Biology subjects

Sridhar, V. H.

Publications and source records attributed to Sridhar, V. H..

5 recordsLinked to original sources

bioRxiv2026

TracktorLive: an integrated real-time object tracking and response system

Real-time tracking and automated response systems are essential for standardising experiments, reducing observer bias, and improving reproducibility in studies of movement and behaviour. However, existing solutions face significant challenges: AI-based tracking systems require expensive hardware and impose computational delays, creating challenges for closed-loop experiments; existing real-time tracking tools lack standardised implementations for response delivery; and steep learning curves limit accessibility for users without programming or computer vision expertise. Here, we introduce TracktorLive, an open-source Python package designed to overcome these challenges through concurrency and a modular, cassette-based architecture. TracktorLive leverages traditional computer vision techniques to perform image-based object detection without the need for expensive hardware or deep learning. By parallelizing object tracking and response delivery into separate, concurrent server and client processes, the software minimizes frame processing time, enabling rapid, real-time analysis and response delivery. User-friendly cassettes--portable code snippets that can be copy-pasted into scripts--enable users with minimal programming experience to implement complex workflows for use in experiments and practical applications. We demonstrate TracktorLives utility through several applications, including microcontroller-based stimulus delivery for location-dependent manipulations; conditional video recording that activates only during events of interest; kinematic-based response triggering using real-time velocity computations; and multi-cassette experimental designs combining multiple functionalities. Detailed tutorials are provided to familiarize users with TracktorLives operation and functionality, and a growing library of cassettes supports diverse applications for processing both real-time and pre-recorded video. We validated the software by comparing its response timing to human experimenters in a stimulus delivery task involving two fish species, where TracktorLive demonstrated consistently higher accuracy and lower variability, particularly for fast-moving subjects. Beyond experimental biology, TracktorLives unique architecture and versatility could support many different applications in fields ranging from neuroscience to wildlife management. As an open-source software combining accessibility, modularity, and computational efficiency, TracktorLive can help democratize real-time tracking and automated response systems across disciplines.

animal behavior and cognition↗

bioRxiv2021

The geometry of decision-making

Choosing among spatially-distributed options is a central challenge for animals, from deciding among alternative potential food sources or refuges, to choosing with whom to associate. Using an integrated theoretical and experimental approach (employing immersive virtual reality), we consider the interplay between movement and vectorial integration during decision-making regarding two, or more, options in space. In computational models of this process we reveal the occurrence of spontaneous and abrupt "critical" transitions (associated with specific geometrical relationships) whereby organisms spontaneously switch from averaging vectorial information among, to suddenly excluding one, among the remaining options. This bifurcation process repeats until only one option--the one ultimately selected--remains. Thus we predict that the brain repeatedly breaks multi-choice decisions into a series of binary decisions in space-time. Experiments with fruit flies, desert locusts, and larval zebrafish reveal that they exhibit these same bifurcations, demonstrating that across taxa and ecological context, there exist fundamental geometric principles that are essential to explain how, and why, animals move the way they do.

animal behavior and cognition↗

bioRxiv2021

Collective detection based on visual information in animal groups

The spatio-temporal distribution of individuals within a group (i.e its internal structure) plays a defining role in how individuals interact with their environment, make decisions, and transmit information via social interactions. Group-living organisms across taxa, including many species of fish, birds, ungulates, and insects, use vision as the predominant modality to coordinate their collective behavior. Despite this importance, there have been few quantitative studies examining visual detection capabilities of individuals within groups. We investigate key principles underlying individual, and collective, visual detection of stimuli (which could include cryptic predators, potential food items, etc.) and how this relates to the internal structure of groups. While the individual and collective detection principles are generally applicable, we employ a model experimental system of schooling golden shiner fish (Notemigonus crysoleucas) to relate theory directly to empirical data, using computational reconstruction of the visual fields of all individuals to do so. Our integrative approach allows us to reveal how the external visual information available to each group member depends on the number of individuals in the group, the position within the group, and the location of the external visually-detectable stimulus. We find that in small groups, individuals have detection capability in nearly all directions, while in large groups, occlusion by neighbors causes detection capability to vary with position within the group. We then formulate a simple, and generally applicable, model that captures how visual detection properties emerge due to geometric scaling of the space occupied by the group and occlusion caused by neighbors. We employ these insights to discuss principles that extend beyond our specific system, such as how collective detection depends on individual body shape, and the size and structure of the group.

animal behavior and cognition↗

bioRxiv2018

Tracktor: image-based automated tracking of animal movement and behaviour

1. Automated movement tracking is essential for high-throughput quantitative analyses of the behaviour and kinematics of organisms. Automated tracking also improves replicability by avoiding observer biases and allowing reproducible workflows. However, few automated tracking programs exist that are open access, open source, and capable of tracking unmarked organisms in noisy environments.\n\n2. Tracktor is an image-based tracking freeware designed to perform single-object tracking in noisy environments, or multi-object tracking in uniform environments while maintaining individual identities. Tracktor is code-based but requires no coding skills other than the user being able to specify tracking parameters in a designated location, much like in a graphical user interface (GUI). The installation and use of the software is fully detailed in a user manual.\n\n3. Through four examples of common tracking problems, we show that Tracktor is able to track a variety of animals in diverse conditions. The main strengths of Tracktor lie in its ability to track single individuals under noisy conditions (e.g. when the object shape is distorted), its robustness to perturbations (e.g. changes in lighting conditions during the experiment), and its capacity to track multiple individuals while maintaining their identities. Additionally, summary statistics and plots allow measuring and visualizing common metrics used in the analysis of animal movement (e.g. cumulative distance, speed, acceleration, activity, time spent in specific areas, distance to neighbour, etc.).\n\n4. Tracktor is a versatile, reliable, easy-to-use automated tracking software that is compatible with all operating systems and provides many features not available in other existing freeware. Access Tracktor and the complete user manual here: https://github.com/vivekhsridhar/tracktor

animal behavior and cognition↗

bioRxiv2017

Consistent Individual Differences Drive Collective Behaviour And Group Functioning Of Schooling Fish

The ubiquity of consistent inter-individual differences in behaviour ( animal personalities) 1,2, suggests they may constitute a fundamental component of animal groups that may drive their functioning 3,4. Despite increasing evidence that highlights their importance 5-16, we still lack a unified mechanistic frame-work to explain and predict how inter-individual differences may affect collective behaviour. Here we investigate how differences in individual behavioural tendencies affect the group structure, movement dynamics, and foraging behaviour of animal groups using free-swimming stickleback fish (Gasterosteus aculeatus). Analysis of high-resolution tracking data of known individuals demonstrates that, across a range of contexts, consistent inter-individual differences in social proximity were strongly linked to speed and, together with boldness predicted spatial positioning and leadership within groups, differences in structure and movement dynamics between groups, as well as individual and group foraging performance. These effects of inter-individual behavioural variation on group-level states emerged naturally from a generic model of heterogeneous, self-organising groups. Our study combines experimental and theoretical evidence for a simple mechanism to explain variation in the emergence of structure, dynamics, and functional capabilities of groups across social and ecological scales. In addition, we show that individual performance was conditional to the group composition, providing a potential basis for social selection driving behavioural differentiation between individuals.

animal behavior and cognition↗