors to Monitor Olfactory Information and Determine Behavioral Changes Associated with Ethanol-Plume Contact during Mouse Odor-Guided Navigation

“…A growing body of literature has focused on understanding the behavioral strategies for odor-source localization in freely moving mammals (Findley et al, 2020;Gire et al, 2016;Gumaste et al, 2020;Liu et al, 2020). However, correlating real-time olfactory information with behavior and physiological recordings from freely moving animals has been a challenging avenue due to the dynamic nature of the olfactory stimulus.…”

“…The small footprint of these sensors can be leveraged to allow us to record the olfactory information arriving at the two nares simultaneously. This can allow us to assess whether binaral comparison (Findley et al, 2020;Jones and Urban, 2018;Khan et al, 2012;Rajan et al, 2006) informs behavior during plume tracking in freely behaving animals. Additionally, combining these sensors with other sensors that are specific to other chemicals (M. L. Johnston et al, 2012;McGinn et al, 2020) can allow us to study the 'foreground' vs. 'background' problem in the olfactory realm (Riffell et al, 2014).…”

Real-time, low-latency closed-loop feedback using markerless posture tracking

“…If neuroscience’s embrace of studying the brain in its natural context of complex, contingent, and open-ended behavior ( Krakauer et al, 2017 ; Mathis and Mathis, 2020 ; Datta et al, 2019 ) is smashing the champagne on a long-delayed voyage, the technical complexity of the experiments is the grim spectre of the sea. Markerless tracking has already enabled a qualitatively new class of data-dense behavioral experiments, but the heroism required to simultaneously record natural behavior from 62 cameras ( Bala et al, 2020 ) or electrophysiology from 65,000 electrodes ( Sahasrabuddhe et al, 2020 ), or integrate dozens of heterogeneous custom-built components ( Findley et al, 2020 ) hints that a central challenge facing neuroscience is scale .…”

Real-time, low-latency closed-loop feedback using markerless posture tracking

“…If neuroscience’s embrace of studying the brain in its natural context of complex, contingent, and open-ended behavior ( Krakauer et al, 2017 ; Mathis and Mathis, 2020 ; Datta et al, 2019 ) is smashing the champagne on a long-delayed voyage, the technical complexity of the experiments is the grim spectre of the sea. Markerless tracking has already enabled a qualitatively new class of datadense behavioral experiments, but the heroism required to simultaneously record natural behavior from 62 cameras ( Bala et al, 2020 ) or electrophysiology from 65,000 electrodes ( Sahasrabuddhe et al, 2020 ), or integrate dozens of heterogeneous custom-built components ( Findley et al, 2020 ) hints that a central challenge facing neuroscience is scale .…”