Biology subjects

Govindarajan, L. N.

Publications and source records attributed to Govindarajan, L. N..

4 recordsLinked to original sources

bioRxiv2025

Task-optimized models of sensory uncertainty reproduce human confidence judgments

Sensory input is often ambiguous, leading to uncertain interpretations of the external world. Estimates of perceptual uncertainty might be useful in guiding behavior, but it remains unclear whether humans explicitly represent uncertainty in naturalistic settings, and whether any such representations are normatively correct. Progress has been hindered by the absence of stimulus-computable models that estimate uncertainty. We developed a class of task-optimized models that generate probability distributions over perceptual estimates. To assess whether human uncertainty representations align with the models, we compared human confidence judgments, which might indirectly reflect uncertainty representations, to confidence judgments extracted from the models uncertainty. In both sound localization and pitch perception, human confidence varied systematically, being lower for stimuli that produced more variable estimates across trials. Human confidence tracked model confidence across conditions, suggesting that human uncertainty representations accurately reflect the actual uncertainty of perceptual estimation. The modeling framework is extensible to other perceptual domains.

neuroscience↗

bioRxiv2024

An active electronic bidirectional interface for high resolution interrogation of the spinal cord

ObjectiveEpidural electrical stimulation (EES) has shown promise as both a clinical therapy and research tool for studying nervous system function. However, available clinical EES paddles are limited to using a small number of contacts due to the burden of wires necessary to connect each contact to the therapeutic delivery device, limiting the treatment area or density of epidural electrode arrays. We aimed to eliminate this burden using advanced on-paddle electronics. ApproachWe developed a smart EES paddle with a 60-electrode programmable array, addressable using an active electronic multiplexer embedded within the electrode paddle body. The electronics are sealed in novel, ultra-low profile hermetic packaging. We conducted extensive reliability testing on the novel array, including a battery of ISO 10993-1 biocompatibility tests and determination of the hermetic package leak rate. We then evaluated the EES device in vivo, placed on the epidural surface of the ovine lumbosacral spinal cord for 15 months. Main resultsThe active paddle array performed nominally when implanted in sheep for over 15 months and no device-related malfunctions were observed. The onboard multiplexer enabled bespoke electrode arrangements across, and within, experimental sessions. We identified stereotyped responses to stimulation in lower extremity musculature, and examined local field potential responses to EES using high-density recording bipoles. Finally, spatial electrode encoding enabled machine learning models to accurately perform EES parameter inference for unseen stimulation electrodes, reducing the need for extensive training data in future deep models. SignificanceWe report the development and chronic large animal in vivo evaluation of a high-density EES paddle array containing active electronics. Our results provide a foundation for more advanced computation and processing to be integrated directly into devices implanted at the neural interface, opening new avenues for the study of nervous system function and new therapies to treat neural injury and dysfunction.

bioengineering↗

bioRxiv2020

Discriminating between sleep and exercise-induced fatigue using computer vision and behavioral genetics

Following prolonged swimming, Caenorhabditis elegans cycle between active swimming bouts and inactive quiescent bouts. Swimming is exercise for C. elegans and here we suggest that inactive bouts are a recovery state akin to fatigue. Previously, analysis of exercise-induced quiescent (EIQ) bouts relied on laborious manual observation, as existing automated analysis methods for C. elegans swimming either cannot analyze EIQ bouts or fail to accurately track animal posture during these bouts. It is known that cGMP-dependent kinase (PKG) activity plays a conserved role in sleep, rest, and arousal. Using C. elegans EGL-4 PKG, we first validate a novel learning-based computer vision approach to automatically analyze C. elegans locomotory behavior and distinguish between activity and inactivity during swimming for long periods of time. We find that C. elegans EGL-4 PKG function predicts EIQ first bout timing, fractional quiescence, bout number, and bout duration, suggesting that previously described pathways are engaged during EIQ bouts. However, EIQ bouts are likely not sleep as animals are feeding during the majority of EIQ bouts. We find that genetic perturbation of neurons required for other C. elegans sleep states also does not alter EIQ dynamics. Additionally, we find that EIQ onset is sensitive to age and DAF-16 FOXO function. In summary, we have validated a new behavioral analysis software that enabled a quantitative and detailed assessment of swimming behavior, including EIQ. We found novel EIQ defects in aged animals and animals with mutations in a gene involved in stress tolerance. We anticipate that further use of this software will facilitate the analysis of genes and pathways critical for fatigue and other C. elegans behaviors.

animal behavior and cognition↗

bioRxiv2018

Automated leg tracking reveals distinct conserved gait and tremor signatures in Drosophila models of Parkinson’s Disease and Spinocerebellar ataxia 3

Genetic models in Drosophila have made invaluable contributions to our understanding of the molecular mechanisms underlying neurodegeneration. In human patients, some neurodegenerative diseases lead to characteristic movement dysfunctions, such as abnormal gait and tremors. However, it is currently unknown whether similar movement defects occur in the respective fly models, which could be used to model and better understand the pathophysiology of movement disorders. To address this question, we developed a machine-learning image-analysis programme -- Feature Learning-based LImb segmentation and Tracking (FLLIT) -- that automatically tracks leg claw positions of freely moving flies recorded on high-speed video, generating a series of body and leg movement parameters. Of note, FLLIT requires no user input for learning. We used FLLIT to characterise fly models of Parkinsons Disease (PD) and Spinocerebellar ataxia 3 (SCA3). Between these models, walking gait and tremor characteristics differed markedly, and recapitulated signatures of the respective human diseases. Selective expression of mutant SCA3 in dopaminergic neurons led to phenotypes resembling that of PD flies, suggesting that the behavioural phenotype may depend on the circuits affected, rather than the specific nature of the mutation. Different mutations produced tremors in distinct leg pairs, indicating that different motor circuits are affected. Almost 190,000 video frames were tracked in this study, allowing, for the first time, high-throughput analysis of gait and tremor features in Drosophila mutants. As an efficient assay of mutant gait and tremor features in an important model system, FLLIT will enable the analysis of the neurogenetic mechanisms that underlie movement disorders.

neuroscience↗