Biology subjects

Susoy, V.

Publications and source records attributed to Susoy, V..

6 recordsLinked to original sources

bioRxiv2024

Efficient pheromone navigation via antagonistic detectors

Chemotaxis to a moving potential mate that emits a volatile sex pheromone poses a navigation challenge requiring rapid, precise responses to maximize reproductive success. Volatile chemicals form gradients that differ from soluble compounds, potentially making navigation based on comparisons between spatially separated sensors unreliable for small-bodied animals. Here we show that, rather than a simple spatial comparison, Caenorhabditis elegans males employ an antagonistic strategy, comparing inputs from sex-shared head (AWA) and male-specific tail (PHD) sensory neurons with distinct response properties. Despite sharing a receptor, SRD-1, these detectors play different roles: AWAs promote forward movement and acceleration, while PHDs induce reversals and deceleration. In rising pheromone gradients, AWA activity dominates; in falling gradients, AWA inactivates, allowing PHD to correct trajectories. AWAs are essential for mate-searching, while PHDs are crucial for complex tasks. A minimal computational model reproduces these behaviors and infers how head-tail signals are combined. Thus, a sexually dimorphic, antagonistic sensory system enables adaptive navigation in dynamic environments

neuroscience↗

bioRxiv2024

Identifying Interpretable Latent Factors with Sparse Component Analysis

In many neural populations, the computationally relevant signals are posited to be a set of latent factors - signals shared across many individual neurons. Understanding the relationship between neural activity and behavior requires the identification of factors that reflect distinct computational roles. Methods for identifying such factors typically require supervision, which can be suboptimal if one is unsure how (or whether) factors can be grouped into distinct, meaningful sets. Here, we introduce Sparse Component Analysis (SCA), an unsupervised method that identifies interpretable latent factors. SCA seeks factors that are sparse in time and occupy orthogonal dimensions. With these simple constraints, SCA facilitates surprisingly clear parcellations of neural activity across a range of behaviors. We applied SCA to motor cortex activity from reaching and cycling monkeys, single-trial imaging data from C. elegans, and activity from a multitask artificial network. SCA consistently identified sets of factors that were useful in describing network computations.

neuroscience↗

bioRxiv2023

Evolutionarily-conserved behavioral plasticity enables context-dependent performance of mating behavior in C. elegans

Behavioral plasticity helps humans and animals to achieve their goals by adapting their behaviors to different environments. Although behavioral plasticity is ubiquitous, many innate species-specific behaviors, such as mating, are often assumed to be stereotyped and unaffected by plasticity or learning, especially in invertebrates. Here, we describe a novel case of behavioral plasticity in the nematode C. elegans - under a different set of naturalistic conditions the male uses a unique, previously undescribed set of behavioral steps for mating. Under standard lab conditions (agar plates with bacterial food), the male performs parallel mating, a largely two-dimensional behavioral strategy where his body and tail remain flat on the surface and slide alongside the partner s body from initial contact to copulation. But when placed in liquid medium, the male performs spiral mating, a distinctly three-dimensional behavioral strategy where he winds around the partner s body in a helical embrace. The performance of spiral mating does not require a long-term change in growing conditions but it does improve with experience. This experience-dependent improvement involves a critical period - a time window around the L4 to early adult stage, which coincides with the development of most male-specific neurons. We tested several wild isolates of C. elegans and other Caenorhabditis species and found that most were capable of parallel mating on surfaces and spiral mating in liquids. We suggest that two- and three-dimensional mating strategies in Caenorhabditis are plastic, conditionally expressed phenotypes conserved across the genus, and which can be genetically "fixed " in some species.

animal behavior and cognition↗

bioRxiv2022

PEZO-1 and TRP-4 mechanosensors are involved in mating behavior in Caenorhabditis elegans

Male mating in Caenorhabditis elegans is a complex behavior with a strong mechanosensory component. C. elegans has several characterized mechanotransducer proteins, but few have been shown to contribute to mating. Here, we investigated the roles of PEZO-1, a piezo channel, and TRP-4, a mechanotransducing TRPN channel, in male mating behavior. We show that pezo-1 is expressed in several male-specific neurons with known roles in mating. We show that, among other neurons, trp-4 is expressed in the PCA sensory neuron, which monitors relative sliding between the male and the hermaphrodite and inhibits neurons involved in vulva detection. Mutations in both genes compromise many steps of mating, including initial response to the hermaphrodite, scanning, turning, and vulva detection. We performed pan-neuronal imaging during mating between freely-moving mutant males and hermaphrodites. Both pezo-1 and trp-4 mutants showed spurious activation of the sensory neurons involved in vulva detection. In trp-4 mutants, this spurious activation might be caused by PCA failure to inhibit vulva-detecting neurons during scanning. Indeed, we show that without functional TRP-4, PCA fails to detect the relative sliding between the male and hermaphrodite. Cell-specific TRP-4 expression restores PCAs mechanosensory function. Our results demonstrate new roles for both PEZO-1 and TRP-4 mechanotransducers in C. elegans mating behavior.

neuroscience↗

bioRxiv2022

Automated neuron tracking inside moving and deforming animals using deep learning and targeted augmentation

Advances in functional brain imaging now allow sustained rapid 3D visualization of large numbers of neurons inside behaving animals. To decode circuit activity, imaged neurons must be individually segmented and tracked. This is particularly challenging when the brain itself moves and deforms inside a flexible body. The field has lacked general methods for solving this problem effectively. To address this need, we developed a method based on a convolutional neural network (CNN) Awith specific enhancements which we apply to freely moving Caenorhabditis elegans. For a traditional CNN to track neurons across images of a brain with different postures, the CNN must be trained with ground truth (GT) annotations of similar postures. When these postures are diverse, an adequate number of GT annotations can be prohibitively large to generate manually. We introduce targeted augmentation, a method to automatically synthesize reliable annotations from a few manual annotations. Our method effectively learns the internal deformations of the brain. The learned deformations are used to synthesize annotations for new postures by deforming the manual annotations of similar postures in GT images. The technique is germane to 3D images, which are generally more difficult to analyze than 2D images. The synthetic annotations, which are added to diversify training datasets, drastically reduce manual annotation and proofreading. Our method is effective both when neurons are represented as individual points or as 3D volumes. We provide a GUI that incorporates targeted augmentation in an end-to-end pipeline, from manual GT annotation of a few images to final proofreading of all images. We apply the method to simultaneously measure activity in the second-layer interneurons in C. elegans: RIA, RIB, and RIM, including the RIA neurite. We find that these neurons show rich behaviors, including switching entrainment on and off dynamically when the animal is exposed to periodic odor pulses.

neuroscience↗

bioRxiv2020

Natural sensory context drives diverse brain-wide activity during C. elegans mating

Natural goal-directed behaviors often involve complex sequences of many stimulus-triggered components. Understanding how brain circuits organize such behaviors requires mapping the interactions between an animal, its environment, and its nervous system. Here, we use continuous brain-wide neuronal imaging to study the full performance of mating by the C. elegans male. We show that as each mating unfolds in its own sequence of component behaviors, the brain operates similarly between instances of each component, but distinctly between different components. When the full sensory and behavioral context is taken into account, unique roles emerge for each neuron. Functional correlations between neurons are not fixed, but change with behavioral dynamics. From the contribution of individual neurons to circuits, our study shows how diverse brain-wide dynamics emerge from the integration of sensory perception and motor actions within their natural context.

neuroscience↗