# behav: predicting long-term collective behavior with deep learning (among fish species Hemigrammus rhodostomus)

<< Deciphering the social interactions that govern collective behavior in animal societies has greatly benefited from advancements in modern computing. Computational models diverge into two kinds of approaches: analytical models and machine learning models. This work introduces a deep learning model for social interactions in the fish species Hemigrammus rhodostomus, and compares its results to experiments and to the results of a state-of-the-art analytical model. To that end, (AA) propose a systematic methodology to assess the faithfulness of a model, based on the introduction of a set of stringent observables. (They) demonstrate that machine learning models of social interactions can directly compete against their analytical counterparts. Moreover, this work demonstrates the need for consistent validation across different timescales and highlights which design aspects critically enables (AA) deep learning approach to capture both short- and long-term dynamics. (AA) also show that this approach is scalable to other fish species. >>️

Vaios Papaspyros, Ramon Escobedo, et al. Predicting long-term collective animal behavior with deep learning. bioRxiv. doi: 10.1101/ 2023.02.15.528318. Feb 15, 2023.

https://www.biorxiv.org/content/10.1101/2023.02.15.528318v1

Also

keywords 'behav' in FonT

https://flashontrack.blogspot.com/search?q=behav

keyword 'ai' in FonT

https://flashontrack.blogspot.com/search?q=ai

keyword 'ia' | 'ai' in Notes 

(quasi-stochastic poetry)

https://inkpi.blogspot.com/search?q=ia

https://inkpi.blogspot.com/search?q=ai

Keywords: ai, gst, behav, behavior, cognition, machine learning, deep learning, social interactions

# gst: continuous, (not intermittent, perpetual) tremors and slips ...

<< Applying deep learning to seismic data has revealed tremor and slip occur at all times—before and after known large-scale slow-slip earthquakes—rather than intermittently in discrete bursts, as previously believed. Even more surprisingly, the machine learning generalizes to other tectonic environments, including the San Andreas Fault. >>

Machine learning reveals earth tremor and slip occur continuously, not intermittently. Los Alamos National Laboratory.  Feb 27, 2020.

https://m.phys.org/news/2020-02-machine-reveals-earth-tremor-intermittently.html

<< Slow earthquakes cyclically load fault zones and have been observed preceding major earthquakes on continental faults as well as subduction zones. Slow earthquakes and associated tremor are common to most subduction zones, taking place downdip from the neighboring locked zone where megathrust earthquakes occur. In the clearest cases, tremor is observed in discrete bursts that are identified from multiple seismic stations. By training a convolutional neural network to recognize known tremor on a single station in Cascadia, we detect weak tremor preceding and following known larger slow earthquakes, the detection rate of these weak tremors approximates the slow slip rate at all times, and the same model is able to recognize tremor from different tectonic environments with no further training. >>

Bertrand Rouet-Leduc, Claudia Hulbert, et al. Probing Slow Earthquakes With Deep Learning. Geophysical Research Letters. Volume 47, Issue 4. doi: 10.1029/2019GL085870. Jan 23, 2020.

https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019GL085870