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Structure and flexibility in cortical representations of odour space Data to replicate analyses in Pashkovski et al. 2020 Nature https://doi.org/10.1038/s41586-020-2451-1 ## Abstract The cortex organizes sensory information to enable discrimination and generalization. As systematic representations of chemical odour space have notyet been described in the olfactory cortex, it remains unclear how odour relationships are encoded to place chemically distinct but similar odours, such as lemon and orange, into perceptual categories, such as citrus. Here, by combining chemoinformatics and multiphoton imaging in the mouse, we show that both the piriform cortex and its sensory inputs from the olfactory bulb represent chemical odour relationships through correlated patterns of activity. However, cortical odour codes differ from those in the bulb: cortex more strongly clusters together representations for related odours, selectively rewrites pairwise odour relationships, and better matches odour perception. The bulb-to-cortex transformation depends on the associative network originating within the piriform cortex, and can be reshaped by passive odour experience. Thus, cortex actively builds a structured representation of chemical odour space that highlights odour relationships; this representation is similar across individuals but remains plastic, suggesting a means through which the olfactory system can assign related odour cues to common and yet personalized percepts. ## Supplementary data * `pseudopopulations.h5` contains pseudopopulations of the mean responses for trials x ROIs x odors * `chemical_descriptors.h5` is a pandas dataframe containing chemical features for each odorant. # Code For an example notebook using these data, please see the associated GitHub repo: https://github.com/dattalab/cortical-representations-odour-space. &nbsp

Spontaneous mouse behavior is composed from repeatedly-used modules of movement (e.g., rearing, running, grooming) that are flexibly placed into sequences whose content evolves over time. By identifying behavioral modules and the order in which they are expressed, researchers can gain insight into the impact of drugs, genes, context, sensory stimuli and neural activity on behavior. Here we present a protocol for performing Motion Sequencing (MoSeq), an ethologically-inspired method that uses 3D machine vision and unsupervised machine learning to decompose spontaneous mouse behavior in the laboratory into a series of elemental modules called "syllables". This protocol is based upon a notebook-based pipeline for MoSeq that includes modules for depth video acquisition, data pre-processing and modeling, as well as a standardized set of visualization tools. Users are provided with instructions and code for building a MoSeq imaging rig and acquiring three-dimensional video of spontaneous mouse behavior for submission to the modeling framework; the outputs of this protocol include syllable labels for each frame of video data as well as summary plots describing how often each syllable was used and how syllables transitioned from one to the other over time. This protocol and the accompanying pipeline significantly lower the bar for adopting this unsupervised, data-driven approach to characterizing mouse behavior, enabling users without significant computational ethology experience to gain insight into how the structure of behavior is altered after experimental manipulations., Comment: 63 pages, 11 figures, 7 tables

Understanding how genes, drugs and neural circuits influence behavior requires the ability to effectively organize information about similarities and differences within complex behavioral datasets. Motion Sequencing (MoSeq) is an ethologically-inspired behavioral analysis method that identifies modular components of 3D mouse body language called “syllables.” Here we show that MoSeq effectively parses behavioral differences and captures similarities elicited by a panel of neuro- and psychoactive drugs administered to a cohort of nearly 700 mice. These data reveal that MoSeq can identify syllables that are characteristic of individual drugs; we leverage this finding to characterize the on- and off-target effects of both established and candidate therapeutics in a mouse model of autism spectrum disorder. These results demonstrate that MoSeq can meaningfully organize large-scale behavioral data, illustrate the power of a fundamentally modular description of behavior, and suggest that behavioral syllables represent a new class of druggable target.Understanding how genes, drugs and neural circuits influence behavior requires the ability to effectively organize information about similarities and differences within complex behavioral datasets. Motion Sequencing (MoSeq) is an ethologically-inspired behavioral analysis method that identifies modular components of 3D mouse body language called “syllables.” Here we show that MoSeq effectively parses behavioral differences and captures similarities elicited by a panel of neuro- and psychoactive drugs administered to a cohort of nearly 700 mice. These data reveal that MoSeq can identify syllables that are characteristic of individual drugs; we leverage this finding to characterize the on- and off-target effects of both established and candidate therapeutics in a mouse model of autism spectrum disorder. These results demonstrate that MoSeq can meaningfully organize large-scale behavioral data, illustrate the power of a fundamentally modular description of behavior, and suggest that behavioral syllables represent a new class of druggable target.

Evolution sculpts the olfactory nervous system in response to the unique sensory challenges facing each species. In vertebrates, dramatic and diverse adaptations to the chemical environment are possible because of the hierarchical structure of the olfactory receptor (OR) gene superfamily: rapid growth or pruning across the OR gene tree accompany major changes in habitat and lifestyle; independent selection on OR subfamilies can permit local adaptation or conserved chemical communication; and genetic variation in single OR genes among thousands can alter odor percepts and behaviors driven by precise chemical cues. However, this genetic flexibility contrasts with the relatively fixed neural architecture of the vertebrate olfactory system, whose slower rate of divergence mandates that new olfactory receptors integrate into segregated and functionally-distinct neural pathways. This organization allows evolution to couple critical chemical signals with selectively advantageous responses, but also constrains relationships between olfactory receptors and behavior. The coevolution of the OR repertoire and the structure of the olfactory system therefore reveals general principles of how the brain solves specific sensory problems and how it adapts to new ones.

Optogenetics promises spatiotemporal precise control of neural processes using light. However, the spatial extent of illumination within the brain is difficult to control and cannot be adjusted using standard fiber optics. We demonstrate that optical fibers with tapered tips can be used to illuminate either spatially restricted or large brain volumes. Remotely adjusting the light input angle to the fiber varies the light-emitting portion of the taper over several millimeters without movement of the implant. We use this mode to activate dorsal versus ventral striatum of individual mice and reveal different effects of each manipulation on motor behavior. Conversely, injecting light over the full numerical aperture of the fiber results in light emission from the entire taper surface, achieving broader and more efficient optogenetic activation of neurons when compared to the standard flat-faced fiber stimulation. Thus, tapered fibers permit focal or broad illumination that can be precisely and dynamically matched to experimental needs.