Overview

The project will focus on analyzing the newly available data from multiple cichlid species using the Cichlid Bower Tracking Repository.

This will involve processing the raw video footage through the existing pipeline to extract annotated behavioral data. Concurrently, a multi-species  animal tracking dataset will be curated by combining data from the various species.

Building upon the initial analysis, the project will explore data distillation techniques to improve the efficiency and scalability of the tracking process.

This may involve techniques such as data subsampling, compression, or  feature extraction to reduce the computational overhead without significantly compromising accuracy.

Novel challenges such as occlusion, where animals partially or fully obscure each other, will be tackled through the development of specialized algorithms and model architectures.

Previous works

Publications

In the wild, behaviors are often expressed over long time periods in complex and dynamic environments, and many behaviors include direct interaction with the environment itself. However, measuring behavior in naturalistic settings is difficult, and this has limited progress in understanding the mechanisms underlying many naturally evolved behaviors that are critical for survival and reproduction. Here we describe an automated system for measuring long-term bower construction behaviors in Lake Malawi cichlid fishes, in which males use their mouths to sculpt sand into large species-specific structures for courtship and mating. We integrate two orthogonal methods, depth sensing and action recognition, to simultaneously track the developing bower structure and the thousands of individual sand manipulation behaviors performed throughout construction. By registering these two data streams, we show that behaviors can be topographically mapped onto a dynamic 3D sand surface through time. The system runs reliably in multiple species, across many aquariums simultaneously, and for up to weeks at a time. Using this system, we show strong differences in construction behavior and bower form that reflect species differences in nature, and we gain new insights into spatial, temporal, social dimensions of bower construction, feeding, and quivering behaviors. Taken together, our work highlights how low-cost tools can automatically quantify behavior in naturalistic and social environments over long timescales in the lab.

Many behaviors that are critical for survival and reproduction are expressed over extended time periods. The ability to inexpensively record and store large volumes of video data creates new opportunities to understand the biological basis of these behaviors and simultaneously creates a need for tools that can automatically quantify behaviors from large video datasets. Here, we demonstrate that 3D Residual Networks can be used to classify an array of complex behaviors in Lake Malawi cichlid fishes. We first apply pixel-based hidden Markov modeling combined with density-based spatiotemporal clustering to identify sand disturbance events. After this, a 3D ResNet, trained on 11,000 manually annotated video clips, accurately (>76%) classifies the sand disturbance events into 10 fish behavior categories, distinguishing between spitting, scooping, fin swipes, and spawning. Furthermore, animal intent can be determined from these clips, as spits and scoops performed during bower construction are classified independently from those during feeding.

Talks

In the wild, behaviors are often expressed over long time periods in complex and dynamic environments, and many behaviors include direct interaction with the environment itself. However, measuring behavior in naturalistic settings is difficult, and this has limited progress in understanding the mechanisms underlying many naturally evolved behaviors that are critical for survival and reproduction. Here we describe an automated system for measuring long-term bower construction behaviors in Lake Malawi cichlid fishes, in which males use their mouths to sculpt sand into large species-specific structures for courtship and mating. We integrate two orthogonal methods, depth sensing and action recognition, to simultaneously track the developing bower structure and the thousands of individual sand manipulation behaviors performed throughout construction. As an example of the utility of this system, we will demonstrate how it can be used with single nuclei RNAseq to identify cellular populations activated during bower building behavior.