Biology subjects

Fajen, B.

Publications and source records attributed to Fajen, B..

3 recordsLinked to original sources

bioRxiv2026

Learned landmark associations support online visual control under degraded visibility

Humans and other animals often act in environments that are at least partly familiar, where aspects of the spatial layout are known. Although such knowledge is known to support navigation and spatial cognition, its role in the online control of action remains unclear. We investigated whether drivers use knowledge of road layout to guide steering in high and low visibility and, if so, the form of such knowledge. In two simulated driving experiments (total N = 90), participants repeatedly drove winding roads containing segments with and without fog. Drivers who repeatedly experienced the same road exhibited more stable steering and lane positioning than drivers encountering novel roads, but only when visibility was reduced. These advantages were accompanied by superior performance on post-tests assessing knowledge of road geometry. We next examined the form of such knowledge by dissociating global knowledge of road layout from local associations between landmarks and road segments. Disrupting landmark-road segment associations produced the largest impairment in steering performance. The benefits of prior experience were largely preserved when road-segment order was scrambled but landmark associations remained intact. These findings show that spatial knowledge can support moment-to-moment steering control when visibility is reduced. Rather than relying on a globally coherent representation of the environment, drivers use local associations between landmarks and upcoming road geometry to anticipate future demands. More broadly, the results elucidate how familiarity with environmental structure contributes to the control of action when visual information is degraded, revealing a close interplay between spatial knowledge and visual control. Significance StatementPeople routinely act within surroundings they have encountered before, from commuting on the same streets to walking familiar hallways. Whether the spatial knowledge acquired from such experience actually shapes online visual control remains an open question. Using a simulated driving task, we show that familiarity with a road improves steering stability specifically when visibility is reduced, and that this benefit depends on learned associations between landmarks and upcoming road geometry rather than a global cognitive map. The results indicate that spatial knowledge plays a key role in moment-to-moment control, letting drivers anticipate road segments they cannot yet see. Unfamiliar roads and impaired spatial learning may compound the risks of poor visibility, suggesting a role for driver-assistance systems that leverage landmarks.

animal behavior and cognition↗

bioRxiv2025

Deep learning-based image quantification of epithelial cell shapes and its application to polycystic kidney disease

Cell shape is a fundamental determinant of tissue architecture and organ function. In epithelial tissues, cytoskeletal organization and tight junctions regulate cell geometry, shaping functional tissue units. Disruption of these mechanisms may cause diseases such as autosomal dominant polycystic kidney disease (ADPKD), in which cyst formation is characterized by abnormal regulation of epithelial cell shape. The mechanisms of cystogenesis remain incompletely understood, highlighting the need for robust, high-throughput methods to quantify the morphology of epithelial cells. Here, we present a fully automated, deep learning-based image analysis pipeline to quantify epithelial cell shape and tight junction morphology from immunofluorescence images. Our approach employs a U-Net convolutional neural network for accurate segmentation of fluorescence labeled tight junctions. We introduce novel algorithms to quantify overall cell shape and tight junction morphology, as well as to estimate cytoskeletal traction at shared cell borders. Our analysis pipeline objectively identifies subtle morphogenetic changes associated with disease-related mutations, applied to a genetically modified Madin-Darby Canine Kidney cell model of ADPKD. The method enables high-throughput, standardized analysis, reduces observer bias, and facilitates comparison across experiments. We further demonstrate the pipelines generalizability by applying it to Drosophila egg chamber epithelia. Our results establish a robust and scalable framework for analyzing cell shape and mechanical interactions in epithelial tissues, with broad applications in phenotypic screening, disease modeling, and morphogenesis research. Author SummaryThe shape of epithelial cells is critical for organ function. In the kidney, properly shaped epithelial cells assemble to tubules ensuring efficient waste excretion as well as body electrolyte and water balance. Disruption of cell shape regulation can lead to diseases such as autosomal dominant polycystic kidney disease (ADPKD), characterized by cyst formation and displacement of normal kidney tissue. Traditionally, analysis of epithelial cell morphology has relied on manual, low-throughput methods, which are time-consuming and prone to error. To overcome these limitations, we developed a fully automated, artificial intelligence-based pipeline that rapidly and reliably quantifies cell shape and junctional organization from microscopic images. We validated our approach using a cellular model of ADPKD, demonstrating clear differences in cell shape and junctional structure between normal and mutant cells harboring mutations in PKD-related genes. Our method enables efficient, objective analysis of large datasets and provides a powerful tool for understanding the mechanisms underlying cell shape regulation in health and disease.

bioinformatics↗

bioRxiv2021

A Dynamic Efficient Sensory Encoding Approach to Adaptive Tuning in Neural Models of Visual Motion Processing

This paper introduces a self-tuning mechanism for capturing rapid adaptation to changing visual stimuli by a population of neurons. Building upon the principles of efficient sensory encoding, we show how neural tuning curve parameters can be continually updated to optimally encode a time-varying distribution of recently detected stimulus values. We implemented this mechanism in a neural model that produces human-like estimates of self-motion direction (i.e., heading) based on optic flow. The parameters of speed-sensitive units were dynamically tuned in accordance with efficient sensory encoding such that the network remained sensitive as the distribution of optic flow speeds varied. In two simulation experiments, we found that model performance with dynamic tuning yielded more accurate, shorter latency heading estimates compared to the model with static tuning. We conclude that dynamic efficient sensory encoding offers a plausible approach for capturing adaptation to varying visual environments in biological visual systems and neural models alike.

neuroscience↗