DOI: 10.5061/dryad.crjdfn387
title: Behavioral discrimination and olfactory bulb encoding of odor plume intermittency

description: In order to survive, animals often need to navigate a complex odor
 landscape where odors can exist in airborne plumes. Several odor plume
 properties change with distance from the odor source, providing potential
 navigational cues to searching animals. Here, we focus on odor
 intermittency, a temporal odor plume property that measures the fraction
 of time odor is present at a given point within the plume and decreases
 with increasing distance from the odor source. We sought to determine if
 mice are capable of using changes in intermittency to locate an odor
 source. To do so, we trained mice on an intermittency discrimination task.
 We establish that mice can discriminate odor plume samples of low and high
 intermittency and that the neural responses in the olfactory bulb can
 account for task performance and support intermittency encoding.
 Modulation of sniffing, a behavioral parameter that is highly dynamic
 during odor-guided navigation, affects both behavioral outcomes on the
 intermittency discrimination task as well as neural representation of
 intermittency. Together, this work demonstrates that intermittency is an
 odor plume property that can inform olfactory search and more broadly
 supports the notion that mammalian odor-based navigation can be guided by
 temporal odor plume properties.
description: Mice were tested to see if they could discriminate different
 levels of odor intermittency (an odor plume property). Labview was used to
 run the tests. The RedShirtImaging Neuroplex and NeuroCCD system was used
 for concomitant optical imaging of the dorsal olfactory bulb in the
 head-fixed mice. Half the mice expressed G-CamP6f in olfactory receptor
 neurons, the other half in post-synaptic mitral/tufted cells. Matlab was
 used to analyze the behavioral and neural glomerular data. Another set of
 mice was similarly optically imaged under anesthesia to systematically
 assess neural coding across the dorsal olfactory bulbs of odor
 intermittency as a function of sniff rate.
description: # Behavioral discrimination and olfactory bulb encoding of odor plume
 intermittency ## Artificial\_Sniffing.zip - odor input files --- ###
 ZIPPED ARCHIVES: 1. Artificial_Sniffing/ These are imaging experiments
 done solely in anesthetized mice. Artificial_Sniffing/odor_input_files/
 These are the dynamic odor trace files, and sequences thereof, during the
 artificial sniffing experiments.
 Artificial_Sniffing/snifftraces_input_files/ These are the dynamic
 sniffing trace files, during the artificial sniffing experiments.
 Artificial_Sniffing/Data/ Recorded glomerular responses in MAT files.
 Artificial_Sniffing/Analysis code/ ArtificialSniffingAnalysisElife.m:
 MATLAB code to analyze the artificial sniffing data
 ArtificalSniffAverageResponses.mat and ArtificalSniffPulseResponses.mat:
 Tables with average responses and individual odor pulse responses 2.
 Behavior\ These are experiments done solely in awake mice.
 Behavior\Stimuli\ These are the dynamic odor traces of 3 types, each in a
 folder: natural, binary, and synthetic Behavior\Behavior Imaging Data\
 OMP-GCaMP6f and THY-GCaMP6f Folders: Recorded glomerular responses in MAT
 files for all animals per session Behavior\Behavior data\ Discrimination
 behavior to the 3 types of odor traces. Behavior\Analysis code\
 BehaviorAnalysisElife.m: Main MATLAB code to analyze Behavior data
 BehaviorPerformance.m and ReadBehaviorData.m: codes that are called by
 BehaviorAnalysisElife.m to determine behavior performance and read
 measured data during tasks (sniffing, licking, running)
 BehaviorImagingAnalysisElife.m: Main MATLAB code to analyze Behavior
 Imaging Data BehaviorImagingData.mat: Table with parameters extracted (can
 be read in to skip to line 795 in code) ## Description of the Data and
 file structure ### Artificial\_Sniff Artificial_Sniffing/ -Several
 reference files Artificial_Sniffing/odor_input_files/ Input odor traces:
 -202 values (0-255, 8 bit) for controlling the rapid olfactometer flow
 rate during a trial -B=binary (0/255) -I=intermittency (x10; 5=0.5)
 -F=frequency -WN=white noise -sweep=frequency sweep (start Hz_end Hz)
 Input odor sequences: -the number of lines each calling an input odor
 trace file and additional trial properties. -start with
 "input_ODORsequence"
 Artificial_Sniffing/snifftraces_input_files/ -Synthetic and biological
 ("rcr") sniff traces to drive the artificial sniffer solenoid
 syringe. -name describing the function
 Artificial_Sniffing/snifftraces_input_files/snifftraces_RC/ -recorded
 sniff from a mouse -1000 samples of pressure Artificial_Sniffing/Data/
 -Matlab mat files each containing organized fluorescence traces of
 selected glomeruli. -Data organized per
 README_ArtificialSniff_MAT_files.txt -files organized in folders:
 Data/Odor/Driver/Mouse Artificial_Sniffing/Analysis code/ Matlab m files
 are annotated for guidance. -ArtificialSniffingAnalysisElife.m: MATLAB
 code to analyze the artificial sniffing data
 -ArtificalSniffAverageResponses.mat and ArtificalSniffPulseResponses.mat:
 Tables with average responses and individual odor pulse responses ###
 Behavior Behavior\Stimuli\ -organized by stimulus type -Files described in
 README.txt Behavior\Behavior Imaging Data\ -Matlab mat files each
 containing organized fluorescence traces of selected glomeruli. -files
 organized in folders: \Driver\Mouse\Odor type & Day Behavior\Behavior
 data\ -files organized in folders: Odor type\Mouse\Session Day -Per
 session day: 64 Neuroplex DA files and associated 64 pupillary diameter
 txt files Behavior\Analysis code\ Matlab m files are annotated for
 guidance. BehaviorAnalysisElife.m: Main MATLAB code to analyze Behavior
 data in \Behavior Data\ BehaviorPerformance.m and ReadBehaviorData.m:
 codes that are called by BehaviorAnalysisElife.m to determine behavior
 performance and read measured data during tasks (sniffing, licking,
 running) BehaviorImagingAnalysisElife.m: Main MATLAB code to analyze
 \Behavior Imaging Data\ BehaviorImagingData.mat: Table with parameters
 extracted (can be read in to skip to line 795 in code) ## Sharing/Access
 Information NA