Publications

2025

1. 

   A tale of two birds: cognitive simplicity drives collective route improvements in homing pigeons

   Shoubhik Chandan Banerjee, Fritz A Francisco, and Albert B Kao

   Sep 2025

   Abs DOI

   Cognitive abilities are central to how animals navigate complex environments. Beyond individual cognition, group living can also enhance navigation by pooling individually acquired information. One way this may be achieved is by following experienced leaders, which requires recognizing expertise within group members. Alternatively, accurate decisions could also emerge without expert opinions, through simpler mechanisms like the ‘wisdom of crowds’ principle that average out individual biases. Consequently, collective navigation strategies range from cognitively complex to simple, and yet, the prevalence or interplay of different collective strategies in nature remains unexplored. In this study, we asked: what is the navigation mechanism(s), requiring minimal cognitive demands, that is necessary and sufficient to quantitatively replicate the experimental results of a 2017 study on homing pigeons (Columba livia), which showed that sequential chains of bird pairs flying home — similar to a game of telephone — led to shorter homing routes compared to control birds flying individually or in fixed pairs. Our results show that the experimental data aligns closely with the simplest strategy — route averaging. Surprisingly, the complex mechanism of selectively propagating the best flight through social learning offered no additional advantage. We further observed that mixed strategies, although not supported by the experimental data, in theory combined advantages from both averaging and active selection of better routes, resulting in even greater performance. Hence, our results highlight the potential for future research to investigate selective pressures shaping the evolution of cultural learning and trade-offs among different decision mechanisms theoretically available to social animals in nature.

2024

1. 

   Timing decisions as the next frontier for collective intelligence

   Albert B. Kao, Shoubhik Chandan Banerjee, Fritz A. Francisco, and 1 more author

   Trends in Ecology & Evolution, Oct 2024

   DOI

2023

1. 

   Deep-worm-tracker: Deep learning methods for accurate detection and tracking for behavioral studies in C. elegans

   Shoubhik Chandan Banerjee, Khursheed Ahmad Khan, and Rati Sharma

   Applied Animal Behaviour Science, Sep 2023

   Abs DOI

   Accurate detection and tracking of model organisms such as C. elegans worms remains a fundamental task in behavioral studies. Traditional Machine Learning and Computer Vision methods produce poor detection results and suffer from repeated ID switches during tracking under occlusions and noisy backgrounds. Considering this, we propose Deep-Worm-Tracker, an end-to-end Deep Learning (DL) model, which is a combination of You Only Look Once (YOLOv5) object detection model and Strong Simple Online Real Time Tracking (Strong SORT) tracking backbone that is highly accurate and provides tracking results in real-time inference speeds. Present literature has few solutions to track animals under occlusions and even fewer publicly available large-scale animal re-ID datasets. Thus, we also provide a worm re-ID dataset to minimize worm ID switches, which, to the best of our knowledge, is the first of its kind for C. elegans. We are able to track worms at a mean Average Precision (mAP@0.5) \textgreater98% within just 9 min of training time with inference speeds of 9–15 ms for worm detection and on average 27 ms for worm tracking. Our tracking results show that Deep-Worm-Tracker is well suited for ethological studies involving C. elegans.

2022

1. 

   Persistent Correlation in Cellular Noise Determines Longevity of Viral Infections

   Abhilasha Batra, Shoubhik Chandan Banerjee, and Rati Sharma

   J. Phys. Chem. Lett., Aug 2022

   Abs DOI

   The slowly decaying viral dynamics, even after 2−3 weeks from diagnosis, is one of the characteristics of COVID-19 infection that is still unexplored in theoretical and experimental studies. This long-lived characteristic of viral infections in the framework of inherent variations or noise present at the cellular level is often overlooked. Therefore, in this work, we aim to understand the effect of these variations by proposing a stochastic non-Markovian model that not only captures the coupled dynamics between the immune cells and the virus but also enables the study of the effect of fluctuations. Numerical simulations of our model reveal that the long-range temporal correlations in fluctuations dictate the long-lived dynamics of a viral infection and, in turn, also affect the rates of immune response. Furthermore, predictions of our model system are in agreement with the experimental viral load data of COVID-19 patients from various countries.